HappyMeter: An Automated System for Real-Time Twitter Sentiment Analysis

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.

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