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
Movies reviews provide valuable insights that can help people decide which movies are worth watching and avoid wasting their time on movies they will not enjoy.Movie reviews may contain spoilers or reveal significant ...Movies reviews provide valuable insights that can help people decide which movies are worth watching and avoid wasting their time on movies they will not enjoy.Movie reviews may contain spoilers or reveal significant plot details,which can reduce the enjoyment of the movie for those who have not watched it yet.Additionally,the abundance of reviews may make it difficult for people to read them all at once,classifying all of the movie reviews will help in making this decision without wasting time reading them all.Opinion mining,also called sentiment analysis,is the process of identifying and extracting subjective information from textual data.This study introduces a sentiment analysis approach using advanced deep learning models:Extra-Long Neural Network(XLNet),Long Short-Term Memory(LSTM),and Convolutional Neural Network-Long Short-Term Memory(CNN-LSTM).XLNet understands the context of a word from both sides,which is helpful for capturing complex language patterns.LSTM performs better in modeling long-term dependencies,while CNN-LSTM combines local and global context for robust feature extraction.Deep learning models take advantage of their ability to extract complex linguistic patterns and contextual information from raw text data.We carefully cleaned the IMDb movie reviews dataset with the goal of optimizing the results of models used in the experiment.This involves eliminating unnecessary punctuation,links,hashtags,stop words,and duplicate reviews.Lemmatization is also used for keeping consistent word forms.This cleaned IMDb dataset is evaluated on the proposed model for sentiment analysis in which XLNet performs well achieving an impressive 93.74%accuracy on the IMDb Dataset.The findings highlight the effectiveness of deep learning models in improving sentiment analysis,showing its potential for wider applications in natural language processing.展开更多
为对互联网电影资料库(Internet Movie Database,IMDb)内的影评文字进行情感分析并保证较高的准确率,提出了基于多层感知机(multilayer perceptron,MLP)模型的情感分析算法.通过使用Keras内置的Tokenizer模块建立字典,利用字典将影评文...为对互联网电影资料库(Internet Movie Database,IMDb)内的影评文字进行情感分析并保证较高的准确率,提出了基于多层感知机(multilayer perceptron,MLP)模型的情感分析算法.通过使用Keras内置的Tokenizer模块建立字典,利用字典将影评文字进行预处理后,通过Keras框架构建MLP模型并训练,对训练后模型的准确率进行评估,最后对测试集的影评进行情感分析.测试结果显示,训练后的模型对影评情感分析具有较高的准确率.展开更多
Movies are the better source of entertainment.Every year,a great percentage of movies are released.People comment on movies in the form of reviews after watching them.Since it is difficult to read all of the reviews f...Movies are the better source of entertainment.Every year,a great percentage of movies are released.People comment on movies in the form of reviews after watching them.Since it is difficult to read all of the reviews for a movie,summarizing all of the reviews will help make this decision without wasting time in reading all of the reviews.Opinion mining also known as sentiment analysis is the process of extracting subjective information from textual data.Opinion mining involves identifying and extracting the opinions of individuals,which can be positive,neutral,or negative.The task of opinion mining also called sentiment analysis is performed to understand people’s emotions and attitudes in movie reviews.Movie reviews are an important source of opinion data because they provide insight into the general public’s opinions about a particular movie.The summary of all reviews can give a general idea about the movie.This study compares baseline techniques,Logistic Regression,Random Forest Classifier,Decision Tree,K-Nearest Neighbor,Gradient Boosting Classifier,and Passive Aggressive Classifier with Linear Support Vector Machines and Multinomial Naïve Bayes on the IMDB Dataset of 50K reviews and Sentiment Polarity Dataset Version 2.0.Before applying these classifiers,in pre-processing both datasets are cleaned,duplicate data is dropped and chat words are treated for better results.On the IMDB Dataset of 50K reviews,Linear Support Vector Machines achieve the highest accuracy of 89.48%,and after hyperparameter tuning,the Passive Aggressive Classifier achieves the highest accuracy of 90.27%,while Multinomial Nave Bayes achieves the highest accuracy of 70.69%and 71.04%after hyperparameter tuning on the Sentiment Polarity Dataset Version 2.0.This study highlights the importance of sentiment analysis as a tool for understanding the emotions and attitudes in movie reviews and predicts the performance of a movie based on the average sentiment of all the reviews.展开更多
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.
