A Bayesian modeling approach to multi-dimensional sentiment distributions prediction

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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.

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Publication date2012
Publication titleProceeding : WISDOM '12 proceedings of the first international workshop on issues of sentiment discovery and opinion mining
EditorsErik Cambria, Zhang Yongzheng, Xia Yunqing, Howard Newton
Place of PublicationNew York, NY (US)
PublisherACM
ISBN (Print)978-1-4503-1543-2
Original languageEnglish
Event1st international workshop on issues of sentiment discovery and opinion mining, WISDOM '12 - Beijing, China

Conference

Conference1st international workshop on issues of sentiment discovery and opinion mining, WISDOM '12
CountryChina
CityBeijing
Period12/08/1212/08/12

DOI

Employable Graduates; Exploitable Research

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