This essay analyzes a crucial difference in the ways in which erotic feelings are articulated in the sentimental novel from eighteenth-century England and Feng Menglong＇s stories of qing from late Ming （1573-1644）....This essay analyzes a crucial difference in the ways in which erotic feelings are articulated in the sentimental novel from eighteenth-century England and Feng Menglong＇s stories of qing from late Ming （1573-1644）. It compares Feng＇s stories and Samuel Richardson＇s novels with a focus on how they chart the courses of love affairs. The essay argues that English sentimental novels accentuate psychological depth while their Chinese counterparts preclude depth with ritualized expressions of feelings. The contrast goes a long way to explaining the bifurcation of English and Chinese fiction in modern eras; one gives rise to several nuanced forms of psychological realism, modulating narrative perspectives as a way of mimicking the complex workings of layered consciousness. The Chinese stories of qing, on the other hand, suggest a different theory of love, one that downplays subjective control of feelings in favor of the effects of social or accidental circumstances. They evolve into a fictional tradition that aestheticizes and stylizes qing, reducing it to a surface of fixed patterns by virtue of inserting verse pieces into prose narratives.展开更多
Wireless technologies and the Internet of Things(IoT)are being extensively utilized for advanced development in traditional communication systems.This evolution lowers the cost of the extensive use of sensors,changing...Wireless technologies and the Internet of Things(IoT)are being extensively utilized for advanced development in traditional communication systems.This evolution lowers the cost of the extensive use of sensors,changing the way devices interact and communicate in dynamic and uncertain situations.Such a constantly evolving environment presents enormous challenges to preserving a secure and lightweight IoT system.Therefore,it leads to the design of effective and trusted routing to support sustainable smart cities.This research study proposed a Genetic Algorithm sentiment-enhanced secured optimization model,which combines big data analytics and analysis rules to evaluate user feedback.The sentiment analysis is utilized to assess the perception of network performance,allowing the classification of device behavior as positive,neutral,or negative.By integrating sentiment-driven insights,the IoT network adjusts the system configurations to enhance the performance using network behaviour in terms of latency,reliability,fault tolerance,and sentiment score.Accordingly to the analysis,the proposed model categorizes the behavior of devices as positive,neutral,or negative,facilitating real-time monitoring for crucial applications.Experimental results revealed a significant improvement in the proposed model for threat prevention and network efficiency,demonstrating its resilience for real-time IoT applications.展开更多
Joint Multimodal Aspect-based Sentiment Analysis(JMASA)is a significant task in the research of multimodal fine-grained sentiment analysis,which combines two subtasks:Multimodal Aspect Term Extraction(MATE)and Multimo...Joint Multimodal Aspect-based Sentiment Analysis(JMASA)is a significant task in the research of multimodal fine-grained sentiment analysis,which combines two subtasks:Multimodal Aspect Term Extraction(MATE)and Multimodal Aspect-oriented Sentiment Classification(MASC).Currently,most existing models for JMASA only perform text and image feature encoding from a basic level,but often neglect the in-depth analysis of unimodal intrinsic features,which may lead to the low accuracy of aspect term extraction and the poor ability of sentiment prediction due to the insufficient learning of intra-modal features.Given this problem,we propose a Text-Image Feature Fine-grained Learning(TIFFL)model for JMASA.First,we construct an enhanced adjacency matrix of word dependencies and adopt graph convolutional network to learn the syntactic structure features for text,which addresses the context interference problem of identifying different aspect terms.Then,the adjective-noun pairs extracted from image are introduced to enable the semantic representation of visual features more intuitive,which addresses the ambiguous semantic extraction problem during image feature learning.Thereby,the model performance of aspect term extraction and sentiment polarity prediction can be further optimized and enhanced.Experiments on two Twitter benchmark datasets demonstrate that TIFFL achieves competitive results for JMASA,MATE and MASC,thus validating the effectiveness of our proposed methods.展开更多
Multimodal Aspect-Based Sentiment Analysis(MABSA)aims to detect sentiment polarity toward specific aspects by leveraging both textual and visual inputs.However,existing models suffer from weak aspectimage alignment,mo...Multimodal Aspect-Based Sentiment Analysis(MABSA)aims to detect sentiment polarity toward specific aspects by leveraging both textual and visual inputs.However,existing models suffer from weak aspectimage alignment,modality imbalance dominated by textual signals,and limited reasoning for implicit or ambiguous sentiments requiring external knowledge.To address these issues,we propose a unified framework named Gated-Linear Aspect-Aware Multimodal Sentiment Network(GLAMSNet).First of all,an input encoding module is employed to construct modality-specific and aspect-aware representations.Subsequently,we introduce an image–aspect correlation matching module to provide hierarchical supervision for visual-textual alignment.Building upon these components,we further design a Gated-Linear Aspect-Aware Fusion(GLAF)module to enhance aspect-aware representation learning by adaptively filtering irrelevant textual information and refining semantic alignment under aspect guidance.Additionally,an External Language Model Knowledge-Guided mechanism is integrated to incorporate sentimentaware prior knowledge from GPT-4o,enabling robust semantic reasoning especially under noisy or ambiguous inputs.Experimental studies conducted based on Twitter-15 and Twitter-17 datasets demonstrate that the proposed model outperforms most state-of-the-art methods,achieving 79.36%accuracy and 74.72%F1-score,and 74.31%accuracy and 72.01%F1-score,respectively.展开更多
In the rapidly evolving landscape of natural language processing(NLP)and sentiment analysis,improving the accuracy and efficiency of sentiment classification models is crucial.This paper investigates the performance o...In the rapidly evolving landscape of natural language processing(NLP)and sentiment analysis,improving the accuracy and efficiency of sentiment classification models is crucial.This paper investigates the performance of two advanced models,the Large Language Model(LLM)LLaMA model and NLP BERT model,in the context of airline review sentiment analysis.Through fine-tuning,domain adaptation,and the application of few-shot learning,the study addresses the subtleties of sentiment expressions in airline-related text data.Employing predictive modeling and comparative analysis,the research evaluates the effectiveness of Large Language Model Meta AI(LLaMA)and Bidirectional Encoder Representations from Transformers(BERT)in capturing sentiment intricacies.Fine-tuning,including domain adaptation,enhances the models'performance in sentiment classification tasks.Additionally,the study explores the potential of few-shot learning to improve model generalization using minimal annotated data for targeted sentiment analysis.By conducting experiments on a diverse airline review dataset,the research quantifies the impact of fine-tuning,domain adaptation,and few-shot learning on model performance,providing valuable insights for industries aiming to predict recommendations and enhance customer satisfaction through a deeper understanding of sentiment in user-generated content(UGC).This research contributes to refining sentiment analysis models,ultimately fostering improved customer satisfaction in the airline industry.展开更多
