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
Most consumers read online reviews written by different users before making purchase decisions,where each opinion expresses some sentiment.Therefore,sentiment analysis is currently a hot topic of research.In particula...Most consumers read online reviews written by different users before making purchase decisions,where each opinion expresses some sentiment.Therefore,sentiment analysis is currently a hot topic of research.In particular,aspect-based sentiment analysis concerns the exploration of emotions,opinions and facts that are expressed by people,usually in the form of polarity.It is crucial to consider polarity calculations and not simply categorize reviews as positive,negative,or neutral.Currently,the available lexicon-based method accuracy is affected by limited coverage.Several of the available polarity estimation techniques are too general and may not reect the aspect/topic in question if reviews contain a wide range of information about different topics.This paper presents a model for the polarity estimation of customer reviews using aspect-based sentiment analysis(ABSA-PER).ABSA-PER has three major phases:data preprocessing,aspect co-occurrence calculation(CAC)and polarity estimation.A multi-domain sentiment dataset,Twitter dataset,and trust pilot forum dataset(developed by us by dened judgement rules)are used to verify ABSA-PER.Experimental outcomes show that ABSA-PER achieves better accuracy,i.e.,85.7%accuracy for aspect extraction and 86.5%accuracy in terms of polarity estimation,than that of the baseline methods.展开更多
People utilize microblogs and other social media platforms to express their thoughts and feelings regarding current events,public products and the latest affairs.People share their thoughts and feelings about various ...People utilize microblogs and other social media platforms to express their thoughts and feelings regarding current events,public products and the latest affairs.People share their thoughts and feelings about various topics,including products,news,blogs,etc.In user reviews and tweets,sentiment analysis is used to discover opinions and feelings.Sentiment polarity is a term used to describe how sentiment is represented.Positive,neutral and negative are all examples of it.This area is still in its infancy and needs several critical upgrades.Slang and hidden emotions can detract from the accuracy of traditional techniques.Existing methods only evaluate the polarity strength of the sentiment words when dividing them into positive and negative categories.Some existing strategies are domain-specific.The proposed model incorporates aspect extraction,association rule mining and the deep learning technique Bidirectional EncoderRepresentations from Transformers(BERT).Aspects are extracted using Part of Speech Tagger and association rulemining is used to associate aspects with opinion words.Later,classification was performed using BER.The proposed approach attained an average of 89.45%accuracy,88.45%precision and 85.98%recall on different datasets of products and Twitter.The results showed that the proposed technique achieved better than state-of-the-art sentiment analysis techniques.展开更多
Aspect-based sentiment analysis(ABSA)is a fine-grained process.Its fundamental subtasks are aspect termextraction(ATE)and aspect polarity classification(APC),and these subtasks are dependent and closely related.Howeve...Aspect-based sentiment analysis(ABSA)is a fine-grained process.Its fundamental subtasks are aspect termextraction(ATE)and aspect polarity classification(APC),and these subtasks are dependent and closely related.However,most existing works on Arabic ABSA content separately address them,assume that aspect terms are preidentified,or use a pipeline model.Pipeline solutions design different models for each task,and the output from the ATE model is used as the input to the APC model,which may result in error propagation among different steps because APC is affected by ATE error.These methods are impractical for real-world scenarios where the ATE task is the base task for APC,and its result impacts the accuracy of APC.Thus,in this study,we focused on a multi-task learning model for Arabic ATE and APC in which the model is jointly trained on two subtasks simultaneously in a singlemodel.This paper integrates themulti-task model,namely Local Cotext Foucse-Aspect Term Extraction and Polarity classification(LCF-ATEPC)and Arabic Bidirectional Encoder Representation from Transformers(AraBERT)as a shred layer for Arabic contextual text representation.The LCF-ATEPC model is based on a multi-head selfattention and local context focus mechanism(LCF)to capture the interactive information between an aspect and its context.Moreover,data augmentation techniques are proposed based on state-of-the-art augmentation techniques(word embedding substitution with constraints and contextual embedding(AraBERT))to increase the diversity of the training dataset.This paper examined the effect of data augmentation on the multi-task model for Arabic ABSA.Extensive experiments were conducted on the original and combined datasets(merging the original and augmented datasets).Experimental results demonstrate that the proposed Multi-task model outperformed existing APC techniques.Superior results were obtained by AraBERT and LCF-ATEPC with fusion layer(AR-LCF-ATEPC-Fusion)and the proposed data augmentation word embedding-based method(FastText)on the combined dataset.展开更多
