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Sentiment Analysis Based on Performance of Linear Support Vector Machine and Multinomial Naïve Bayes Using Movie Reviews with Baseline Techniques

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Journal on Big Data 2023年第1期5卷 1-18页

作者： mian muhammad danyal Sarwar Shah Khan Muzammil Khan muhammad Bilal Ghaffar Bilal Khan muhammad Arshad Department of Computer Science City University of Science&TechnologyPeshawar25000Pakistan Department of Computer Science and Software Technology University of SwatSwat19200Pakistan Department of Computer Software Engineering University of Engineering&Technology MardanMardan23200Pakistan Department of Computer Science IQRA National UniversitySwat19200Pakistan

Movies are the better source of *** year,a great percentage of movies are *** comment on movies in the form of reviews after watching *** 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 *** mining also known as sentiment analysis is the process of extracting subjective information from textual *** mining involves identifying and extracting the opinions of individuals,which can be positive,neutral,or *** task of opinion mining also called sentiment analysis is performed to understand people’s emotions and attitudes in movie *** reviews are an important source of opinion data because they provide insight into the general public’s opinions about a particular *** summary of all reviews can give a general idea about the *** 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 *** applying these classifiers,in pre-processing both datasets are cleaned,duplicate data is dropped and chat words are treated for better *** 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 *** 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.

关键词： Opinion mining machine learning movie reviews IMDB Dataset of 50K reviews Sentiment Polarity Dataset Version 2.0

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