Twitter Media Sentiment Analysis to Convert Non-Informative to Informative Using QER

Abstract

The term sentiment analysis deals with sentiment classification based on the review made by the user in a social network. The sentiment classification accuracy is evaluated using various selection methods, especially those that deal with algorithm selection. In this work, every sentiment received through user expressions is ranked in order to categorise sentiments as informative and non-informative. In order to do so, the work focus on Query Expansion Ranking (QER) algorithm that takes user text as input and process for sentiment analysis and finally produces the results as informative or non-informative. The challenge is to convert non-informative into informative using the concepts of classifiers like Bayes multinomial, entropy modelling along with the traditional sentimental analysis algorithm like Support Vector Machine (SVM) and decision trees. The work also addresses simulated annealing along with QER to classify data based on sentiment analysis. As the input volume is very fast, the work also addresses the concept of big data for information retrieval and processing. The result comparison shows that the QER algorithm proved to be versatile when compared with the result of SVM. This work uses Twitter user comments for evaluating sentiment analysis.