Aspect-based sentiment analysis aims to detect and classify the sentiment polarities as negative,positive,or neutral while associating them with their identified aspects from the corresponding context.In this regard,p...Aspect-based sentiment analysis aims to detect and classify the sentiment polarities as negative,positive,or neutral while associating them with their identified aspects from the corresponding context.In this regard,prior methodologies widely utilize either word embedding or tree-based rep-resentations.Meanwhile,the separate use of those deep features such as word embedding and tree-based dependencies has become a significant cause of information loss.Generally,word embedding preserves the syntactic and semantic relations between a couple of terms lying in a sentence.Besides,the tree-based structure conserves the grammatical and logical dependencies of context.In addition,the sentence-oriented word position describes a critical factor that influences the contextual information of a targeted sentence.Therefore,knowledge of the position-oriented information of words in a sentence has been considered significant.In this study,we propose to use word embedding,tree-based representation,and contextual position information in combination to evaluate whether their combination will improve the result’s effectiveness or not.In the meantime,their joint utilization enhances the accurate identification and extraction of targeted aspect terms,which also influences their classification process.In this research paper,we propose a method named Attention Based Multi-Channel Convolutional Neural Net-work(Att-MC-CNN)that jointly utilizes these three deep features such as word embedding with tree-based structure and contextual position informa-tion.These three parameters deliver to Multi-Channel Convolutional Neural Network(MC-CNN)that identifies and extracts the potential terms and classifies their polarities.In addition,these terms have been further filtered with the attention mechanism,which determines the most significant words.The empirical analysis proves the proposed approach’s effectiveness compared to existing techniques when evaluated on standard datasets.The experimental results represent our approach outperforms in the F1 measure with an overall achievement of 94%in identifying aspects and 92%in the task of sentiment classification.展开更多
The aspect-based sentiment analysis(ABSA)consists of two subtasksaspect term extraction and aspect sentiment prediction.Most methods conduct the ABSA task by handling the subtasks in a pipeline manner,whereby problems...The aspect-based sentiment analysis(ABSA)consists of two subtasksaspect term extraction and aspect sentiment prediction.Most methods conduct the ABSA task by handling the subtasks in a pipeline manner,whereby problems in performance and real application emerge.In this study,we propose an end-to-end ABSA model,namely,SSi-LSi,which fuses the syntactic structure information and the lexical semantic information,to address the limitation that existing end-to-end methods do not fully exploit the text information.Through two network branches,the model extracts syntactic structure information and lexical semantic information,which integrates the part of speech,sememes,and context,respectively.Then,on the basis of an attention mechanism,the model further realizes the fusion of the syntactic structure information and the lexical semantic information to obtain higher quality ABSA results,in which way the text information is fully used.Subsequent experiments demonstrate that the SSi-LSi model has certain advantages in using different text information.展开更多
