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.

Joaquim Perotti Canela and Tina Tian, “HappyMeter: An Automated System for Real-Time Twitter Sentiment Analysis” International Journal of Advanced Computer Science and Applications(IJACSA), 8(8), 2017. http://dx.doi.org/10.14569/IJACSA.2017.080801

@article{Canela2017,
title = {HappyMeter: An Automated System for Real-Time Twitter Sentiment Analysis},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2017.080801},
url = {http://dx.doi.org/10.14569/IJACSA.2017.080801},
year = {2017},
publisher = {The Science and Information Organization},
volume = {8},
number = {8},
author = {Joaquim Perotti Canela and Tina Tian}
}