Classification of sentiment analysis using machine learning

Abstract

An application of the computational linguistics is identified as Natural Language Processing or NLP. With the help of NLP, the text can be analyzed. Any person’s opinion through which the emotions, attitude and thoughts can be communicated is known as sentiment. The surveys of human beings towards specific occasions, brands, items or organization can be known through sentiment analysis. Every one of the assumptions can be ordered into three unique categories, they are positive, negative and neutral. Twitter, being the utmost mainstream microblogging webpage, is utilized to gather the information for perform analysis. Tweepy is utilized to extract the source of information from Twitter. Python language is utilized in this exploration to execute the classification algorithm on the gathered information. In sentiment analysis, two steps namely feature extraction and classifications are implemented. The features are extracted using N-gram modeling technique. The opinion is classified among positive, negative and neutral by utilizing a supervised machine learning algorithm. In this research work, SVM (Support Vector Machine) and KNN (K-Nearest Neighbor) classification models are utilized. Also, we have shown both comparison for sentiment analysis.