The aspect-based sentiment analysis(ABSA) consists of two subtasks—aspect term extraction and aspect sentiment prediction. Existing methods deal with both subtasks one by one in a pipeline manner, in which there lies...The aspect-based sentiment analysis(ABSA) consists of two subtasks—aspect term extraction and aspect sentiment prediction. Existing methods deal with both subtasks one by one in a pipeline manner, in which there lies some problems in performance and real application. This study investigates the end-to-end ABSA and proposes a novel multitask multiview network(MTMVN) architecture. Specifically, the architecture takes the unified ABSA as the main task with the two subtasks as auxiliary tasks. Meanwhile, the representation obtained from the branch network of the main task is regarded as the global view, whereas the representations of the two subtasks are considered two local views with different emphases. Through multitask learning, the main task can be facilitated by additional accurate aspect boundary information and sentiment polarity information. By enhancing the correlations between the views under the idea of multiview learning, the representation of the global view can be optimized to improve the overall performance of the model. The experimental results on three benchmark datasets show that the proposed method exceeds the existing pipeline methods and end-to-end methods, proving the superiority of our MTMVN architecture.展开更多
The Aspect-Based Sentiment Analysis(ABSA)task is designed to judge the sentiment polarity of a particular aspect in a review.Recent studies have proved that GCN can capture syntactic and semantic features from depende...The Aspect-Based Sentiment Analysis(ABSA)task is designed to judge the sentiment polarity of a particular aspect in a review.Recent studies have proved that GCN can capture syntactic and semantic features from dependency graphs generated by dependency trees and semantic graphs generated by Multi-headed self-attention(MHSA).However,these approaches do not highlight the sentiment information associated with aspect in the syntactic and semantic graphs.We propose the Aspect-Guided Multi-Graph Convolutional Networks(AGGCN)for Aspect-Based Sentiment Classification.Specifically,we reconstruct two kinds of graphs,changing the weight of the dependency graph by distance from aspect and improving the semantic graph by Aspect-guided MHSA.For interactive learning of syntax and semantics,we dynamically fuse syntactic and semantic diagrams to generate syntactic-semantic graphs to learn emotional features jointly.In addition,Multi-dropout is added to solve the overftting of AGGCN in training.The experimental results on extensive datasets show that our model AGGCN achieves particularly advanced results and validates the effectiveness of the model.展开更多
Peer reviews of academic articles contain reviewers’ overall impressions and specific comments on the contributed articles,which have a lot of sentimental information.By exploring the fine-grained sentiments in peer ...Peer reviews of academic articles contain reviewers’ overall impressions and specific comments on the contributed articles,which have a lot of sentimental information.By exploring the fine-grained sentiments in peer reviews,we can discover critical aspects of interest to the reviewers.The results can also assist editors and chairmen in making final decisions.However,current research on the aspects of peer reviews is coarse-grained,and mostly focuses on the overall evaluation of the review objects.Therefore,this paper constructs a multi-level fine-grained aspect set of peer reviews for further study.First,this paper uses the multi-level aspect extraction method to extract the aspects from peer reviews of ICLR conference papers.Comparative experiments confirm the validity of the method.Secondly,various Deep Learning models are used to classify aspects’ sentiments automatically,with LCFS-BERT performing best.By calculating the correlation between sentimental scores of the review aspects and the acceptance result of papers,we can find the important aspects affecting acceptance.Finally,this paper predicts acceptance results of papers(accepted/rejected) according to the peer reviews.The optimal acceptance prediction model is XGboost,achieving a Macro_F1 score of 87.43%.展开更多
Many datasets in E-commerce have rich information about items and users who purchase or rate them. This information can enable advanced machine learning algorithms to extract and assign user sentiments to various aspe...Many datasets in E-commerce have rich information about items and users who purchase or rate them. This information can enable advanced machine learning algorithms to extract and assign user sentiments to various aspects of the items thus leading to more sophisticated and justifiable recommendations. However, most Collaborative Filtering (CF) techniques rely mainly on the overall preferences of users toward items only. And there is lack of conceptual and computational framework that enables an understandable aspect-based AI approach to recommending items to users. In this paper, we propose concepts and computational tools that can sharpen the logic of recommendations and that rely on users’ sentiments along various aspects of items. These concepts include: The sentiment of a user towards a specific aspect of a specific item, the emphasis that a given user places on a specific aspect in general, the popularity and controversy of an aspect among groups of users, clusters of users emphasizing a given aspect, clusters of items that are popular among a group of users and so forth. The framework introduced in this study is developed in terms of user emphasis, aspect popularity, aspect controversy, and users and items similarity. Towards this end, we introduce the Aspect-Based Collaborative Filtering Toolbox (ABCFT), where the tools are all developed based on the three-index sentiment tensor with the indices being the user, item, and aspect. The toolbox computes solutions to the questions alluded to above. We illustrate the methodology using a hotel review dataset having around 6000 users, 400 hotels and 6 aspects.