Open AccessA comparative study of classifier algorithms for Twitter’s sentiment based spam detection
Author(s) -
Santosh Kumar,
Kumar Ravi,
Mohammad Rafiqul Haider,
Anil Kumar Dubey
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1022/1/012016
Subject(s) - c4.5 algorithm , naive bayes classifier , computer science , social media , classifier (uml) , logistic regression , sentiment analysis , random forest , feeling , artificial intelligence , machine learning , support vector machine , statistical classification , world wide web , psychology , social psychology
In today’s time Social media is a vital part of everybody’s life. People are expressing their feeling and emotions on social media platforms. Emotions are associated with the daily life experiences of everyone. Twitter is one of the mostly used social media platform. From analysis on social media, we can predict the mental status of users. In this paper a comparative study of user’s posts on Social media has been done. The approach is based on relevant keywords. Sentiment analysis and classification of emotions is done using Bayes Network Classifier, Naive Bayes Classifier, Logistic Regression, Simple Logistic, SMO, J48 pruned tree, and Random Forest. Finally, accuracy of classification is evaluated by different classifiers.