Weakly Supervised Joint Sentiment-Topic Detection from Text

@article{Lin2012WeaklySJ,
  title={Weakly Supervised Joint Sentiment-Topic Detection from Text},
  author={Chenghua Lin and Yulan He and Richard Everson and Stefan M. R{\"u}ger},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  year={2012},
  volume={24},
  pages={1134-1145}
}
Sentiment analysis or opinion mining aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings expressed in text. This paper proposes a novel probabilistic modeling framework called joint sentiment-topic (JST) model based on latent Dirichlet allocation (LDA), which detects sentiment and topic simultaneously from text. A reparameterized version of the JST model called Reverse-JST, obtained by reversing the sequence of sentiment and topic generation in… CONTINUE READING

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