Wednesday, 01 Dec, 2021

+91-9899775880

011-28082485

011-47044510

Predicting Polarity Using Sentimental Analysis

International Journal of Emerging Trends in Science and Technology

Volume 6 Issue 1

Published: 2020
Author(s) Name: V. Mani, Deepika S., Harishkumar R. S., Ranisnekha M. and Mukesh P. S. | Author(s) Affiliation: Assistant Prof., Department of CSE, M. Kumarasamy College of Engineering, Tamil Nadu, India.
Locked Subscribed Available for All

Abstract

Sentiment analysis is a study of people’s sentiments, opinions, attitudes, emotions in a written language or text. Rapid Growth in the field of sentiment analysis and explore to find the sentiment on various social media platforms using techniques of machine learning with analyzing sentiment, and also helps in the analysis of polarity or subjectivity. The most widely used social media site is Twitter, where people share their thoughts in the form of tweets and hence it becomes the major data sources of sentimental analysis. Recently the more used social media platform such as Twitter. Their people express their thoughts and opinion as a tweet representation. It is the major data resources of analyzing the sentiments. Sentiments are classified to different group like positive, negative or neutral. Such analysis process helps to differentiate the sentiments also classifying them into different groups comes under prediction of sentiment. Very first we pre-process the dataset, feature extraction in which meaningful insights are extracted from the dataset, then extracted features are applied for classification model using machine learning Random Forest algorithm, Regression algorithm, etc. But with the advancement of the python language and to reduce the code complexity we have analyzed the polarity using the python packages, API and Algorithm which are available. This model proved to be highly effective and accurate on the analysis of feelings. At last the trained classification model are tested in order to check the performance it is measured by accuracy.

Keywords: Classification, Random forest, Regression, Sentimental analysis.

View PDF

Refund policy | Privacy policy | Copyright Information | Contact Us | Feedback © Publishingindia.com, All rights reserved