The efficient market hypothesis in traditional financial theory struggles to explain the short-term irrational fluctuations in the A-share market,where investor sentiment fluctuations often serve as the core driver of...The efficient market hypothesis in traditional financial theory struggles to explain the short-term irrational fluctuations in the A-share market,where investor sentiment fluctuations often serve as the core driver of abnormal stock price movements.Traditional sentiment measurement methods suffer from limitations such as lag,high misjudgment rates,and the inability to distinguish confounding factors.To more accurately explore the dynamic correlation between investor sentiment and stock price fluctuations,this paper proposes a sentiment analysis framework based on large language models(LLMs).By constructing continuous sentiment scoring factors and integrating them with a long short-term memory(LSTM)deep learning model,we analyze the correlation between investor sentiment and stock price fluctuations.Empirical results indicate that sentiment factors based on large language models can generate an annualized excess return of 9.3%in the CSI 500 index domain.The LSTM stock price prediction model incorporating sentiment features achieves a mean absolute percentage error(MAPE)as low as 2.72%,significantly outperforming traditional models.Through this analysis,we aim to provide quantitative references for optimizing investment decisions and preventing market risks.展开更多
Theburgeoning e-commerce industry hasmade online customer reviews a crucial source of feedback for businesses.Sentiment analysis,a technique used to extract subjective information from text,has become essential for un...Theburgeoning e-commerce industry hasmade online customer reviews a crucial source of feedback for businesses.Sentiment analysis,a technique used to extract subjective information from text,has become essential for understanding consumer sentiment and preferences.However,traditional sentiment analysis methods often struggle with the nuances and context of natural language.To address these issues,this study proposes a comparison of deep learningmodels that figure out the optimalmethod to accurately analyze consumer reviews onwomen’s clothing.CNNs excel at capturing local features and semantic information,while LSTMs are adept at handling long-range dependencies and contextual understanding.By integrating these two deep learning techniques,our model aims to achieve better performance in sentiment classification.The models were trained and evaluated on a dataset of women’s clothing reviews sourced from Kaggle.The dataset was pre-processed to clean and tokenize the text data,and word embeddings were used to represent words as numerical vectors.The CNN component of the model extracts local features from the text,while the LSTM component captures long-range dependencies and contextual information.The outputs of the CNN and LSTM layers are then concatenated and fed into a fully connected layer for final sentiment classification.Experimental results demonstrate that the hybrid model outperforms traditional machine learning techniques and other deep learning models in terms of accuracy,precision,recall,and F1-score.By accurately classifying sentiment,identifying key themes,and predicting future trends,our model can provide valuable insights to businesses in the apparel industry.These insights can be used to improve product design,marketing strategies,and customer service,ultimately leading to increased customer satisfaction and business success.展开更多
Aspect-oriented sentiment analysis is a meticulous sentiment analysis task that aims to analyse the sentiment polarity of specific aspects. Most of the current research builds graph convolutional networks based on dep...Aspect-oriented sentiment analysis is a meticulous sentiment analysis task that aims to analyse the sentiment polarity of specific aspects. Most of the current research builds graph convolutional networks based on dependent syntactic trees, which improves the classification performance of the models to some extent. However, the technical limitations of dependent syntactic trees can introduce considerable noise into the model. Meanwhile, it is difficult for a single graph convolutional network to aggregate both semantic and syntactic structural information of nodes, which affects the final sentence classification. To cope with the above problems, this paper proposes a bi-channel graph convolutional network model. The model introduces a phrase structure tree and transforms it into a hierarchical phrase matrix. The adjacency matrix of the dependent syntactic tree and the hierarchical phrase matrix are combined as the initial matrix of the graph convolutional network to enhance the syntactic information. The semantic information feature representations of the sentences are obtained by the graph convolutional network with a multi-head attention mechanism and fused to achieve complementary learning of dual-channel features. Experimental results show that the model performs well and improves the accuracy of sentiment classification on three public benchmark datasets, namely Rest14, Lap14 and Twitter.展开更多
Green consumption(GC)are crucial for achieving the SustainableDevelopmentGoals(SDGs).However,few studies have explored public attitudes toward GC using social media data,missing potential public concerns captured thro...Green consumption(GC)are crucial for achieving the SustainableDevelopmentGoals(SDGs).However,few studies have explored public attitudes toward GC using social media data,missing potential public concerns captured through big data.To address this gap,this study collects and analyzes public attention toward GC using web crawler technology.Based on the data from Sina Weibo,we applied RoBERTa,an advanced NLP model based on transformer architecture,to conduct fine-grained sentiment analysis of the public’s attention,attitudes and hot topics on GC,demonstrating the potential of deep learning methods in capturing dynamic and contextual emotional shifts across time and regions.Among the sample(N=188,509),53.91% expressed a positive attitude,with variation across different times and regions.Temporally,public interest in GC has shown an annual growth rate of 30.23%,gradually shifting fromfulfilling basic needs to prioritizing entertainment consumption.Spatially,GC is most prevalent in the southeast coastal regions of China,with Beijing ranking first across five evaluated domains.Individuals and government-affiliated accounts play a key role in public discussions on social networks,accounting for 45.89% and 30.01% of user reviews,respectively.A significant positive correlation exists between economic development and public attention to GC,as indicated by a Pearson correlation coefficient of 0.55.Companies,in particular,exhibit cautious behavior in the early stages of green product adoption,prioritizing profitability before making substantial investments.These findings provide valuable insights into the evolving public perception of GC,contributing to the development of more effective environmental policies in China.展开更多
