[1]HAO Jie,XIE Jun,SU Jingqiong,et al.An unsupervised approach for sentiment classification based on weighted latent dirichlet allocation[J].CAAI Transactions on Intelligent Systems,2016,11(4):539-545.[doi:10.11992/tis.201606007]
Copy

An unsupervised approach for sentiment classification based on weighted latent dirichlet allocation

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 11 Number of periods: 2016 4 Page number: 539-545 Column: 学术论文—自然语言处理与理解 Public date: 2016-07-25
Title:
An unsupervised approach for sentiment classification based on weighted latent dirichlet allocation
Author(s):
HAO Jie;  XIE Jun;  SU Jingqiong;  XU Xinying;  HAN Xiaoxia
Information Engineering College, Taiyuan University of Technology, Jinzhong 030600, China
Keywords:
sentiment classification; topic and sentiment unification model; topic model; LDA; weighting algorithm
CLC:
TP391
DOI:
10.11992/tis.201606007
Abstract:
The topic and sentiment unification model can efficiently detect topics and emotions for a given corpus. Faced with the low discriminability of topics in sentiment/topic analysis methods, this paper proposes a novel method, the weighted latent dirichlet allocation algorithm (WLDA), which can acquire sentiments and topics without supervision. The model assigns weights to terms during Gibbs sampling by calculating the distance between seed words and terms, then counts the weights of key words to estimate the sentiment orientation of each topic and obtain the emotional distribution throughout documents. This method enhances the impact of words that convey emotional attitudes and obtains more discriminative topics as a consequence. The experiments show that WLDA, compared with the joint sentiment/topic model (JST), not only has a higher iteration sampling speed, but also gives better results for topic extraction and sentiment classification.
References:
[1] AGARWAL B, PORIA S, MITTAL N, et al. Concept-level sentiment analysis with dependency-based semantic parsing:a novel approach[J]. Cognitive computation, 2015, 7(4):487-499.
[2] CAMBRIA E. Affective computing and sentiment analysis[J]. IEEE intelligent systems, 2016, 31(2):102-107.
[3] LIN Chenghua, HE Yulan. Joint sentiment/topic model for sentiment analysis[C]//Proceedings of the 18th ACM Conference on Information and Knowledge Management. Hong Kong, China:ACM, 2009:375-384.
[4] LIN Chenghua, HE Yulan, EVERSON R. A comparative study of Bayesian models for unsupervised sentiment detection[C]//Proceedings of the Fourteenth Conference on Computational Natural Language Learning. Stroudsburg, PA, USA:ACM, 2011:144-152.
[5] TITOV I, MCDONALD R. A joint model of text and aspect ratings for sentiment summarization[C]//Proceedings of Annual Meeting of the Computational Linguistics. Columbus, USA:Association for Computational Linguistics, 2008:308-316.
[6] PAUL M, GIRJU R. A two-dimensional topic-aspect model for discovering multi-faceted topics[C]//Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence. Atlanta, USA:AAAI, 2010:545-550.
[7] MEI Qiaozhu, LING Xu, WONDRA M, et al. Topic sentiment mixture:modeling facets and opinions in weblogs[C]//Proceedings of the 16th International Conference on World Wide Web. North Carolina, USA:ACM, 2010:171-180.
[8] JO Y, OH A H. Aspect and sentiment unification model for online review analysis[C]//Proceedings of the Fourth ACM International Conference on Web Search and Data Mining. Hong Kong, China:ACM, 2011:815-824.
[9] 欧阳继红, 刘燕辉, 李熙铭, 等. 基于LDA的多粒度主题情感混合模型[J]. 电子学报, 2015, 43(9):1875-1880. OUYANG Jihong, LIU Yanhui, LI Ximing, et al. Multi-grain sentiment/topic model based on LDA[J]. Acta electronica sinica, 2015, 43(9):1875-1880.
[10] BLEI D M, NG A Y, JORDAN M I. Latent dirichlet allocation[J]. The journal of machine learning research, 2003, 3:993-1022.
[11] RUBIN T N, CHAMBERS A, SMYTH P, et al. Statistical topic models for multi-label document classification[J]. Machine learning, 2012, 88(1/2):157-208.
[12] ANDRZEJEWSKI D, BUTTLER D. Latent topic feedback for information retrieval[C]//Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Diego, USA:ACM, 2011:600-608.
[13] WALLACH H M. Topic modeling:beyond bag-of-words[C]//Proceedings of the 23rd International Conference on Machine Learning. New York, USA:ACM, 2006:977-984.
[14] CHURCH K W, HANKS P. Word association norms, mutual information, and lexicography[J]. Computational linguistics, 1990, 16(1):22-29.
[15] TURNEY P D, LITTMAN M L. Measuring praise and criticism:inference of semantic orientation from association[J]. ACM transactions on information systems, 2003, 21(4):315-346.
[16] 张小平. 主题模型及其在中医临床诊疗中的应用研究[D]. 北京:北京交通大学, 2011:57-58. ZHANG Xiaoping. Study on topic model and its application to TCM clinical diagnosis and treatment[D]. Beijing:Beijing Jiaotong University, 2011:57-58.
[17] ALSUMAIT L, BARBARá D, GENTLE J, et al. Topic significance ranking of LDA generative models[C]//Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases. Bled, Slovenia:ACM, 2009:67-82.
Similar References:

Memo

-

Last Update: 1900-01-01

Copyright © CAAI Transactions on Intelligent Systems