A localization toolkit for sentic net

Yunqing Xia; Xiaoyu Li; Erik Cambria; Amir Hussain

doi:10.1109/ICDMW.2014.179

A localization toolkit for sentic net

Yunqing Xia
, Xiaoyu Li
, Erik Cambria
, Amir Hussain

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 Scopus citations

Abstract

Sentic Net is a popular resource for concept-level sentiment analysis. Because Sentic Net was created specifically for opinion mining in English language, however, its localization can be very laborious. In this work, a toolkit for creating non-English versions of Sentic Net in a time-and cost-effective way is proposed. This is achieved by exploiting online facilities such as Web dictionaries and translation engines. The challenging issues are three: firstly, when a Web lexicon is used, one sentiment concept in English can usually be mapped to multiple concepts in the local language. In this work, we develop a concept disambiguation algorithm to discover context within texts in the target language. Secondly, the polarity of some concepts in the local language may be different from the counterpart in English, which is referred to as language-dependent sentiment concepts. An algorithm is developed to detect sentiment conflict using sentiment annotation corpora in the two languages. Lastly, some sentiment concepts are not included in the local language after dictionary consulting and online translation. In this work, we develop a tool to extract these concepts from sentiment dictionary in the local language. Our practice and evaluation in constructing the Chinese version of Sentic Net indicate that the proposed algorithms represent an effective toolkit for localizing Sentic Net.

Original language	English
Title of host publication	Proceedings - 14th IEEE International Conference on Data Mining Workshops, ICDMW 2014
Editors	Zhi-Hua Zhou, Wei Wang, Ravi Kumar, Hannu Toivonen, Jian Pei, Joshua Zhexue Huang, Xindong Wu
Publisher	IEEE Computer Society
Pages	403-408
Number of pages	6
Edition	January
ISBN (Electronic)	9781479942749
DOIs	https://doi.org/10.1109/ICDMW.2014.179
State	Published - 26 Jan 2015
Externally published	Yes
Event	14th IEEE International Conference on Data Mining Workshops, ICDMW 2014 - Shenzhen, China Duration: 14 Dec 2014 → …

Publication series

Name	IEEE International Conference on Data Mining Workshops, ICDMW
Number	January
Volume	2015-January
ISSN (Print)	2375-9232
ISSN (Electronic)	2375-9259

Conference

Conference	14th IEEE International Conference on Data Mining Workshops, ICDMW 2014
Country/Territory	China
City	Shenzhen
Period	14/12/14 → …

Bibliographical note

Publisher Copyright:
© 2014 IEEE.

Keywords

Sentic Net
Sentiment analysis
common sense
localization

ASJC Scopus subject areas

Software
Computer Science Applications

Access to Document

10.1109/ICDMW.2014.179

Cite this

Xia, Y., Li, X., Cambria, E., & Hussain, A. (2015). A localization toolkit for sentic net. In Z.-H. Zhou, W. Wang, R. Kumar, H. Toivonen, J. Pei, J. Zhexue Huang, & X. Wu (Eds.), Proceedings - 14th IEEE International Conference on Data Mining Workshops, ICDMW 2014 (January ed., pp. 403-408). Article 7022624 (IEEE International Conference on Data Mining Workshops, ICDMW; Vol. 2015-January, No. January). IEEE Computer Society. https://doi.org/10.1109/ICDMW.2014.179

Xia, Yunqing ; Li, Xiaoyu ; Cambria, Erik et al. / A localization toolkit for sentic net. Proceedings - 14th IEEE International Conference on Data Mining Workshops, ICDMW 2014. editor / Zhi-Hua Zhou ; Wei Wang ; Ravi Kumar ; Hannu Toivonen ; Jian Pei ; Joshua Zhexue Huang ; Xindong Wu. January. ed. IEEE Computer Society, 2015. pp. 403-408 (IEEE International Conference on Data Mining Workshops, ICDMW; January).

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title = "A localization toolkit for sentic net",

abstract = "Sentic Net is a popular resource for concept-level sentiment analysis. Because Sentic Net was created specifically for opinion mining in English language, however, its localization can be very laborious. In this work, a toolkit for creating non-English versions of Sentic Net in a time-and cost-effective way is proposed. This is achieved by exploiting online facilities such as Web dictionaries and translation engines. The challenging issues are three: firstly, when a Web lexicon is used, one sentiment concept in English can usually be mapped to multiple concepts in the local language. In this work, we develop a concept disambiguation algorithm to discover context within texts in the target language. Secondly, the polarity of some concepts in the local language may be different from the counterpart in English, which is referred to as language-dependent sentiment concepts. An algorithm is developed to detect sentiment conflict using sentiment annotation corpora in the two languages. Lastly, some sentiment concepts are not included in the local language after dictionary consulting and online translation. In this work, we develop a tool to extract these concepts from sentiment dictionary in the local language. Our practice and evaluation in constructing the Chinese version of Sentic Net indicate that the proposed algorithms represent an effective toolkit for localizing Sentic Net.",

