Sentiment-Based Event Detection in Twitter

The main focus of this article is to examine whether sentiment analysis can be successfully used for event detection, that is, detecting significant events that occur in the world. Most solutions to this problem are typically based on increases or spikes in frequency of terms in social media. In our case, we explore whether sudden changes in the positivity or negativity that keywords are typically associated with can be exploited for this purpose. A data set that contains several million Twitter messages over a 1-month time span is presented and experimental results demonstrate that sentiment analysis can be successfully utilized for this purpose. Further experiments study the sensitivity of both frequency- or sentiment-based solutions to a number of parameters. Concretely, we show that the number of tweets that are used for event detection is an important factor, while the number of days used to extract token frequency or sentiment averages is not. Lastly, we present results focusing on detecting local events and conclude that all approaches are dependant on the level of coverage that such events receive in social media.