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
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
This study examines the connectedness of firm-level online investor sentiment using Dow Jones Industrial Average constituent stocks.Leveraging two proxies of online textual sentiment,namely news media and social media...This study examines the connectedness of firm-level online investor sentiment using Dow Jones Industrial Average constituent stocks.Leveraging two proxies of online textual sentiment,namely news media and social media sentiment,we investigate sentiment connectedness at two levels:frequency interval and asymmetric level.Frequency connectedness dissects connectedness into short-,medium-,and long-term investing horizons,while asymmetric connectedness focuses on the transmission of positive and negative sentiment shocks on news and social media platforms.Our results reveal interesting patterns in which both news and social media sentiments demonstrate consistency in connectedness across the short-,medium-,and long-term.Regarding asymmetric connectedness,we observe that negative news sentiment has a higher connectedness than positive news sentiments.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
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
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
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
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
This study examines a comprehensive set of 30 cross-sectional anomalies in the Chinese A-share market to investigate whether incorporating investor sentiment as conditioning information enhances the explanatory power ...This study examines a comprehensive set of 30 cross-sectional anomalies in the Chinese A-share market to investigate whether incorporating investor sentiment as conditioning information enhances the explanatory power of asset pricing models.Utilizing a long–short portfolio strategy and Fama–MacBeth cross-sectional regression,we find that trading-based anomalies outnumber accounting-based anomalies in the Chinese market.Our results demonstrate that conditional models significantly outperform their unconditional counterparts.Notably,investor sentiment is crucial for capturing the size anomaly when excluding observations from the COVID-19 pandemic period.Additionally,it substantially improves the ability of conditional Fama–French three-factor models to capture individual anomalies and enhances the return–prediction accuracy of conditional CAPMs.We suggest further investigating high-frequency investor sentiment-based conditional models to anticipate stock price fluctuations during extraordinary public health events.展开更多
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funded by the University of Jeddah,Saudi Arabia,under Grant No.(UJ-12-18-DR).
文摘Most consumers read online reviews written by different users before making purchase decisions,where each opinion expresses some sentiment.Therefore,sentiment analysis is currently a hot topic of research.In particular,aspect-based sentiment analysis concerns the exploration of emotions,opinions and facts that are expressed by people,usually in the form of polarity.It is crucial to consider polarity calculations and not simply categorize reviews as positive,negative,or neutral.Currently,the available lexicon-based method accuracy is affected by limited coverage.Several of the available polarity estimation techniques are too general and may not reect the aspect/topic in question if reviews contain a wide range of information about different topics.This paper presents a model for the polarity estimation of customer reviews using aspect-based sentiment analysis(ABSA-PER).ABSA-PER has three major phases:data preprocessing,aspect co-occurrence calculation(CAC)and polarity estimation.A multi-domain sentiment dataset,Twitter dataset,and trust pilot forum dataset(developed by us by dened judgement rules)are used to verify ABSA-PER.Experimental outcomes show that ABSA-PER achieves better accuracy,i.e.,85.7%accuracy for aspect extraction and 86.5%accuracy in terms of polarity estimation,than that of the baseline methods.
