International Journal of Scientific and Research Publications

IJSRP, Volume 4, Issue 6, June 2014 Edition [ISSN 2250-3153]

Joint Sentiment-Topic Detection from Text Document
      Gauri Nivrutti Tuplondhe, Prof. Dr. S. S. Sane
Abstract: Automated tools are used to detect subjective information like attitudes, opinions and feelings. Such process is called as sentiment analysis. The Joint Sentiment-Detection (JST) model is the probabilistic model which is extension of Latent Dirichlet Allocation (LDA) model that detects sentiment and topic simultaneously from text. Supervised approaches to sentiment classification often fail to produce satisfactory results when applied to other domains while the JST model is weakly supervised in nature where supervision only comes from domain independent sentiment lexicon. Thus, makes JST model portable to other domains. The proposed system incorporates a small amount of domain independent prior knowledge which is sentiment lexicon to further improve the sentiment classification accuracy. It also carry out experiments and evaluates the model performance on different datasets.

Reference this Research Paper (copy & paste below code):

Gauri Nivrutti Tuplondhe, Prof. Dr. S. S. Sane (2018); Joint Sentiment-Topic Detection from Text Document; Int J Sci Res Publ 4(6) (ISSN: 2250-3153). http://www.ijsrp.org/research-paper-0614.php?rp=P302849
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