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

作     者：Mian Muhammad Danyal Sarwar Shah Khan Muzammil Khan Muhammad Bilal Ghaffar Bilal Khan Muhammad Arshad 

作者机构：Department of Computer ScienceCity University of Science&TechnologyPeshawar25000Pakistan Department of Computer Science and Software TechnologyUniversity of SwatSwat19200Pakistan Department of Computer Software EngineeringUniversity of Engineering&Technology MardanMardan23200Pakistan Department of Computer ScienceIQRA National UniversitySwat19200Pakistan 

出 版 物：《Journal on Big Data》 (大数据杂志（英文）)

年 卷 期：2023年第5卷第1期

页      面：1-18页

学科分类：1303[艺术学-戏剧与影视学] 13[艺术学] 

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

摘      要：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.