A Bayesian modeling approach to multi-dimensional sentiment distributions prediction

Yulan He

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.
Original languageEnglish
Title of host publicationProceeding : 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
DOIs
Publication statusPublished - 2012
Event1st international workshop on issues of sentiment discovery and opinion mining, WISDOM '12 - Beijing, China
Duration: 12 Aug 201212 Aug 2012

Conference

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

Fingerprint

Websites
Classifiers
Entropy

Cite this

He, Y. (2012). A Bayesian modeling approach to multi-dimensional sentiment distributions prediction. In E. Cambria, Z. Yongzheng, X. Yunqing, & H. Newton (Eds.), Proceeding : WISDOM '12 proceedings of the first international workshop on issues of sentiment discovery and opinion mining New York, NY (US) : ACM. https://doi.org/10.1145/2346676.2346677
He, Yulan. / A Bayesian modeling approach to multi-dimensional sentiment distributions prediction. Proceeding : WISDOM '12 proceedings of the first international workshop on issues of sentiment discovery and opinion mining. editor / Erik Cambria ; Zhang Yongzheng ; Xia Yunqing ; Howard Newton. New York, NY (US) : ACM, 2012.
@inproceedings{c123d48450994edab7b1709c0361df34,
title = "A Bayesian modeling approach to multi-dimensional sentiment distributions prediction",
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.",
author = "Yulan He",
year = "2012",
doi = "10.1145/2346676.2346677",
language = "English",
isbn = "978-1-4503-1543-2",
editor = "Erik Cambria and Zhang Yongzheng and Xia Yunqing and Howard Newton",
booktitle = "Proceeding : WISDOM '12 proceedings of the first international workshop on issues of sentiment discovery and opinion mining",
publisher = "ACM",
address = "United States",

}

He, Y 2012, A Bayesian modeling approach to multi-dimensional sentiment distributions prediction. in E Cambria, Z Yongzheng, X Yunqing & H Newton (eds), Proceeding : WISDOM '12 proceedings of the first international workshop on issues of sentiment discovery and opinion mining. ACM, New York, NY (US) , 1st international workshop on issues of sentiment discovery and opinion mining, WISDOM '12, Beijing, China, 12/08/12. https://doi.org/10.1145/2346676.2346677

A Bayesian modeling approach to multi-dimensional sentiment distributions prediction. / He, Yulan.

Proceeding : WISDOM '12 proceedings of the first international workshop on issues of sentiment discovery and opinion mining. ed. / Erik Cambria; Zhang Yongzheng; Xia Yunqing; Howard Newton. New York, NY (US) : ACM, 2012.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - A Bayesian modeling approach to multi-dimensional sentiment distributions prediction

AU - He, Yulan

PY - 2012

Y1 - 2012

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84866015784&partnerID=8YFLogxK

UR - http://dl.acm.org/citation.cfm?id=2346677

U2 - 10.1145/2346676.2346677

DO - 10.1145/2346676.2346677

M3 - Conference contribution

AN - SCOPUS:84866015784

SN - 978-1-4503-1543-2

BT - Proceeding : WISDOM '12 proceedings of the first international workshop on issues of sentiment discovery and opinion mining

A2 - Cambria, Erik

A2 - Yongzheng, Zhang

A2 - Yunqing, Xia

A2 - Newton, Howard

PB - ACM

CY - New York, NY (US)

ER -

He Y. A Bayesian modeling approach to multi-dimensional sentiment distributions prediction. In Cambria E, Yongzheng Z, Yunqing X, Newton H, editors, Proceeding : WISDOM '12 proceedings of the first international workshop on issues of sentiment discovery and opinion mining. New York, NY (US) : ACM. 2012 https://doi.org/10.1145/2346676.2346677