A Framework for Real-Time Sentiment Analysis of On-line Micro-texts with Sentiment Drift Detection Method

Ritesh Srivastava, M. P. S Bhatia
2017 International Journal of Software Engineering and Its Applications  
Nowadays, the Twitter data streams have become most trusted on-line micro-texts (tweets) for sensing and monitoring the public opinion for any entity (event, topic or product). The real-time monitoring of Twitter data can be done by performing the realtime sentiment analysis of the tweets. However, unlike to the sentiment analysis of static textual data in which the aggregated sentiment score remains static, the aggregated sentiment score of Twitter data changes very frequently with the time.
more » ... believe that the accurate and timely identification of those change points where the sentiment drifts have occurred can strengthen the real-time decision-making process. This paper offers a framework for the real-time sentiment analysis of on-line micro-texts with sentiment drift detection method. Furthermore, this paper also demonstrates the proposed framework with a case study; that utilized a dataset of tweets related to US election 2016, collected using Twitter API.
doi:10.14257/ijseia.2017.11.1.07 fatcat:diyjokzwvvd4hdiooca64yepqq