Event-driven Sentiment Drift Analysis in Text Streams: An Application in a Soccer Match

Social media has been a data source for various applications, given its characteristic of working as a social sensor. Many applications in several areas, such as brand reputation and online opinion monitoring, use this valuable resource to understand the users of services and products. This paper describes an application in the soccer domain, considering data collected from a social media textual data stream. The goal is to detect possible sentiment drifts related to actual events in a soccer match. This task is challenging as we resort to short texts made available during a short time (match length). We evaluated four drift detectors using four metrics: false alarms, delay (considering the number of posts), delay, and missing drifts. Our results show that ADWIN had a stable performance in sentiment drift detection compared to other methods in timely detecting the flagged drifts, raising a small number of false alarms. Given the drifts detected, we used Incremental Word-Vectors to monitor words of interest and check their relatedness to actual events in the match. We empirically assert that the closest words trace back to the sentiment drift generator events.