Sentiment analysis, visualization and classification of summarized news articles: A novel approach

Abstract

Due to advancement in technology, enormous amount of data is generated every day. One of the main challenges of large amount of data is user overloaded with huge volume of data. Hence effective methods are highly required to help user to comprehend large amount of data. This research work proposes effective methods to extract and represent the data. The summarization is applicable to obtain a brief overview of the text and sentiment analysis can obtain emotions expressed in the text computationally. The combined text summarization and sentiment analysis is proposed on BBC news articles. A pronoun replacement based text summarization method is developed and VADER sentiment analyzer is used to determine sentiment information. The 3-D visualization schemes have been provided to represent the sentiment information. The sentiment analysis and classification are performed on original BBC news articles as well as on summarized articles using classifiers, such as Logistic Regression, Random Forest and Adaboost. On original news articles highest classification rate of 84.93%, using summarization of ratio 25%, 50% and 75% highest classification rates of 78.73%, 83.06% and 83.23%, respectively are observed.