期刊名称：International Journal of Advances in Engineering and Management
电子版ISSN：2395-5252
出版年度：2021
卷号：3
期号：7
页码：3285-3290
DOI：10.35629/5252-030731423146
语种：English
出版社：IJAEM JOURNAL
摘要：Sentiment analysis is defined as the process of mining of data, view, review or sentence to predict the emotion of the sentence through natural language processing (NLP). The sentiment analysis involves classification of text into different phases like “Positive”, “Negative” or “Neutral”. It analyses the data and labels the „better‟ and „worse‟ sentiment as positive and negative respectively. Thus, in the past years, the World Wide Web (WWW) has become a huge source of raw data generated custom or user. Using social media, e-commerce website, movies reviews such as Facebook, twitter, Amazon, Flipkart etc. user share their views, feelings in a convenient way. In the web, millions of people express their views in their daily interaction, either in the social media or in e-commerce which can be their sentiments and opinions about particular thing. These growing raw data are an extremely high source of information for any kind of decision making process either positive or negative. To analyse such huge data automatically, the field of sentiment analysis has turned up. Therefore, to find polarity or sentiment of customers there is a demand for automated data analysis techniques. In this paper, a sentimental analysis of product review data is performed using machine learning algorithms. The algorithms used in this experiment include, Support Vector Machine (SVM) and Naive Bayes classifiers. A comparative study of their performences is also presented.