Incorporation of explainability features in the decision-making web-based systems is considered a primary concern to enhance accountability,transparency,and trust in the community.Multi-domain Sentiment Analysis is a ...Incorporation of explainability features in the decision-making web-based systems is considered a primary concern to enhance accountability,transparency,and trust in the community.Multi-domain Sentiment Analysis is a significant web-based system where the explainability feature is essential for achieving user satisfaction.Conventional design methodologies such as object-oriented design methodology(OODM)have been proposed for web-based application development,which facilitates code reuse,quantification,and security at the design level.However,OODM did not provide the feature of explainability in web-based decision-making systems.X-OODM modifies the OODM with added explainable models to introduce the explainability feature for such systems.This research introduces an explainable model leveraging X-OODM for designing transparent applications for multidomain sentiment analysis.The proposed design is evaluated using the design quality metrics defined for the evaluation of the X-OODM explainable model under user context.The design quality metrics,transferability,simulatability,informativeness,and decomposability were introduced one after another over time to the evaluation of the X-OODM user context.Auxiliary metrics of accessibility and algorithmic transparency were added to increase the degree of explainability for the design.The study results reveal that introducing such explainability parameters with X-OODM appropriately increases system transparency,trustworthiness,and user understanding.The experimental results validate the enhancement of decision-making for multi-domain sentiment analysis with integration at the design level of explainability.Future work can be built in this direction by extending this work to apply the proposed X-OODM framework over different datasets and sentiment analysis applications to further scrutinize its effectiveness in real-world scenarios.展开更多
In the first half of 2025,the global textile machinery market continued to face significant headwinds,including economic slowdown,persistent inflation,and dampened consumer sentiment.According to Dr.Harald Weber,Manag...In the first half of 2025,the global textile machinery market continued to face significant headwinds,including economic slowdown,persistent inflation,and dampened consumer sentiment.According to Dr.Harald Weber,Managing Director of the VDMA Textile Machinery Association,German exports of textile machinery and accessories saw a yearon-year decrease of approximately 9%between January and May.This trend was not unique to Germany,as exports from all European countries also declined amid ongoing geopolitical tensions and unpredictable trade policies.Despite these challenges,the incoming orders are bottoming out,potentially signaling the beginning of an industry recovery.However,the protectionist policies have contributed to a cautious investment climate worldwide.And the protectionism is not limited to the U.S.,with subsidies and other unfair advantages for domestic companies distorting competition in multiple regions.Now,trade barriers are the most pressing challenge for the global textile industry,urging manufacturers to reduce strategic dependencies to mitigate risks.展开更多
This study investigates sarcasm detection in text using a dataset of 8095 sentences compiled from MUStARD and HuggingFace repositories,balanced across sarcastic and non-sarcastic classes.A sequential baseline model(LS...This study investigates sarcasm detection in text using a dataset of 8095 sentences compiled from MUStARD and HuggingFace repositories,balanced across sarcastic and non-sarcastic classes.A sequential baseline model(LSTM)is compared with transformer-based models(RoBERTa and XLNet),integrated with attention mechanisms.Transformers were chosen for their proven ability to capture long-range contextual dependencies,whereas LSTM serves as a traditional benchmark for sequential modeling.Experimental results show that RoBERTa achieves 0.87 accuracy,XLNet 0.83,and LSTM 0.52.These findings confirm that transformer architectures significantly outperform recurrent models in sarcasm detection.Future work will incorporate multimodal features and error analysis to further improve robustness.展开更多
Dialectal Arabic text classifcation(DA-TC)provides a mechanism for performing sentiment analysis on recent Arabic social media leading to many challenges owing to the natural morphology of the Arabic language and its ...Dialectal Arabic text classifcation(DA-TC)provides a mechanism for performing sentiment analysis on recent Arabic social media leading to many challenges owing to the natural morphology of the Arabic language and its wide range of dialect variations.Te availability of annotated datasets is limited,and preprocessing of the noisy content is even more challenging,sometimes resulting in the removal of important cues of sentiment from the input.To overcome such problems,this study investigates the applicability of using transfer learning based on pre-trained transformer models to classify sentiment in Arabic texts with high accuracy.Specifcally,it uses the CAMeLBERT model fnetuned for the Multi-Domain Arabic Resources for Sentiment Analysis(MARSA)dataset containing more than 56,000 manually annotated tweets annotated across political,social,sports,and technology domains.Te proposed method avoids extensive use of preprocessing and shows that raw data provides better results because they tend to retain more linguistic features.Te fne-tuned CAMeLBERT model produces state-of-the-art accuracy of 92%,precision of 91.7%,recall of 92.3%,and F1-score of 91.5%,outperforming standard machine learning models and ensemble-based/deep learning techniques.Our performance comparisons against other pre-trained models,namely AraBERTv02-twitter and MARBERT,show that transformer-based architectures are consistently the best suited when dealing with noisy Arabic texts.Tis work leads to a strong remedy for the problems in Arabic sentiment analysis and provides recommendations on easy tuning of the pre-trained models to adapt to challenging linguistic features and domain-specifc tasks.展开更多
BACKGROUND Hepatocellular carcinoma(HCC)is among the most aggressive primary liver cancers,leading to significant global mortality.While early diagnosis improves prognosis,treatment decisions,particularly between surg...BACKGROUND Hepatocellular carcinoma(HCC)is among the most aggressive primary liver cancers,leading to significant global mortality.While early diagnosis improves prognosis,treatment decisions,particularly between surgical resection and radiofrequency ablation(RFA),remain controversial.AIM To clarify this issue using sentiment analysis of medical literature alongside a meta-analysis of overall survival(OS).METHODS We included studies comparing liver resection and RFA,excluding case reports,editorials,and studies without relevant outcomes.A systematic search in PubMed and Web of Science identified 197 studies.Abstracts underwent sentiment analysis using Python’s Natural Language Toolkit library,categorizing them as favoring resection,ablation,or neutral.We also performed a meta-analysis using a random-effects model on 11 studies reporting hazard ratios(HRs)for OS.RESULTS Sentiment analysis revealed that 75.1%of abstracts were neutral,14.2%favored resection,and 10.7%favored RFA.The meta-analysis showed a significant survival advantage for liver resection,with a pooled HR of 0.5924(95%confidence interval:0.540-0.649).Heterogeneity was moderate(I²=39.98%).Despite the meta-analysis demonstrating clear survival benefits of liver resection,most abstracts maintained a neutral stance.This discrepancy highlights potential biases or hesitancy in drawing definitive conclusions.CONCLUSION The study emphasizes the need for clinicians to prioritize robust statistical evidence over narrative impressions.Liver resection remains the preferred treatment for HCC in eligible patients.展开更多