文摘People utilize microblogs and other social media platforms to express their thoughts and feelings regarding current events,public products and the latest affairs.People share their thoughts and feelings about various topics,including products,news,blogs,etc.In user reviews and tweets,sentiment analysis is used to discover opinions and feelings.Sentiment polarity is a term used to describe how sentiment is represented.Positive,neutral and negative are all examples of it.This area is still in its infancy and needs several critical upgrades.Slang and hidden emotions can detract from the accuracy of traditional techniques.Existing methods only evaluate the polarity strength of the sentiment words when dividing them into positive and negative categories.Some existing strategies are domain-specific.The proposed model incorporates aspect extraction,association rule mining and the deep learning technique Bidirectional EncoderRepresentations from Transformers(BERT).Aspects are extracted using Part of Speech Tagger and association rulemining is used to associate aspects with opinion words.Later,classification was performed using BER.The proposed approach attained an average of 89.45%accuracy,88.45%precision and 85.98%recall on different datasets of products and Twitter.The results showed that the proposed technique achieved better than state-of-the-art sentiment analysis techniques.
文摘Aspect-based sentiment analysis(ABSA)is a fine-grained process.Its fundamental subtasks are aspect termextraction(ATE)and aspect polarity classification(APC),and these subtasks are dependent and closely related.However,most existing works on Arabic ABSA content separately address them,assume that aspect terms are preidentified,or use a pipeline model.Pipeline solutions design different models for each task,and the output from the ATE model is used as the input to the APC model,which may result in error propagation among different steps because APC is affected by ATE error.These methods are impractical for real-world scenarios where the ATE task is the base task for APC,and its result impacts the accuracy of APC.Thus,in this study,we focused on a multi-task learning model for Arabic ATE and APC in which the model is jointly trained on two subtasks simultaneously in a singlemodel.This paper integrates themulti-task model,namely Local Cotext Foucse-Aspect Term Extraction and Polarity classification(LCF-ATEPC)and Arabic Bidirectional Encoder Representation from Transformers(AraBERT)as a shred layer for Arabic contextual text representation.The LCF-ATEPC model is based on a multi-head selfattention and local context focus mechanism(LCF)to capture the interactive information between an aspect and its context.Moreover,data augmentation techniques are proposed based on state-of-the-art augmentation techniques(word embedding substitution with constraints and contextual embedding(AraBERT))to increase the diversity of the training dataset.This paper examined the effect of data augmentation on the multi-task model for Arabic ABSA.Extensive experiments were conducted on the original and combined datasets(merging the original and augmented datasets).Experimental results demonstrate that the proposed Multi-task model outperformed existing APC techniques.Superior results were obtained by AraBERT and LCF-ATEPC with fusion layer(AR-LCF-ATEPC-Fusion)and the proposed data augmentation word embedding-based method(FastText)on the combined dataset.
基金supported by the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia[Grant No.3418].
文摘Aspect-based sentiment analysis aims to detect and classify the sentiment polarities as negative,positive,or neutral while associating them with their identified aspects from the corresponding context.In this regard,prior methodologies widely utilize either word embedding or tree-based rep-resentations.Meanwhile,the separate use of those deep features such as word embedding and tree-based dependencies has become a significant cause of information loss.Generally,word embedding preserves the syntactic and semantic relations between a couple of terms lying in a sentence.Besides,the tree-based structure conserves the grammatical and logical dependencies of context.In addition,the sentence-oriented word position describes a critical factor that influences the contextual information of a targeted sentence.Therefore,knowledge of the position-oriented information of words in a sentence has been considered significant.In this study,we propose to use word embedding,tree-based representation,and contextual position information in combination to evaluate whether their combination will improve the result’s effectiveness or not.In the meantime,their joint utilization enhances the accurate identification and extraction of targeted aspect terms,which also influences their classification process.In this research paper,we propose a method named Attention Based Multi-Channel Convolutional Neural Net-work(Att-MC-CNN)that jointly utilizes these three deep features such as word embedding with tree-based structure and contextual position informa-tion.These three parameters deliver to Multi-Channel Convolutional Neural Network(MC-CNN)that identifies and extracts the potential terms and classifies their polarities.In addition,these terms have been further filtered with the attention mechanism,which determines the most significant words.The empirical analysis proves the proposed approach’s effectiveness compared to existing techniques when evaluated on standard datasets.The experimental results represent our approach outperforms in the F1 measure with an overall achievement of 94%in identifying aspects and 92%in the task of sentiment classification.
