Aspect-based sentence segmentation for sentiment summarization

Jingbo Zhu; Muhua Zhu; Huizhen Wang; Benjamin K. Tsou

doi:10.1145/1651461.1651474

Aspect-based sentence segmentation for sentiment summarization

Jingbo Zhu
, Muhua Zhu
, Huizhen Wang
, Benjamin K. Tsou

City University of Hong Kong

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

24 Citations (Scopus)

Abstract

Aspect-based sentiment summarization systems generally use sentences associated with relevant aspects extracted from the reviews as the basis for summarization. However, in real reviews, a single sentence often exhibits several aspects for opinions. This paper proposes a two-stage segmentation model to address the challenge of identifying multiple single-aspect and single-polarity units in one sentence, namely aspect-based sentence segmentation. Our model deals with both issues of aspect change and polarity change occurring in the input sentence. Experiments on restaurant reviews show that our model outperforms state-of-the-art linear text segmentation methods. Copyright 2009 ACM.

Original language	English
Title of host publication	1st Int. CIKM Workshop on Topic-Sentiment Analysis for Mass Opinion Measurement, TSA'09, Co-located with the 18th ACM International Conference on Information and Knowledge Management, CIKM 2009
Pages	65-72
DOIs	https://doi.org/10.1145/1651461.1651474
Publication status	Published - 2009
Event	1st International CIKM Workshop on Topic-Sentiment Analysis for Mass Opinion Measurement, TSA'09, Co-located with the 18th ACM International Conference on Information and Knowledge Management, CIKM 2009 - Hong Kong, China Duration: 2 Nov 2009 → 6 Nov 2009

Publication series

Name	International Conference on Information and Knowledge Management, Proceedings

Conference

Conference	1st International CIKM Workshop on Topic-Sentiment Analysis for Mass Opinion Measurement, TSA'09, Co-located with the 18th ACM International Conference on Information and Knowledge Management, CIKM 2009
Place	China
City	Hong Kong
Period	2/11/09 → 6/11/09

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

Funding

This work was supported in part by the National Science Foundation of China (60873091).

Research Keywords

Aspect-based sentiment summarization
Sentence segmentation
Text segmentation

Access Output

10.1145/1651461.1651474

Other Access Options

Check CityUHK Library

Permanent Link

Right click to copy link

Cite this

Zhu, J., Zhu, M., Wang, H., & Tsou, B. K. (2009). Aspect-based sentence segmentation for sentiment summarization. In 1st Int. CIKM Workshop on Topic-Sentiment Analysis for Mass Opinion Measurement, TSA'09, Co-located with the 18th ACM International Conference on Information and Knowledge Management, CIKM 2009 (pp. 65-72). (International Conference on Information and Knowledge Management, Proceedings). https://doi.org/10.1145/1651461.1651474

Zhu, Jingbo ; Zhu, Muhua ; Wang, Huizhen et al. / Aspect-based sentence segmentation for sentiment summarization. 1st Int. CIKM Workshop on Topic-Sentiment Analysis for Mass Opinion Measurement, TSA'09, Co-located with the 18th ACM International Conference on Information and Knowledge Management, CIKM 2009. 2009. pp. 65-72 (International Conference on Information and Knowledge Management, Proceedings).

@inproceedings{5631209da7b24a62bd83a3a7ba078d55,

title = "Aspect-based sentence segmentation for sentiment summarization",

abstract = "Aspect-based sentiment summarization systems generally use sentences associated with relevant aspects extracted from the reviews as the basis for summarization. However, in real reviews, a single sentence often exhibits several aspects for opinions. This paper proposes a two-stage segmentation model to address the challenge of identifying multiple single-aspect and single-polarity units in one sentence, namely aspect-based sentence segmentation. Our model deals with both issues of aspect change and polarity change occurring in the input sentence. Experiments on restaurant reviews show that our model outperforms state-of-the-art linear text segmentation methods. Copyright 2009 ACM.",

keywords = "Aspect-based sentiment summarization, Sentence segmentation, Text segmentation",

author = "Jingbo Zhu and Muhua Zhu and Huizhen Wang and Tsou, \{Benjamin K.\}",

note = "Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].; 1st International CIKM Workshop on Topic-Sentiment Analysis for Mass Opinion Measurement, TSA'09, Co-located with the 18th ACM International Conference on Information and Knowledge Management, CIKM 2009 ; Conference date: 02-11-2009 Through 06-11-2009",

year = "2009",

doi = "10.1145/1651461.1651474",

language = "English",

isbn = "9781605588056",

series = "International Conference on Information and Knowledge Management, Proceedings",

pages = "65--72",

booktitle = "1st Int. CIKM Workshop on Topic-Sentiment Analysis for Mass Opinion Measurement, TSA'09, Co-located with the 18th ACM International Conference on Information and Knowledge Management, CIKM 2009",