While automatic image captioning systems have made notable progress in the past few years,generating captions that fully convey sentiment remains a considerable challenge.Although existing models achieve strong perfor...While automatic image captioning systems have made notable progress in the past few years,generating captions that fully convey sentiment remains a considerable challenge.Although existing models achieve strong performance in visual recognition and factual description,they often fail to account for the emotional context that is naturally present in human-generated captions.To address this gap,we propose the Sentiment-Driven Caption Generator(SDCG),which combines transformer-based visual and textual processing withmulti-level fusion.RoBERTa is used for extracting sentiment from textual input,while visual features are handled by the Vision Transformer(ViT).These features are fused using several fusion approaches,including Concatenation,Attention,Visual-Sentiment Co-Attention(VSCA),and Cross-Attention.Our experiments demonstrate that SDCG significantly outperforms baseline models such as the Generalized Image Transformer(GIT),which achieves 82.01%,and Bootstrapping Language-Image Pre-training(BLIP),which achieves 83.07%,in sentiment accuracy.While SDCG achieves 94.52%sentiment accuracy and improves scores in BLEU and ROUGE-L,the model demonstrates clear advantages.More importantly,the captions aremore natural,as they incorporate emotional cues and contextual awareness,making them resemble those written by a human.展开更多
With the popularization of social media,public opi-nion information on emergencies spreads rapidly on the Internet,the impact of negative public opinions on an event has become more significant.Based on the organizati...With the popularization of social media,public opi-nion information on emergencies spreads rapidly on the Internet,the impact of negative public opinions on an event has become more significant.Based on the organizational form of public opinion information,the knowledge graph is used to construct the knowledge base of public opinion risk cases on the emer-gency network.The emotion recognition model of negative pub-lic opinion information based on the bi-directional long short-term memory(BiLSTM)network is studied in the model layer design,and a linear discriminant analysis(LDA)topic extraction method combined with association rules is proposed to extract and mine the semantics of negative public opinion topics to real-ize further in-depth analysis of information topics.Focusing on public health emergencies,knowledge acquisition and knowl-edge processing of public opinion information are conducted,and the experimental results show that the knowledge graph framework based on the construction can facilitate in-depth theme evolution analysis of public opinion events,thus demon-strating important research significance for reducing online pub-lic opinion risks.展开更多
In this paper,the sentiment classification method of multimodal adversarial autoencoder is studied.This paper includes the introduction of the multimodal adversarial autoencoder emotion classification method and the e...In this paper,the sentiment classification method of multimodal adversarial autoencoder is studied.This paper includes the introduction of the multimodal adversarial autoencoder emotion classification method and the experiment of the emotion classification method based on the encoder.The experimental analysis shows that the encoder has higher precision than other encoders in emotion classification.It is hoped that this analysis can provide some reference for the emotion classification under the current intelligent algorithm mode.展开更多
X(formerly known as Twitter)is one of the most prominent social media platforms,enabling users to share short messages(tweets)with the public or their followers.It serves various purposes,from real-time news dissemina...X(formerly known as Twitter)is one of the most prominent social media platforms,enabling users to share short messages(tweets)with the public or their followers.It serves various purposes,from real-time news dissemination and political discourse to trend spotting and consumer engagement.X has emerged as a key space for understanding shifting brand perceptions,consumer preferences,and product-related sentiment in the fashion industry.However,the platform’s informal,dynamic,and context-dependent language poses substantial challenges for sentiment analysis,mainly when attempting to detect sarcasm,slang,and nuanced emotional tones.This study introduces a hybrid deep learning framework that integrates Transformer encoders,recurrent neural networks(i.e.,Long Short-Term Memory(LSTM)and Gated Recurrent Unit(GRU)),and attention mechanisms to improve the accuracy of fashion-related sentiment classification.These methods were selected due to their proven strength in capturing both contextual dependencies and sequential structures,which are essential for interpreting short-form text.Our model was evaluated on a dataset of 20,000 fashion tweets.The experimental results demonstrate a classification accuracy of 92.25%,outperforming conventional models such as Logistic Regression,Linear Support Vector Machine(SVM),and even standalone LSTM by a margin of up to 8%.This improvement highlights the importance of hybrid architectures in handling noisy,informal social media data.This study’s findings offer strong implications for digital marketing and brand management,where timely sentiment detection is critical.Despite the promising results,challenges remain regarding the precise identification of negative sentiments,indicating that further work is needed to detect subtle and contextually embedded expressions.展开更多
Sentiment analysis plays an important role in distilling and clarifying content from movie reviews,aiding the audience in understanding universal views towards the movie.However,the abundance of reviews and the risk o...Sentiment analysis plays an important role in distilling and clarifying content from movie reviews,aiding the audience in understanding universal views towards the movie.However,the abundance of reviews and the risk of encountering spoilers pose challenges for efcient sentiment analysis,particularly in Arabic content.Tis study proposed a Stochastic Gradient Descent(SGD)machine learning(ML)model tailored for sentiment analysis in Arabic and English movie reviews.SGD allows for fexible model complexity adjustments,which can adapt well to the Involvement of Arabic language data.Tis adaptability ensures that the model can capture the nuances and specifc local patterns of Arabic text,leading to better performance.Two distinct language datasets were utilized,and extensive pre-processing steps were employed to optimize the datasets for analysis.Te proposed SGD model,designed to accommodate the nuances of each language,aims to surpass existing models in terms of accuracy and efciency.Te SGD model achieves an accuracy of 84.89 on the Arabic dataset and 87.44 on the English dataset,making it the top-performing model in terms of accuracy on both datasets.Tis indicates that the SGD model consistently demonstrates high accuracy levels across Arabic and English datasets.Tis study helps deepen the understanding of sentiments across various linguistic datasets.Unlike many studies that focus solely on movie reviews,the Arabic dataset utilized here includes hotel reviews,ofering a broader perspective.展开更多
