Georgios Kalamatianos
Avi Arampatzis
ABSTRACT
The rapid growth of social media has rendered opinion and sentiment mining an important area of research with a wide range of applications. We focus on the Greek language and the microblogging platform "Twitter", investigating methods for extracting sentiment of individual tweets as well population sentiment for different subjects (hashtags). The proposed methods are based on a sentiment lexicon. We compare several approaches for measuring the intensity of "Anger", "Disgust", "Fear", "Happiness", "Sadness", and "Surprise". To evaluate the effectiveness of our methods, we develop a benchmark dataset of tweets, manually rated by two humans. Our automated sentiment results seem promising and correlate to real user sentiment. Finally, we examine the variation of sentiment intensity over time for selected hashtags, and associate it with real-world events.
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Index Terms
- Sentiment analysis of greek tweets and hashtags using a sentiment lexicon
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