基金This work was supported by the National Natural Science Foundation of China(No.61976247).
文摘The aspect-based sentiment analysis(ABSA)consists of two subtasksaspect term extraction and aspect sentiment prediction.Most methods conduct the ABSA task by handling the subtasks in a pipeline manner,whereby problems in performance and real application emerge.In this study,we propose an end-to-end ABSA model,namely,SSi-LSi,which fuses the syntactic structure information and the lexical semantic information,to address the limitation that existing end-to-end methods do not fully exploit the text information.Through two network branches,the model extracts syntactic structure information and lexical semantic information,which integrates the part of speech,sememes,and context,respectively.Then,on the basis of an attention mechanism,the model further realizes the fusion of the syntactic structure information and the lexical semantic information to obtain higher quality ABSA results,in which way the text information is fully used.Subsequent experiments demonstrate that the SSi-LSi model has certain advantages in using different text information.
基金supported by the National Natural Science Foundation of China(No.61976247)
文摘The aspect-based sentiment analysis(ABSA) consists of two subtasks—aspect term extraction and aspect sentiment prediction. Existing methods deal with both subtasks one by one in a pipeline manner, in which there lies some problems in performance and real application. This study investigates the end-to-end ABSA and proposes a novel multitask multiview network(MTMVN) architecture. Specifically, the architecture takes the unified ABSA as the main task with the two subtasks as auxiliary tasks. Meanwhile, the representation obtained from the branch network of the main task is regarded as the global view, whereas the representations of the two subtasks are considered two local views with different emphases. Through multitask learning, the main task can be facilitated by additional accurate aspect boundary information and sentiment polarity information. By enhancing the correlations between the views under the idea of multiview learning, the representation of the global view can be optimized to improve the overall performance of the model. The experimental results on three benchmark datasets show that the proposed method exceeds the existing pipeline methods and end-to-end methods, proving the superiority of our MTMVN architecture.
基金supported by the National Natural Science Foundation of China under Grant 61976158 and Grant 61673301.
文摘The Aspect-Based Sentiment Analysis(ABSA)task is designed to judge the sentiment polarity of a particular aspect in a review.Recent studies have proved that GCN can capture syntactic and semantic features from dependency graphs generated by dependency trees and semantic graphs generated by Multi-headed self-attention(MHSA).However,these approaches do not highlight the sentiment information associated with aspect in the syntactic and semantic graphs.We propose the Aspect-Guided Multi-Graph Convolutional Networks(AGGCN)for Aspect-Based Sentiment Classification.Specifically,we reconstruct two kinds of graphs,changing the weight of the dependency graph by distance from aspect and improving the semantic graph by Aspect-guided MHSA.For interactive learning of syntax and semantics,we dynamically fuse syntactic and semantic diagrams to generate syntactic-semantic graphs to learn emotional features jointly.In addition,Multi-dropout is added to solve the overftting of AGGCN in training.The experimental results on extensive datasets show that our model AGGCN achieves particularly advanced results and validates the effectiveness of the model.
基金This work is supported by Opening fund of Key Laboratory of Rich-media Knowledge Organization and Service of Digital Publishing Content(No.zd2022-10/02).