Transboundary rivers,traversing multiple national borders,integrate sovereign states into a unified ecological system,complicating water resource governance amid rising global water scarcity and geopolitical tensions....Transboundary rivers,traversing multiple national borders,integrate sovereign states into a unified ecological system,complicating water resource governance amid rising global water scarcity and geopolitical tensions.Consequently,transboundary river governance exemplifies the public resource dilemma.This study,framed by constructivist international relations theory,examines the Lancang-Mekong River Basin as a case study,using data from multiple sources and socioeconomic indicators to explore the evolution of collective identity among riparian countries and its influencing factors.Key findings include:(1)The collective identity of riparian countries evolved in three phases:emergence(1971-1991),formation(1992-2014),and development(2015–2022).During this process,basin governance evolved from limited mechanisms to a more comprehensive,basin-wide system,with an expanded issue range and an increasing number of cooperation agreements.Cooperative attitudes transitioned from broadly positive to differentiated,ultimately aligning more favorably.(2)Economic interdependence is critical to the formation of collective identity among riparian countries,while diplomatic alignment enhances cooperation.(3)Extreme weather events and political globalization exert dual effects on collective identity formation:extreme weather fosters cooperation but also prioritizes domestic recovery,complicating agreements and expanding issues.Political globalization has facilitated institutionalization and normalization of cooperation,though external involvement has deepened divisions in cooperative attitudes.This study contributes to theoretical perspectives on transboundary river governance and supports collective action in global environmental governance.展开更多
文摘This essay analyzes a crucial difference in the ways in which erotic feelings are articulated in the sentimental novel from eighteenth-century England and Feng Menglong＇s stories of qing from late Ming （1573-1644）. It compares Feng＇s stories and Samuel Richardson＇s novels with a focus on how they chart the courses of love affairs. The essay argues that English sentimental novels accentuate psychological depth while their Chinese counterparts preclude depth with ritualized expressions of feelings. The contrast goes a long way to explaining the bifurcation of English and Chinese fiction in modern eras; one gives rise to several nuanced forms of psychological realism, modulating narrative perspectives as a way of mimicking the complex workings of layered consciousness. The Chinese stories of qing, on the other hand, suggest a different theory of love, one that downplays subjective control of feelings in favor of the effects of social or accidental circumstances. They evolve into a fictional tradition that aestheticizes and stylizes qing, reducing it to a surface of fixed patterns by virtue of inserting verse pieces into prose narratives.
基金supported by the Deanship of Graduate Studies and Scientific Research at Jouf University under Grant No.DGSSR-2024-02-01011.
文摘Wireless technologies and the Internet of Things(IoT)are being extensively utilized for advanced development in traditional communication systems.This evolution lowers the cost of the extensive use of sensors,changing the way devices interact and communicate in dynamic and uncertain situations.Such a constantly evolving environment presents enormous challenges to preserving a secure and lightweight IoT system.Therefore,it leads to the design of effective and trusted routing to support sustainable smart cities.This research study proposed a Genetic Algorithm sentiment-enhanced secured optimization model,which combines big data analytics and analysis rules to evaluate user feedback.The sentiment analysis is utilized to assess the perception of network performance,allowing the classification of device behavior as positive,neutral,or negative.By integrating sentiment-driven insights,the IoT network adjusts the system configurations to enhance the performance using network behaviour in terms of latency,reliability,fault tolerance,and sentiment score.Accordingly to the analysis,the proposed model categorizes the behavior of devices as positive,neutral,or negative,facilitating real-time monitoring for crucial applications.Experimental results revealed a significant improvement in the proposed model for threat prevention and network efficiency,demonstrating its resilience for real-time IoT applications.
基金supported by the Science and Technology Project of Henan Province(No.222102210081).
文摘Joint Multimodal Aspect-based Sentiment Analysis(JMASA)is a significant task in the research of multimodal fine-grained sentiment analysis,which combines two subtasks:Multimodal Aspect Term Extraction(MATE)and Multimodal Aspect-oriented Sentiment Classification(MASC).Currently,most existing models for JMASA only perform text and image feature encoding from a basic level,but often neglect the in-depth analysis of unimodal intrinsic features,which may lead to the low accuracy of aspect term extraction and the poor ability of sentiment prediction due to the insufficient learning of intra-modal features.Given this problem,we propose a Text-Image Feature Fine-grained Learning(TIFFL)model for JMASA.First,we construct an enhanced adjacency matrix of word dependencies and adopt graph convolutional network to learn the syntactic structure features for text,which addresses the context interference problem of identifying different aspect terms.Then,the adjective-noun pairs extracted from image are introduced to enable the semantic representation of visual features more intuitive,which addresses the ambiguous semantic extraction problem during image feature learning.Thereby,the model performance of aspect term extraction and sentiment polarity prediction can be further optimized and enhanced.Experiments on two Twitter benchmark datasets demonstrate that TIFFL achieves competitive results for JMASA,MATE and MASC,thus validating the effectiveness of our proposed methods.
基金supported in part by the National Nature Science Foundation of China under Grants 62476216 and 62273272in part by the Key Research and Development Program of Shaanxi Province under Grant 2024GX-YBXM-146+1 种基金in part by the Scientific Research Program Funded by Education Department of Shaanxi Provincial Government under Grant 23JP091the Youth Innovation Team of Shaanxi Universities.
文摘Multimodal Aspect-Based Sentiment Analysis(MABSA)aims to detect sentiment polarity toward specific aspects by leveraging both textual and visual inputs.However,existing models suffer from weak aspectimage alignment,modality imbalance dominated by textual signals,and limited reasoning for implicit or ambiguous sentiments requiring external knowledge.To address these issues,we propose a unified framework named Gated-Linear Aspect-Aware Multimodal Sentiment Network(GLAMSNet).First of all,an input encoding module is employed to construct modality-specific and aspect-aware representations.Subsequently,we introduce an image–aspect correlation matching module to provide hierarchical supervision for visual-textual alignment.Building upon these components,we further design a Gated-Linear Aspect-Aware Fusion(GLAF)module to enhance aspect-aware representation learning by adaptively filtering irrelevant textual information and refining semantic alignment under aspect guidance.Additionally,an External Language Model Knowledge-Guided mechanism is integrated to incorporate sentimentaware prior knowledge from GPT-4o,enabling robust semantic reasoning especially under noisy or ambiguous inputs.Experimental studies conducted based on Twitter-15 and Twitter-17 datasets demonstrate that the proposed model outperforms most state-of-the-art methods,achieving 79.36%accuracy and 74.72%F1-score,and 74.31%accuracy and 72.01%F1-score,respectively.
