MoodLens: an emoticon-based sentiment analysis system for chinese tweets

@inproceedings{Zhao2012MoodLensAE,
  title={MoodLens: an emoticon-based sentiment analysis system for chinese tweets},
  author={Jichang Zhao and Li Dong and Junjie Wu and Ke Xu},
  booktitle={KDD},
  year={2012}
}
Recent years have witnessed the explosive growth of online social media. Weibo, a Twitter-like online social network in China, has attracted more than 300 million users in less than three years, with more than 1000 tweets generated in every second. These tweets not only convey the factual information, but also reflect the emotional states of the authors, which are very important for understanding user behaviors. However, a tweet in Weibo is extremely short and the words it contains evolve… CONTINUE READING
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  • We then collect over 3.5 million labeled tweets as the corpus and train a fast Naive Bayes classifier, with an empirical precision of 64.3%.

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