文摘Peer reviews of academic articles contain reviewers’ overall impressions and specific comments on the contributed articles,which have a lot of sentimental information.By exploring the fine-grained sentiments in peer reviews,we can discover critical aspects of interest to the reviewers.The results can also assist editors and chairmen in making final decisions.However,current research on the aspects of peer reviews is coarse-grained,and mostly focuses on the overall evaluation of the review objects.Therefore,this paper constructs a multi-level fine-grained aspect set of peer reviews for further study.First,this paper uses the multi-level aspect extraction method to extract the aspects from peer reviews of ICLR conference papers.Comparative experiments confirm the validity of the method.Secondly,various Deep Learning models are used to classify aspects’ sentiments automatically,with LCFS-BERT performing best.By calculating the correlation between sentimental scores of the review aspects and the acceptance result of papers,we can find the important aspects affecting acceptance.Finally,this paper predicts acceptance results of papers(accepted/rejected) according to the peer reviews.The optimal acceptance prediction model is XGboost,achieving a Macro_F1 score of 87.43%.
文摘Many datasets in E-commerce have rich information about items and users who purchase or rate them. This information can enable advanced machine learning algorithms to extract and assign user sentiments to various aspects of the items thus leading to more sophisticated and justifiable recommendations. However, most Collaborative Filtering (CF) techniques rely mainly on the overall preferences of users toward items only. And there is lack of conceptual and computational framework that enables an understandable aspect-based AI approach to recommending items to users. In this paper, we propose concepts and computational tools that can sharpen the logic of recommendations and that rely on users’ sentiments along various aspects of items. These concepts include: The sentiment of a user towards a specific aspect of a specific item, the emphasis that a given user places on a specific aspect in general, the popularity and controversy of an aspect among groups of users, clusters of users emphasizing a given aspect, clusters of items that are popular among a group of users and so forth. The framework introduced in this study is developed in terms of user emphasis, aspect popularity, aspect controversy, and users and items similarity. Towards this end, we introduce the Aspect-Based Collaborative Filtering Toolbox (ABCFT), where the tools are all developed based on the three-index sentiment tensor with the indices being the user, item, and aspect. The toolbox computes solutions to the questions alluded to above. We illustrate the methodology using a hotel review dataset having around 6000 users, 400 hotels and 6 aspects.
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.
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.
文摘This study examines the connectedness of firm-level online investor sentiment using Dow Jones Industrial Average constituent stocks.Leveraging two proxies of online textual sentiment,namely news media and social media sentiment,we investigate sentiment connectedness at two levels:frequency interval and asymmetric level.Frequency connectedness dissects connectedness into short-,medium-,and long-term investing horizons,while asymmetric connectedness focuses on the transmission of positive and negative sentiment shocks on news and social media platforms.Our results reveal interesting patterns in which both news and social media sentiments demonstrate consistency in connectedness across the short-,medium-,and long-term.Regarding asymmetric connectedness,we observe that negative news sentiment has a higher connectedness than positive news sentiments.
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.
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.
基金financially supported by:National Natural Science Foundation of China(72261002,72141304)Youth Foundation for Humanities and Social Sciences Research of the Ministry of Education(22YJC790190)+1 种基金National Key Research and Development Program of China(2022YFC3303304)Student Research Program of Guizhou University of Finance and Economics(2022ZXS).
文摘This study examines a comprehensive set of 30 cross-sectional anomalies in the Chinese A-share market to investigate whether incorporating investor sentiment as conditioning information enhances the explanatory power of asset pricing models.Utilizing a long–short portfolio strategy and Fama–MacBeth cross-sectional regression,we find that trading-based anomalies outnumber accounting-based anomalies in the Chinese market.Our results demonstrate that conditional models significantly outperform their unconditional counterparts.Notably,investor sentiment is crucial for capturing the size anomaly when excluding observations from the COVID-19 pandemic period.Additionally,it substantially improves the ability of conditional Fama–French three-factor models to capture individual anomalies and enhances the return–prediction accuracy of conditional CAPMs.We suggest further investigating high-frequency investor sentiment-based conditional models to anticipate stock price fluctuations during extraordinary public health events.