文摘In the rapidly evolving landscape of natural language processing(NLP)and sentiment analysis,improving the accuracy and efficiency of sentiment classification models is crucial.This paper investigates the performance of two advanced models,the Large Language Model(LLM)LLaMA model and NLP BERT model,in the context of airline review sentiment analysis.Through fine-tuning,domain adaptation,and the application of few-shot learning,the study addresses the subtleties of sentiment expressions in airline-related text data.Employing predictive modeling and comparative analysis,the research evaluates the effectiveness of Large Language Model Meta AI(LLaMA)and Bidirectional Encoder Representations from Transformers(BERT)in capturing sentiment intricacies.Fine-tuning,including domain adaptation,enhances the models'performance in sentiment classification tasks.Additionally,the study explores the potential of few-shot learning to improve model generalization using minimal annotated data for targeted sentiment analysis.By conducting experiments on a diverse airline review dataset,the research quantifies the impact of fine-tuning,domain adaptation,and few-shot learning on model performance,providing valuable insights for industries aiming to predict recommendations and enhance customer satisfaction through a deeper understanding of sentiment in user-generated content(UGC).This research contributes to refining sentiment analysis models,ultimately fostering improved customer satisfaction in the airline industry.
文摘The efficient market hypothesis in traditional financial theory struggles to explain the short-term irrational fluctuations in the A-share market,where investor sentiment fluctuations often serve as the core driver of abnormal stock price movements.Traditional sentiment measurement methods suffer from limitations such as lag,high misjudgment rates,and the inability to distinguish confounding factors.To more accurately explore the dynamic correlation between investor sentiment and stock price fluctuations,this paper proposes a sentiment analysis framework based on large language models(LLMs).By constructing continuous sentiment scoring factors and integrating them with a long short-term memory(LSTM)deep learning model,we analyze the correlation between investor sentiment and stock price fluctuations.Empirical results indicate that sentiment factors based on large language models can generate an annualized excess return of 9.3%in the CSI 500 index domain.The LSTM stock price prediction model incorporating sentiment features achieves a mean absolute percentage error(MAPE)as low as 2.72%,significantly outperforming traditional models.Through this analysis,we aim to provide quantitative references for optimizing investment decisions and preventing market risks.
文摘Theburgeoning e-commerce industry hasmade online customer reviews a crucial source of feedback for businesses.Sentiment analysis,a technique used to extract subjective information from text,has become essential for understanding consumer sentiment and preferences.However,traditional sentiment analysis methods often struggle with the nuances and context of natural language.To address these issues,this study proposes a comparison of deep learningmodels that figure out the optimalmethod to accurately analyze consumer reviews onwomen’s clothing.CNNs excel at capturing local features and semantic information,while LSTMs are adept at handling long-range dependencies and contextual understanding.By integrating these two deep learning techniques,our model aims to achieve better performance in sentiment classification.The models were trained and evaluated on a dataset of women’s clothing reviews sourced from Kaggle.The dataset was pre-processed to clean and tokenize the text data,and word embeddings were used to represent words as numerical vectors.The CNN component of the model extracts local features from the text,while the LSTM component captures long-range dependencies and contextual information.The outputs of the CNN and LSTM layers are then concatenated and fed into a fully connected layer for final sentiment classification.Experimental results demonstrate that the hybrid model outperforms traditional machine learning techniques and other deep learning models in terms of accuracy,precision,recall,and F1-score.By accurately classifying sentiment,identifying key themes,and predicting future trends,our model can provide valuable insights to businesses in the apparel industry.These insights can be used to improve product design,marketing strategies,and customer service,ultimately leading to increased customer satisfaction and business success.
文摘Aspect-oriented sentiment analysis is a meticulous sentiment analysis task that aims to analyse the sentiment polarity of specific aspects. Most of the current research builds graph convolutional networks based on dependent syntactic trees, which improves the classification performance of the models to some extent. However, the technical limitations of dependent syntactic trees can introduce considerable noise into the model. Meanwhile, it is difficult for a single graph convolutional network to aggregate both semantic and syntactic structural information of nodes, which affects the final sentence classification. To cope with the above problems, this paper proposes a bi-channel graph convolutional network model. The model introduces a phrase structure tree and transforms it into a hierarchical phrase matrix. The adjacency matrix of the dependent syntactic tree and the hierarchical phrase matrix are combined as the initial matrix of the graph convolutional network to enhance the syntactic information. The semantic information feature representations of the sentences are obtained by the graph convolutional network with a multi-head attention mechanism and fused to achieve complementary learning of dual-channel features. Experimental results show that the model performs well and improves the accuracy of sentiment classification on three public benchmark datasets, namely Rest14, Lap14 and Twitter.
基金supported by the National Nature Foundation of China under Grants(No.72104108)the College Students’Innovation and Entrepreneurship Training Program(No.202410298155Y).
文摘Green consumption(GC)are crucial for achieving the SustainableDevelopmentGoals(SDGs).However,few studies have explored public attitudes toward GC using social media data,missing potential public concerns captured through big data.To address this gap,this study collects and analyzes public attention toward GC using web crawler technology.Based on the data from Sina Weibo,we applied RoBERTa,an advanced NLP model based on transformer architecture,to conduct fine-grained sentiment analysis of the public’s attention,attitudes and hot topics on GC,demonstrating the potential of deep learning methods in capturing dynamic and contextual emotional shifts across time and regions.Among the sample(N=188,509),53.91% expressed a positive attitude,with variation across different times and regions.Temporally,public interest in GC has shown an annual growth rate of 30.23%,gradually shifting fromfulfilling basic needs to prioritizing entertainment consumption.Spatially,GC is most prevalent in the southeast coastal regions of China,with Beijing ranking first across five evaluated domains.Individuals and government-affiliated accounts play a key role in public discussions on social networks,accounting for 45.89% and 30.01% of user reviews,respectively.A significant positive correlation exists between economic development and public attention to GC,as indicated by a Pearson correlation coefficient of 0.55.Companies,in particular,exhibit cautious behavior in the early stages of green product adoption,prioritizing profitability before making substantial investments.These findings provide valuable insights into the evolving public perception of GC,contributing to the development of more effective environmental policies in China.
基金support of the Deanship of Research and Graduate Studies at Ajman University under Projects 2024-IRG-ENiT-36 and 2024-IRG-ENIT-29.
