NDLI: A Bayesian modeling approach to multi-dimensional sentiment distributions prediction

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A Bayesian modeling approach to multi-dimensional sentiment distributions prediction

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A Bayesian modeling approach to multi-dimensional sentiment distributions prediction

Content Provider	ACM Digital Library
Author	He, Yulan
Abstract	Sentiment analysis has long focused on binary classification of text as either positive or negative. There has been few work on mapping sentiments or emotions into multiple dimensions. This paper studies a Bayesian modeling approach to multi-class sentiment classification and multidimensional sentiment distributions prediction. It proposes effective mechanisms to incorporate supervised information such as labeled feature constraints and document-level sentiment distributions derived from the training data into model learning. We have evaluated our approach on the datasets collected from the confession section of the Experience Project website where people share their life experiences and personal stories. Our results show that using the latent representation of the training documents derived from our approach as features to build a maximum entropy classifier outperforms other approaches on multi-class sentiment classification. In the more difficult task of multi-dimensional sentiment distributions prediction, our approach gives superior performance compared to a few competitive baselines.
Starting Page	1
Ending Page	8
Page Count	8
File Format	PDF
ISBN	9781450315432
DOI	10.1145/2346676.2346677
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-08-12
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Joint sentiment/topic model (jst) Sentiment analysis Latent dirichlet allocation (lda) Opinion mining
Content Type	Text
Resource Type	Article

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