keywords = "Sentic Net, Sentiment analysis, common sense, localization",

author = "Yunqing Xia and Xiaoyu Li and Erik Cambria and Amir Hussain",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 14th IEEE International Conference on Data Mining Workshops, ICDMW 2014 ; Conference date: 14-12-2014",

year = "2015",

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Xia, Y, Li, X, Cambria, E & Hussain, A 2015, A localization toolkit for sentic net. in Z-H Zhou, W Wang, R Kumar, H Toivonen, J Pei, J Zhexue Huang & X Wu (eds), Proceedings - 14th IEEE International Conference on Data Mining Workshops, ICDMW 2014. January edn, 7022624, IEEE International Conference on Data Mining Workshops, ICDMW, no. January, vol. 2015-January, IEEE Computer Society, pp. 403-408, 14th IEEE International Conference on Data Mining Workshops, ICDMW 2014, Shenzhen, China, 14/12/14. https://doi.org/10.1109/ICDMW.2014.179

A localization toolkit for sentic net. / Xia, Yunqing; Li, Xiaoyu; Cambria, Erik et al.
Proceedings - 14th IEEE International Conference on Data Mining Workshops, ICDMW 2014. ed. / Zhi-Hua Zhou; Wei Wang; Ravi Kumar; Hannu Toivonen; Jian Pei; Joshua Zhexue Huang; Xindong Wu. January. ed. IEEE Computer Society, 2015. p. 403-408 7022624 (IEEE International Conference on Data Mining Workshops, ICDMW; Vol. 2015-January, No. January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A localization toolkit for sentic net

AU - Xia, Yunqing

AU - Li, Xiaoyu

AU - Cambria, Erik

AU - Hussain, Amir

PY - 2015/1/26

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N2 - Sentic Net is a popular resource for concept-level sentiment analysis. Because Sentic Net was created specifically for opinion mining in English language, however, its localization can be very laborious. In this work, a toolkit for creating non-English versions of Sentic Net in a time-and cost-effective way is proposed. This is achieved by exploiting online facilities such as Web dictionaries and translation engines. The challenging issues are three: firstly, when a Web lexicon is used, one sentiment concept in English can usually be mapped to multiple concepts in the local language. In this work, we develop a concept disambiguation algorithm to discover context within texts in the target language. Secondly, the polarity of some concepts in the local language may be different from the counterpart in English, which is referred to as language-dependent sentiment concepts. An algorithm is developed to detect sentiment conflict using sentiment annotation corpora in the two languages. Lastly, some sentiment concepts are not included in the local language after dictionary consulting and online translation. In this work, we develop a tool to extract these concepts from sentiment dictionary in the local language. Our practice and evaluation in constructing the Chinese version of Sentic Net indicate that the proposed algorithms represent an effective toolkit for localizing Sentic Net.

AB - Sentic Net is a popular resource for concept-level sentiment analysis. Because Sentic Net was created specifically for opinion mining in English language, however, its localization can be very laborious. In this work, a toolkit for creating non-English versions of Sentic Net in a time-and cost-effective way is proposed. This is achieved by exploiting online facilities such as Web dictionaries and translation engines. The challenging issues are three: firstly, when a Web lexicon is used, one sentiment concept in English can usually be mapped to multiple concepts in the local language. In this work, we develop a concept disambiguation algorithm to discover context within texts in the target language. Secondly, the polarity of some concepts in the local language may be different from the counterpart in English, which is referred to as language-dependent sentiment concepts. An algorithm is developed to detect sentiment conflict using sentiment annotation corpora in the two languages. Lastly, some sentiment concepts are not included in the local language after dictionary consulting and online translation. In this work, we develop a tool to extract these concepts from sentiment dictionary in the local language. Our practice and evaluation in constructing the Chinese version of Sentic Net indicate that the proposed algorithms represent an effective toolkit for localizing Sentic Net.

KW - Sentic Net

KW - Sentiment analysis

KW - common sense

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DO - 10.1109/ICDMW.2014.179

M3 - Conference contribution

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T3 - IEEE International Conference on Data Mining Workshops, ICDMW

SP - 403

EP - 408

BT - Proceedings - 14th IEEE International Conference on Data Mining Workshops, ICDMW 2014

A2 - Zhou, Zhi-Hua

A2 - Wang, Wei

A2 - Kumar, Ravi

A2 - Toivonen, Hannu

A2 - Pei, Jian

A2 - Zhexue Huang, Joshua

A2 - Wu, Xindong

PB - IEEE Computer Society

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ER -

Xia Y, Li X, Cambria E, Hussain A. A localization toolkit for sentic net. In Zhou ZH, Wang W, Kumar R, Toivonen H, Pei J, Zhexue Huang J, Wu X, editors, Proceedings - 14th IEEE International Conference on Data Mining Workshops, ICDMW 2014. January ed. IEEE Computer Society. 2015. p. 403-408. 7022624. (IEEE International Conference on Data Mining Workshops, ICDMW; January). doi: 10.1109/ICDMW.2014.179

A localization toolkit for sentic net

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this