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A Hybrid Approach for Big Data Analysis of Cricket Fan Sentiments in Twitter
Samariya D, Matariya A, Raval D, , Raj E.D, Vekariya B.
Published in Springer Singapore
2016
Volume: 408
   
Pages: 503 - 512
Abstract
Twitter has become one of the most widely used social networks, and its popularity is increasing day by day as the number of tweets grows exponentially each day in the order of millions. The twitter data is used widely for personal, academic, and business purpose. In this paper, we collected real-time tweets from Indian Cricket Team fans during the eight matches of ICC Cricket World Cup (CWC) 2015 (here 8 matches means the total number of games India played in CWC) using the social media twitter. We performed sentiment analysis on the tweets to test emotions of Indian Cricket Lovers. The analysis is based on the fact that the emotions of fans change frequently with each event such that when home country is batting and scoring runs, they will be happy, and for every loss of wicket they will be sad. When the team is bowling, they will be sad for ‘six’ and happy for Wickets. So when Fans are happy, they react with positive tweets and accordingly when they are sad they react with negative tweets. We analyzed that, when India is batting, people use fear, anger, nervousness, and tension which are the most frequently used negative words and words like awesome, happy, and love are the most used positive words. All emotions are entirely dependent on team India’s performance. All negative emotions are increased when opponent team hit runs or when they achieve HIGH SCORE” and is decreased when the Indian team hit runs or when they take wickets’. This paper uses tweets and captures emotions for big data analytics and analyzes emotional quotient. © Springer Science+Business Media Singapore 2016.
About the journal
JournalData powered by TypesetAdvances in Intelligent Systems and Computing Proceedings of International Conference on ICT for Sustainable Development
PublisherData powered by TypesetSpringer Singapore
ISSN2194-5357
Open AccessNo