文摘Incorporation of explainability features in the decision-making web-based systems is considered a primary concern to enhance accountability,transparency,and trust in the community.Multi-domain Sentiment Analysis is a significant web-based system where the explainability feature is essential for achieving user satisfaction.Conventional design methodologies such as object-oriented design methodology(OODM)have been proposed for web-based application development,which facilitates code reuse,quantification,and security at the design level.However,OODM did not provide the feature of explainability in web-based decision-making systems.X-OODM modifies the OODM with added explainable models to introduce the explainability feature for such systems.This research introduces an explainable model leveraging X-OODM for designing transparent applications for multidomain sentiment analysis.The proposed design is evaluated using the design quality metrics defined for the evaluation of the X-OODM explainable model under user context.The design quality metrics,transferability,simulatability,informativeness,and decomposability were introduced one after another over time to the evaluation of the X-OODM user context.Auxiliary metrics of accessibility and algorithmic transparency were added to increase the degree of explainability for the design.The study results reveal that introducing such explainability parameters with X-OODM appropriately increases system transparency,trustworthiness,and user understanding.The experimental results validate the enhancement of decision-making for multi-domain sentiment analysis with integration at the design level of explainability.Future work can be built in this direction by extending this work to apply the proposed X-OODM framework over different datasets and sentiment analysis applications to further scrutinize its effectiveness in real-world scenarios.
文摘In the first half of 2025,the global textile machinery market continued to face significant headwinds,including economic slowdown,persistent inflation,and dampened consumer sentiment.According to Dr.Harald Weber,Managing Director of the VDMA Textile Machinery Association,German exports of textile machinery and accessories saw a yearon-year decrease of approximately 9%between January and May.This trend was not unique to Germany,as exports from all European countries also declined amid ongoing geopolitical tensions and unpredictable trade policies.Despite these challenges,the incoming orders are bottoming out,potentially signaling the beginning of an industry recovery.However,the protectionist policies have contributed to a cautious investment climate worldwide.And the protectionism is not limited to the U.S.,with subsidies and other unfair advantages for domestic companies distorting competition in multiple regions.Now,trade barriers are the most pressing challenge for the global textile industry,urging manufacturers to reduce strategic dependencies to mitigate risks.
文摘This study investigates sarcasm detection in text using a dataset of 8095 sentences compiled from MUStARD and HuggingFace repositories,balanced across sarcastic and non-sarcastic classes.A sequential baseline model(LSTM)is compared with transformer-based models(RoBERTa and XLNet),integrated with attention mechanisms.Transformers were chosen for their proven ability to capture long-range contextual dependencies,whereas LSTM serves as a traditional benchmark for sequential modeling.Experimental results show that RoBERTa achieves 0.87 accuracy,XLNet 0.83,and LSTM 0.52.These findings confirm that transformer architectures significantly outperform recurrent models in sarcasm detection.Future work will incorporate multimodal features and error analysis to further improve robustness.
基金funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2504).
文摘Dialectal Arabic text classifcation(DA-TC)provides a mechanism for performing sentiment analysis on recent Arabic social media leading to many challenges owing to the natural morphology of the Arabic language and its wide range of dialect variations.Te availability of annotated datasets is limited,and preprocessing of the noisy content is even more challenging,sometimes resulting in the removal of important cues of sentiment from the input.To overcome such problems,this study investigates the applicability of using transfer learning based on pre-trained transformer models to classify sentiment in Arabic texts with high accuracy.Specifcally,it uses the CAMeLBERT model fnetuned for the Multi-Domain Arabic Resources for Sentiment Analysis(MARSA)dataset containing more than 56,000 manually annotated tweets annotated across political,social,sports,and technology domains.Te proposed method avoids extensive use of preprocessing and shows that raw data provides better results because they tend to retain more linguistic features.Te fne-tuned CAMeLBERT model produces state-of-the-art accuracy of 92%,precision of 91.7%,recall of 92.3%,and F1-score of 91.5%,outperforming standard machine learning models and ensemble-based/deep learning techniques.Our performance comparisons against other pre-trained models,namely AraBERTv02-twitter and MARBERT,show that transformer-based architectures are consistently the best suited when dealing with noisy Arabic texts.Tis work leads to a strong remedy for the problems in Arabic sentiment analysis and provides recommendations on easy tuning of the pre-trained models to adapt to challenging linguistic features and domain-specifc tasks.
文摘BACKGROUND Hepatocellular carcinoma(HCC)is among the most aggressive primary liver cancers,leading to significant global mortality.While early diagnosis improves prognosis,treatment decisions,particularly between surgical resection and radiofrequency ablation(RFA),remain controversial.AIM To clarify this issue using sentiment analysis of medical literature alongside a meta-analysis of overall survival(OS).METHODS We included studies comparing liver resection and RFA,excluding case reports,editorials,and studies without relevant outcomes.A systematic search in PubMed and Web of Science identified 197 studies.Abstracts underwent sentiment analysis using Python’s Natural Language Toolkit library,categorizing them as favoring resection,ablation,or neutral.We also performed a meta-analysis using a random-effects model on 11 studies reporting hazard ratios(HRs)for OS.RESULTS Sentiment analysis revealed that 75.1%of abstracts were neutral,14.2%favored resection,and 10.7%favored RFA.The meta-analysis showed a significant survival advantage for liver resection,with a pooled HR of 0.5924(95%confidence interval:0.540-0.649).Heterogeneity was moderate(I²=39.98%).Despite the meta-analysis demonstrating clear survival benefits of liver resection,most abstracts maintained a neutral stance.This discrepancy highlights potential biases or hesitancy in drawing definitive conclusions.CONCLUSION The study emphasizes the need for clinicians to prioritize robust statistical evidence over narrative impressions.Liver resection remains the preferred treatment for HCC in eligible patients.
基金funded by the Committee of Science of the Ministry of Science andHigher Education of the Republic of Kazakhstan(Grant No.BR24993166).
