A Comparative Analysis of Different Machine Learning Classification Models for Sentiment Analysis
Aryaman Jain
, Vanshita Tongia
Text Classification, Sentiment Analysis, Machine Learning, Logistic Regression, Random Forest, Multinomial Naïve Bayes, Bernoulli’s Naïve Bayes, Linear Support Vector Classifier, Natural language processing
With the world transforming into a digital age, the generation of textual documents is increasing at an unprecedented rate. This has consequently given rise to the need to organize these documents into proper categories and structure. Text classification, also known as text categorization, is the process of categorizing text into organized groups. In this paper, IMDB dataset of fifty thousand movie reviews is assessed and a classification system is designed. It compares Linear SVC, Bernoulli Naive Bayes, Logistic Regression, Multinomial Naïve Bayes and Random Forest as classification algorithms for applying sentiment analysis and finding the polarity of the given review. These classifiers were tested, analysed and compared with each other and finally a conclusion was obtained. The authors decided to show the comparison based on several parameters such as precision, accuracy, F1-score, recall and confusion matrix. The classifier which gets the highest among all these parameters is termed as the best machine learning algorithm for the text sentiment analysis of IMDB review data set.
"A Comparative Analysis of Different Machine Learning Classification Models for Sentiment Analysis", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.7, Issue 11, page no.270 - 277, November-2022, Available :https://ijsdr.org/papers/IJSDR2211044.pdf
Volume 7
Issue 11,
November-2022
Pages : 270 - 277
Paper Reg. ID: IJSDR_202494
Published Paper Id: IJSDR2211044
Downloads: 000347198
Research Area: Computer Science & Technology
Country: Indore, Madhya Pradesh, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
