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The rapid growth of social media has rendered opinion and sentiment mining an important area of research with a wide range of applications. This paper aims to focus on the…
The rapid growth of social media has rendered opinion and sentiment mining an important area of research with a wide range of applications. This paper aims to focus on the Greek language and the microblogging platform Twitter, investigating methods for extracting emotion of individual tweets as well as population emotion for different subjects (hashtags).
The authors propose and investigate the use of emotion lexicon-based methods as a mean of extracting emotion/sentiment information from social media. The authors compare several approaches for measuring the intensity of six emotions: anger, disgust, fear, happiness, sadness and surprise. To evaluate the effectiveness of the methods, the authors develop a benchmark dataset of tweets, manually rated by two humans.
Development of a new sentiment lexicon for use in Web applications. The authors then assess the performance of the methods with the new lexicon and find improved results.
Automated emotion results of research seem promising and correlate to real user emotion. At this point, the authors make some interesting observations about the lexicon-based approach which lead to the need for a new, better, emotion lexicon.
The authors examine the variation of emotion intensity over time for selected hashtags and associate it with real-world events.
The originality in this research is the development of a training set of tweets, manually annotated by two independent raters. The authors “transfer” the sentiment information of these annotated tweets, in a meaningful way, to the set of words that appear in them.