Sentiment in Twitter events

Abstract

The microblogging site Twitter generates a constant stream of communication, some of which concerns events of general interest. An analysis of Twitter may, therefore, give insights into why particular events resonate with the population. This article reports a study of a month of English Twitter posts, assessing whether popular events are typically associated with increases in sentiment strength, as seems intuitively likely. Using the top 30 events, determined by a measure of relative increase in (general) term usage, the results give strong evidence that popular events are normally associated with increases in negative sentiment strength and some evidence that peaks of interest in events have stronger positive sentiment than the time before the peak. It seems that many positive events, such as the Oscars, are capable of generating increased negative sentiment in reaction to them. Nevertheless, the surprisingly small average change in sentiment associated with popular events (typically 1% and only 6% for Tiger Woods' confessions) is consistent with events affording posters opportunities to satisfy preexisting personal goals more often than eliciting instinctive reactions.

Hashtags and followers

“…Looking into users as a group creates a more complete solution when combined with traditional sentiment analysis approaches. (Bifet and Frank 2010;Pak and Paroubek 2010;Wang et al 2011;Thelwall et al 2011;Wang et al 2013;Go et al 2009;Diakopoulos and Shamma 2010).…”

Arabic Event Detection in Social Media

“…They might discuss news, complain about services and express positive or negative sentiment about products [9,10]. In fact, companies manufacturing such products have developed techniques to analyze these posts to get a sense of sentiment about their products [10].…”

“…Here we focus on negative sentiment regarding identifying disruptive events, given that negative sentiment tweets are more likely to be retweeted as shown in [6,8,9]. We use a semantic classifier based on the SentiStrength model in [9].The SentiStrength algorithm is suitable because it is designed for short informal text with abbreviations and slang. Furthermore, it combines a lexicon-based model with a set of additional linguistic rules for spelling correction, negations, booster words (e.g., very), emoticons, and other factors.…”

Making sense of digital traces: An activity theory driven ontological approach

“…Similar approaches have also been extensively applied in Webometrics, a subfield of informetrics (Stock & Weber, 2006), which is concerned with the quantitative analysis of Web based information (such as hyperlinks). Recent work in these fields has been undertaken on blog entries (O'Leary, 2011), Twitter "tweets" (Thelwall, Buckley, & Paltoglou, 2011, Wiki data and most recently YouTube comments (Thelwall, Sud, & Vis, 2012). Despite these growing efforts, work to apply informetric analysis of digital traces and the methods for organizing and providing access to UGC data are still relatively new (Efron, 2011).…”