文摘While automatic image captioning systems have made notable progress in the past few years,generating captions that fully convey sentiment remains a considerable challenge.Although existing models achieve strong performance in visual recognition and factual description,they often fail to account for the emotional context that is naturally present in human-generated captions.To address this gap,we propose the Sentiment-Driven Caption Generator(SDCG),which combines transformer-based visual and textual processing withmulti-level fusion.RoBERTa is used for extracting sentiment from textual input,while visual features are handled by the Vision Transformer(ViT).These features are fused using several fusion approaches,including Concatenation,Attention,Visual-Sentiment Co-Attention(VSCA),and Cross-Attention.Our experiments demonstrate that SDCG significantly outperforms baseline models such as the Generalized Image Transformer(GIT),which achieves 82.01%,and Bootstrapping Language-Image Pre-training(BLIP),which achieves 83.07%,in sentiment accuracy.While SDCG achieves 94.52%sentiment accuracy and improves scores in BLEU and ROUGE-L,the model demonstrates clear advantages.More importantly,the captions aremore natural,as they incorporate emotional cues and contextual awareness,making them resemble those written by a human.
基金supported by the National Social Science Foundation Major Project(22&ZD135)the National Social Science Fund National Emergency Management System Construction Research Project(20VYJ061).
文摘With the popularization of social media,public opi-nion information on emergencies spreads rapidly on the Internet,the impact of negative public opinions on an event has become more significant.Based on the organizational form of public opinion information,the knowledge graph is used to construct the knowledge base of public opinion risk cases on the emer-gency network.The emotion recognition model of negative pub-lic opinion information based on the bi-directional long short-term memory(BiLSTM)network is studied in the model layer design,and a linear discriminant analysis(LDA)topic extraction method combined with association rules is proposed to extract and mine the semantics of negative public opinion topics to real-ize further in-depth analysis of information topics.Focusing on public health emergencies,knowledge acquisition and knowl-edge processing of public opinion information are conducted,and the experimental results show that the knowledge graph framework based on the construction can facilitate in-depth theme evolution analysis of public opinion events,thus demon-strating important research significance for reducing online pub-lic opinion risks.
文摘In this paper,the sentiment classification method of multimodal adversarial autoencoder is studied.This paper includes the introduction of the multimodal adversarial autoencoder emotion classification method and the experiment of the emotion classification method based on the encoder.The experimental analysis shows that the encoder has higher precision than other encoders in emotion classification.It is hoped that this analysis can provide some reference for the emotion classification under the current intelligent algorithm mode.
文摘X(formerly known as Twitter)is one of the most prominent social media platforms,enabling users to share short messages(tweets)with the public or their followers.It serves various purposes,from real-time news dissemination and political discourse to trend spotting and consumer engagement.X has emerged as a key space for understanding shifting brand perceptions,consumer preferences,and product-related sentiment in the fashion industry.However,the platform’s informal,dynamic,and context-dependent language poses substantial challenges for sentiment analysis,mainly when attempting to detect sarcasm,slang,and nuanced emotional tones.This study introduces a hybrid deep learning framework that integrates Transformer encoders,recurrent neural networks(i.e.,Long Short-Term Memory(LSTM)and Gated Recurrent Unit(GRU)),and attention mechanisms to improve the accuracy of fashion-related sentiment classification.These methods were selected due to their proven strength in capturing both contextual dependencies and sequential structures,which are essential for interpreting short-form text.Our model was evaluated on a dataset of 20,000 fashion tweets.The experimental results demonstrate a classification accuracy of 92.25%,outperforming conventional models such as Logistic Regression,Linear Support Vector Machine(SVM),and even standalone LSTM by a margin of up to 8%.This improvement highlights the importance of hybrid architectures in handling noisy,informal social media data.This study’s findings offer strong implications for digital marketing and brand management,where timely sentiment detection is critical.Despite the promising results,challenges remain regarding the precise identification of negative sentiments,indicating that further work is needed to detect subtle and contextually embedded expressions.
文摘Sentiment analysis plays an important role in distilling and clarifying content from movie reviews,aiding the audience in understanding universal views towards the movie.However,the abundance of reviews and the risk of encountering spoilers pose challenges for efcient sentiment analysis,particularly in Arabic content.Tis study proposed a Stochastic Gradient Descent(SGD)machine learning(ML)model tailored for sentiment analysis in Arabic and English movie reviews.SGD allows for fexible model complexity adjustments,which can adapt well to the Involvement of Arabic language data.Tis adaptability ensures that the model can capture the nuances and specifc local patterns of Arabic text,leading to better performance.Two distinct language datasets were utilized,and extensive pre-processing steps were employed to optimize the datasets for analysis.Te proposed SGD model,designed to accommodate the nuances of each language,aims to surpass existing models in terms of accuracy and efciency.Te SGD model achieves an accuracy of 84.89 on the Arabic dataset and 87.44 on the English dataset,making it the top-performing model in terms of accuracy on both datasets.Tis indicates that the SGD model consistently demonstrates high accuracy levels across Arabic and English datasets.Tis study helps deepen the understanding of sentiments across various linguistic datasets.Unlike many studies that focus solely on movie reviews,the Arabic dataset utilized here includes hotel reviews,ofering a broader perspective.
基金National Science and Technology Support Program of China,No.2013BAB06B03National Key Research and Development Program of China,No.2016YFA0601600。
文摘Transboundary rivers,traversing multiple national borders,integrate sovereign states into a unified ecological system,complicating water resource governance amid rising global water scarcity and geopolitical tensions.Consequently,transboundary river governance exemplifies the public resource dilemma.This study,framed by constructivist international relations theory,examines the Lancang-Mekong River Basin as a case study,using data from multiple sources and socioeconomic indicators to explore the evolution of collective identity among riparian countries and its influencing factors.Key findings include:(1)The collective identity of riparian countries evolved in three phases:emergence(1971-1991),formation(1992-2014),and development(2015–2022).During this process,basin governance evolved from limited mechanisms to a more comprehensive,basin-wide system,with an expanded issue range and an increasing number of cooperation agreements.Cooperative attitudes transitioned from broadly positive to differentiated,ultimately aligning more favorably.(2)Economic interdependence is critical to the formation of collective identity among riparian countries,while diplomatic alignment enhances cooperation.(3)Extreme weather events and political globalization exert dual effects on collective identity formation:extreme weather fosters cooperation but also prioritizes domestic recovery,complicating agreements and expanding issues.Political globalization has facilitated institutionalization and normalization of cooperation,though external involvement has deepened divisions in cooperative attitudes.This study contributes to theoretical perspectives on transboundary river governance and supports collective action in global environmental governance.