}

Zhu, J, Zhu, M, Wang, H & Tsou, BK 2009, Aspect-based sentence segmentation for sentiment summarization. in 1st Int. CIKM Workshop on Topic-Sentiment Analysis for Mass Opinion Measurement, TSA'09, Co-located with the 18th ACM International Conference on Information and Knowledge Management, CIKM 2009. International Conference on Information and Knowledge Management, Proceedings, pp. 65-72, 1st International CIKM Workshop on Topic-Sentiment Analysis for Mass Opinion Measurement, TSA'09, Co-located with the 18th ACM International Conference on Information and Knowledge Management, CIKM 2009, Hong Kong, China, 2/11/09. https://doi.org/10.1145/1651461.1651474

Aspect-based sentence segmentation for sentiment summarization. / Zhu, Jingbo; Zhu, Muhua; Wang, Huizhen et al.
1st Int. CIKM Workshop on Topic-Sentiment Analysis for Mass Opinion Measurement, TSA'09, Co-located with the 18th ACM International Conference on Information and Knowledge Management, CIKM 2009. 2009. p. 65-72 (International Conference on Information and Knowledge Management, Proceedings).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Aspect-based sentence segmentation for sentiment summarization

AU - Zhu, Jingbo

AU - Zhu, Muhua

AU - Wang, Huizhen

AU - Tsou, Benjamin K.

N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

PY - 2009

Y1 - 2009

N2 - Aspect-based sentiment summarization systems generally use sentences associated with relevant aspects extracted from the reviews as the basis for summarization. However, in real reviews, a single sentence often exhibits several aspects for opinions. This paper proposes a two-stage segmentation model to address the challenge of identifying multiple single-aspect and single-polarity units in one sentence, namely aspect-based sentence segmentation. Our model deals with both issues of aspect change and polarity change occurring in the input sentence. Experiments on restaurant reviews show that our model outperforms state-of-the-art linear text segmentation methods. Copyright 2009 ACM.

AB - Aspect-based sentiment summarization systems generally use sentences associated with relevant aspects extracted from the reviews as the basis for summarization. However, in real reviews, a single sentence often exhibits several aspects for opinions. This paper proposes a two-stage segmentation model to address the challenge of identifying multiple single-aspect and single-polarity units in one sentence, namely aspect-based sentence segmentation. Our model deals with both issues of aspect change and polarity change occurring in the input sentence. Experiments on restaurant reviews show that our model outperforms state-of-the-art linear text segmentation methods. Copyright 2009 ACM.

KW - Aspect-based sentiment summarization

KW - Sentence segmentation

KW - Text segmentation

UR - https://www.scopus.com/pages/publications/74049089276

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-74049089276&origin=recordpage

U2 - 10.1145/1651461.1651474

DO - 10.1145/1651461.1651474

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781605588056

T3 - International Conference on Information and Knowledge Management, Proceedings

SP - 65

EP - 72

BT - 1st Int. CIKM Workshop on Topic-Sentiment Analysis for Mass Opinion Measurement, TSA'09, Co-located with the 18th ACM International Conference on Information and Knowledge Management, CIKM 2009

T2 - 1st International CIKM Workshop on Topic-Sentiment Analysis for Mass Opinion Measurement, TSA'09, Co-located with the 18th ACM International Conference on Information and Knowledge Management, CIKM 2009

Y2 - 2 November 2009 through 6 November 2009

ER -

Zhu J, Zhu M, Wang H, Tsou BK. Aspect-based sentence segmentation for sentiment summarization. In 1st Int. CIKM Workshop on Topic-Sentiment Analysis for Mass Opinion Measurement, TSA'09, Co-located with the 18th ACM International Conference on Information and Knowledge Management, CIKM 2009. 2009. p. 65-72. (International Conference on Information and Knowledge Management, Proceedings). doi: 10.1145/1651461.1651474

Aspect-based sentence segmentation for sentiment summarization

Abstract

Publication series

Conference

Bibliographical note

Funding

Research Keywords

Access Output

Other Access Options

Permanent Link

Other Files and Links

Fingerprint

Cite this