Open AccessPredicting Risk in Sentiment Analysis using Machine Learning
Author(s) -
Rakhi Gupta,
Nashrah Gowalker,
D. Y. Patil,
Sarang Joshi
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.a9540.109119
Subject(s) - confusion matrix , support vector machine , computer science , naive bayes classifier , decision tree , sentiment analysis , recall , prism , artificial intelligence , precision and recall , machine learning , f1 score , standard deviation , confusion , variation (astronomy) , data mining , statistics , mathematics , psychology , cognitive psychology , physics , psychoanalysis , astrophysics , optics
The purpose of this research is to do risk modeling after a sentiment analysis of Twitter posts based on a particular or certain sentiment with the help of the PRISM model .The model is named PRISM as the results obtained are an amalgamation of seven different attributes used in the research for comparison and tabulation of quantitative scores. These attributes are Accuracy, Precision, Recall, F1-Score, Support, Confusion Matrix, and Tweets. PRISM model can serve the law enforcement agencies in many ways and help them maintain peace, law and order in society as it is a proactive model. The sub-modules which are part of the PRISM model help to give quantitative values to predict the risk level on the sentiment of interest. After analysis of obtained testing results, it is observed that Support Vector Machine gives better results in accuracy, precision, F1-Score, Support and Recall as compared to the other three classifier models i.e. Naive Bayes, Decision Tree, and K nearest neighbor. It is also observed that with an increase or decrease in data, regarding the number of tweets, the fluctuation in performance of SVM is most stable i.e. it shows the least deviation and variation. The other algorithms show a considerable deviation in their performance.