Open AccessAn Enhanced Approach for Sentiment Analysis Using Association Rule Mining
Author(s) -
Abhishek Sharma
Publication year - 2021
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2021.39404
Subject(s) - sentiment analysis , association rule learning , computer science , support vector machine , baseline (sea) , association (psychology) , recall , apriori algorithm , precision and recall , social media , artificial intelligence , the internet , data mining , machine learning , natural language processing , world wide web , psychology , cognitive psychology , psychotherapist , oceanography , geology
In today’s world social networking platforms like Facebook, YouTube, twitter etc. are a great source of communication for internet users and loaded with large number of emotions, views and opinions of the people. Sentiment analysis is the study of attitudes, emotions and opinions of the people and is also known as opinion mining. Sentiment analysis is used to find the opinion i.e. negative or positive about a particular subject. In this paper an Enhanced sentiment analysis approach is presented by using the Association rule mining i.e. Apriori and machine learning approach such as Support Vector Machine. The Enhanced approach is compared with the baseline approach, on accuracy, precision, recall, and F1-score measures. The Enhanced approach for sentiment analysis is implemented using the R programming language. The Enhanced approach shows better performance in comparison to the baseline approach. Keyword: Sentiment Analysis, Opinion Mining, Support Vector Machine, Association Rule Mining, Machine Learning