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Automatic Construction and Global Optimization of a Multisentiment Lexicon
Manual annotation of sentiment lexicons costs too much labor and time, and it is also difficult to get accurate quantification of emotional intensity. Besides, the excessive emphasis on one specific field has greatly limited the applicability of domain sentiment lexicons (Wang et al., 2010). This pa...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5153545/ https://www.ncbi.nlm.nih.gov/pubmed/28042290 http://dx.doi.org/10.1155/2016/2093406 |
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author | Yang, Xiaoping Zhang, Zhongxia Zhang, Zhongqiu Mo, Yuting Li, Lianbei Yu, Li Zhu, Peican |
author_facet | Yang, Xiaoping Zhang, Zhongxia Zhang, Zhongqiu Mo, Yuting Li, Lianbei Yu, Li Zhu, Peican |
author_sort | Yang, Xiaoping |
collection | PubMed |
description | Manual annotation of sentiment lexicons costs too much labor and time, and it is also difficult to get accurate quantification of emotional intensity. Besides, the excessive emphasis on one specific field has greatly limited the applicability of domain sentiment lexicons (Wang et al., 2010). This paper implements statistical training for large-scale Chinese corpus through neural network language model and proposes an automatic method of constructing a multidimensional sentiment lexicon based on constraints of coordinate offset. In order to distinguish the sentiment polarities of those words which may express either positive or negative meanings in different contexts, we further present a sentiment disambiguation algorithm to increase the flexibility of our lexicon. Lastly, we present a global optimization framework that provides a unified way to combine several human-annotated resources for learning our 10-dimensional sentiment lexicon SentiRuc. Experiments show the superior performance of SentiRuc lexicon in category labeling test, intensity labeling test, and sentiment classification tasks. It is worth mentioning that, in intensity label test, SentiRuc outperforms the second place by 21 percent. |
format | Online Article Text |
id | pubmed-5153545 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-51535452017-01-01 Automatic Construction and Global Optimization of a Multisentiment Lexicon Yang, Xiaoping Zhang, Zhongxia Zhang, Zhongqiu Mo, Yuting Li, Lianbei Yu, Li Zhu, Peican Comput Intell Neurosci Research Article Manual annotation of sentiment lexicons costs too much labor and time, and it is also difficult to get accurate quantification of emotional intensity. Besides, the excessive emphasis on one specific field has greatly limited the applicability of domain sentiment lexicons (Wang et al., 2010). This paper implements statistical training for large-scale Chinese corpus through neural network language model and proposes an automatic method of constructing a multidimensional sentiment lexicon based on constraints of coordinate offset. In order to distinguish the sentiment polarities of those words which may express either positive or negative meanings in different contexts, we further present a sentiment disambiguation algorithm to increase the flexibility of our lexicon. Lastly, we present a global optimization framework that provides a unified way to combine several human-annotated resources for learning our 10-dimensional sentiment lexicon SentiRuc. Experiments show the superior performance of SentiRuc lexicon in category labeling test, intensity labeling test, and sentiment classification tasks. It is worth mentioning that, in intensity label test, SentiRuc outperforms the second place by 21 percent. Hindawi Publishing Corporation 2016 2016-11-29 /pmc/articles/PMC5153545/ /pubmed/28042290 http://dx.doi.org/10.1155/2016/2093406 Text en Copyright © 2016 Xiaoping Yang et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Yang, Xiaoping Zhang, Zhongxia Zhang, Zhongqiu Mo, Yuting Li, Lianbei Yu, Li Zhu, Peican Automatic Construction and Global Optimization of a Multisentiment Lexicon |
title | Automatic Construction and Global Optimization of a Multisentiment Lexicon |
title_full | Automatic Construction and Global Optimization of a Multisentiment Lexicon |
title_fullStr | Automatic Construction and Global Optimization of a Multisentiment Lexicon |
title_full_unstemmed | Automatic Construction and Global Optimization of a Multisentiment Lexicon |
title_short | Automatic Construction and Global Optimization of a Multisentiment Lexicon |
title_sort | automatic construction and global optimization of a multisentiment lexicon |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5153545/ https://www.ncbi.nlm.nih.gov/pubmed/28042290 http://dx.doi.org/10.1155/2016/2093406 |
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