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.
Highlights
Huge amount of data is being generated every day in the form of social media data, various blogs, web sites, Wikipedia, online newspapers, etc
In the recent years we are witnessing significant amount of data being generated in numerous forms such as social media, web blogs, web sites, Wikipedia, news articles and many more
Due this the end user is overloaded with data and there is a greater need for effective methods to help user to absorb the data
Summary
Huge amount of data is being generated every day in the form of social media data, various blogs, web sites, Wikipedia, online newspapers, etc. In this research work the summarizing technique is proposed in which, pronouns are replaced at first with proper nouns and the frequency of words are computed, thereby enhancing the frequency information related to proper nouns to generate an improved version of the text summary. With motivation to help user to comprehend large volume of data, in this research work, summarization on news articles is performed carried out sentiment analysis and representation. The extractive text summarization method is developed based on [21] to produce a brief overview of news articles.
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