文摘Movies reviews provide valuable insights that can help people decide which movies are worth watching and avoid wasting their time on movies they will not enjoy.Movie reviews may contain spoilers or reveal significant plot details,which can reduce the enjoyment of the movie for those who have not watched it yet.Additionally,the abundance of reviews may make it difficult for people to read them all at once,classifying all of the movie reviews will help in making this decision without wasting time reading them all.Opinion mining,also called sentiment analysis,is the process of identifying and extracting subjective information from textual data.This study introduces a sentiment analysis approach using advanced deep learning models:Extra-Long Neural Network(XLNet),Long Short-Term Memory(LSTM),and Convolutional Neural Network-Long Short-Term Memory(CNN-LSTM).XLNet understands the context of a word from both sides,which is helpful for capturing complex language patterns.LSTM performs better in modeling long-term dependencies,while CNN-LSTM combines local and global context for robust feature extraction.Deep learning models take advantage of their ability to extract complex linguistic patterns and contextual information from raw text data.We carefully cleaned the IMDb movie reviews dataset with the goal of optimizing the results of models used in the experiment.This involves eliminating unnecessary punctuation,links,hashtags,stop words,and duplicate reviews.Lemmatization is also used for keeping consistent word forms.This cleaned IMDb dataset is evaluated on the proposed model for sentiment analysis in which XLNet performs well achieving an impressive 93.74%accuracy on the IMDb Dataset.The findings highlight the effectiveness of deep learning models in improving sentiment analysis,showing its potential for wider applications in natural language processing.
文摘为对互联网电影资料库(Internet Movie Database,IMDb)内的影评文字进行情感分析并保证较高的准确率,提出了基于多层感知机(multilayer perceptron,MLP)模型的情感分析算法.通过使用Keras内置的Tokenizer模块建立字典,利用字典将影评文字进行预处理后,通过Keras框架构建MLP模型并训练,对训练后模型的准确率进行评估,最后对测试集的影评进行情感分析.测试结果显示,训练后的模型对影评情感分析具有较高的准确率.
文摘Movies are the better source of entertainment.Every year,a great percentage of movies are released.People comment on movies in the form of reviews after watching them.Since it is difficult to read all of the reviews for a movie,summarizing all of the reviews will help make this decision without wasting time in reading all of the reviews.Opinion mining also known as sentiment analysis is the process of extracting subjective information from textual data.Opinion mining involves identifying and extracting the opinions of individuals,which can be positive,neutral,or negative.The task of opinion mining also called sentiment analysis is performed to understand people’s emotions and attitudes in movie reviews.Movie reviews are an important source of opinion data because they provide insight into the general public’s opinions about a particular movie.The summary of all reviews can give a general idea about the movie.This study compares baseline techniques,Logistic Regression,Random Forest Classifier,Decision Tree,K-Nearest Neighbor,Gradient Boosting Classifier,and Passive Aggressive Classifier with Linear Support Vector Machines and Multinomial Naïve Bayes on the IMDB Dataset of 50K reviews and Sentiment Polarity Dataset Version 2.0.Before applying these classifiers,in pre-processing both datasets are cleaned,duplicate data is dropped and chat words are treated for better results.On the IMDB Dataset of 50K reviews,Linear Support Vector Machines achieve the highest accuracy of 89.48%,and after hyperparameter tuning,the Passive Aggressive Classifier achieves the highest accuracy of 90.27%,while Multinomial Nave Bayes achieves the highest accuracy of 70.69%and 71.04%after hyperparameter tuning on the Sentiment Polarity Dataset Version 2.0.This study highlights the importance of sentiment analysis as a tool for understanding the emotions and attitudes in movie reviews and predicts the performance of a movie based on the average sentiment of all the reviews.
