research-article

Locally contextualized smoothing of language models for sentiment sentence retrieval

Authors:

Takayoshi Okamoto,

Koji EguchiAuthors Info & Claims

TSA '09: Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion

Pages 73 - 80

https://doi.org/10.1145/1651461.1651475

Published: 06 November 2009 Publication History

Abstract

Recently, a number of documents are published on the web. One of the crucial techniques to access to such information is sentiment sentence retrieval. It is very useful to retrieve positive or negative opinions to a specific topic at sentence level. Considering the property that sentiment polarities are often locally consistent in a document, we focus on using local context information for retrieving sentiment-bearing sentences. For this objective, we propose a new smoothing method, extending Dirichlet prior smoothing, to improve effectiveness of the retrieval. We demonstrate through experiments that our proposed smoothing method achieves statistically significant improvements in sentiment sentence retrieval, compared with a conventional smoothing method without local context.

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Cited By

Dietz LWang ZHuston SCroft WHe QIyengar ANejdl WPei JRastogi R(2013)Retrieving opinions from discussion forumsProceedings of the 22nd ACM international conference on Information & Knowledge Management10.1145/2505515.2507861(1225-1228)Online publication date: 27-Oct-2013
https://dl.acm.org/doi/10.1145/2505515.2507861
Blanco RZaragoza HCrestani FMarchand-Maillet SChen HEfthimiadis ESavoy J(2010)Finding support sentences for entitiesProceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval10.1145/1835449.1835507(339-346)Online publication date: 19-Jul-2010
https://dl.acm.org/doi/10.1145/1835449.1835507

Index Terms

Locally contextualized smoothing of language models for sentiment sentence retrieval
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
2. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

Index terms have been assigned to the content through auto-classification.

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cover image ACM Conferences

TSA '09: Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion

November 2009

94 pages

ISBN:9781605588056

DOI:10.1145/1651461

General Chairs:
Maojin Jiang
Illinois Institute of Technology, USA
,
Bei Yu
Syracuse University, USA
,
Program Chair:
Bei Yu
Syracuse University, USA

Copyright © 2009 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 06 November 2009

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November 6, 2009

Hong Kong, China

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Cited By

Dietz LWang ZHuston SCroft WHe QIyengar ANejdl WPei JRastogi R(2013)Retrieving opinions from discussion forumsProceedings of the 22nd ACM international conference on Information & Knowledge Management10.1145/2505515.2507861(1225-1228)Online publication date: 27-Oct-2013
https://dl.acm.org/doi/10.1145/2505515.2507861
Blanco RZaragoza HCrestani FMarchand-Maillet SChen HEfthimiadis ESavoy J(2010)Finding support sentences for entitiesProceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval10.1145/1835449.1835507(339-346)Online publication date: 19-Jul-2010
https://dl.acm.org/doi/10.1145/1835449.1835507

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