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  • × author_ss:"Paltoglou, G."
  • × theme_ss:"Internet"
  • × year_i:[2010 TO 2020}
  1. Thelwall, M.; Buckley, K.; Paltoglou, G.: Sentiment in Twitter events (2011) 0.02 
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    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 pre-existing personal goals more often than eliciting instinctive reactions.
    Date
    22. 1.2011 14:27:06
    Type
    a
  2. Paltoglou, G.: Sentiment-based event detection in Twitter (2016) 0.00 
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    Abstract
    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.
    Type
    a

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