HappyMeter: An Automated System for Real-Time Twitter Sentiment Analysis
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2017, Vol 8, Issue 8
Abstract
The paper presents HappyMeter, an automated system for real-time Twitter sentiment analysis. More than 380 million tweets consisting of nearly 30,000 words, almost 6,000 hashtags and over 5,000 user mentioned have been studied. A sentiment model is used to measure the sentiment level of each term in the contiguous United States. The system automatically mines real-time Twitter data and reveals the changing patterns of the public sentiment over an extended period of time. It is possible to compare the public opinions regarding a subject, hashtag or a Twitter user between different states in the U.S. Users may choose to see the overall sentiment level of a term, as well as its sentiment value on a specific day. Real-time results are delivered continuously and visualized through a web-based graphical user interface.
Authors and Affiliations
Joaquim Perotti Canela, Tina Tian
Keywords
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- EP ID EP259963
- DOI 10.14569/IJACSA.2017.080801
- Views 89
- Downloads 0
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