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Real-Time Streaming Data Analysis Using a Three-Way Classification Method for Sentimental Analysis

Real-Time Streaming Data Analysis Using a Three-Way Classification Method for Sentimental Analysis

Srinidhi Hiriyannaiah, G.M. Siddesh, K.G. Srinivasa
Copyright: © 2020 |Pages: 14
ISBN13: 9781799824602|ISBN10: 1799824608|EISBN13: 9781799824619
DOI: 10.4018/978-1-7998-2460-2.ch069
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MLA

Hiriyannaiah, Srinidhi, et al. "Real-Time Streaming Data Analysis Using a Three-Way Classification Method for Sentimental Analysis." Cognitive Analytics: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2020, pp. 1377-1390. https://doi.org/10.4018/978-1-7998-2460-2.ch069

APA

Hiriyannaiah, S., Siddesh, G., & Srinivasa, K. (2020). Real-Time Streaming Data Analysis Using a Three-Way Classification Method for Sentimental Analysis. In I. Management Association (Ed.), Cognitive Analytics: Concepts, Methodologies, Tools, and Applications (pp. 1377-1390). IGI Global. https://doi.org/10.4018/978-1-7998-2460-2.ch069

Chicago

Hiriyannaiah, Srinidhi, G.M. Siddesh, and K.G. Srinivasa. "Real-Time Streaming Data Analysis Using a Three-Way Classification Method for Sentimental Analysis." In Cognitive Analytics: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 1377-1390. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-2460-2.ch069

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Abstract

This article describes how recent advances in computing have led to an increase in the generation of data in fields such as social media, medical, power and others. With the rapid increase in internet users, social media has given power for sentiment analysis or opinion mining. It is a highly challenging task for storing, querying and analyzing such types of data. This article aims at providing a solution to store, query and analyze streaming data using Apache Kafka as the platform and twitter data as an example for analysis. A three-way classification method is proposed for sentimental analysis of twitter data that combines both the approaches for knowledge-based and machine-learning using three stages namely emotion classification, word classification and sentiment classification. The hybrid three-way classification approach was evaluated using a sample of five query strings on twitter and compared with existing emotion classifier, polarity classifier and Naïve Bayes classifier for sentimental analysis. The accuracy of the results of the proposed approach is superior when compared to existing approaches.

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