A unified latent variable model for contrastive opinion mining

doi:10.1007/s11704-018-7073-5

Front. Comput. Sci.

2020, Vol. 14

Issue (2) : 404-416 https://doi.org/10.1007/s11704-018-7073-5

RESEARCH ARTICLE

A unified latent variable model for contrastive opinion mining

Ebuka IBEKE, Chenghua LIN(

), Adam WYNER, Mohamad Hardyman BARAWI

Department of Computing Science, University of Aberdeen, Aberdeen AB24 3FX, UK

Download: PDF(444 KB)
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

There are large and growing textual corpora in which people express contrastive opinions about the same topic. This has led to an increasing number of studies about contrastive opinion mining. However, there are several notable issues with the existing studies. They mostly focus on mining contrastive opinions from multiple data collections, which need to be separated into their respective collections beforehand. In addition, existing models are opaque in terms of the relationship between topics that are extracted and the sentences in the corpus which express the topics; this opacity does not help us understand the opinions expressed in the corpus. Finally, contrastive opinion is mostly analysed qualitatively rather than quantitatively. This paper addresses these matters and proposes a novel unified latent variable model (contraLDA), which: mines contrastive opinions from both single and multiple data collections, extracts the sentences that project the contrastive opinion, and measures the strength of opinion contrastiveness towards the extracted topics. Experimental results show the effectiveness of our model in mining contrasted opinions, which outperformed our baselines in extracting coherent and informative sentiment-bearing topics. We further show the accuracy of our model in classifying topics and sentiments of textual data, and we compared our results to five strong baselines.

Keywords contrastive opinion mining sentiment analysis topic modelling

Corresponding Author(s): Chenghua LIN

Just Accepted Date: 07 December 2017 Online First Date: 17 September 2019 Issue Date: 16 October 2019

Cite this article:

Ebuka IBEKE,Chenghua LIN,Adam WYNER, et al. A unified latent variable model for contrastive opinion mining[J]. Front. Comput. Sci., 2020, 14(2): 404-416.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-018-7073-5
https://academic.hep.com.cn/fcs/EN/Y2020/V14/I2/404

1	Y Fang, L Si, N Somasundaram, Z Yu. Mining contrastive opinions on political texts using cross-perspective topic model. In: Proceedings of the International Conference on Web Search and Data Mining. 2012, 63–72 https://doi.org/10.1145/2124295.2124306
2	A Trabelsi, O R Zaïane. A joint topic viewpoint model for contention analysis. In: Proceedings of the International Conference on Applications of Natural Language to Data Bases/Information Systems. 2014, 114–125 https://doi.org/10.1007/978-3-319-07983-7_16
3	K Lerman, R McDonald. Contrastive summarization: an experiment with consumer reviews. In: Proceedings of the HLT Annual Conference of the North American Chapter of the Association for Computational Linguistics. 2009, 113–116 https://doi.org/10.3115/1620853.1620886
4	M Paul, R Girju. Cross-cultural analysis of blogs and forums with mixed-collection topic models. In: Proceedings of the ACL Conference on Empirical Methods in Natural Language Processing. 2009, 1408–1417 https://doi.org/10.3115/1699648.1699687
5	M F Elahi, P Monachesi. An examination of cross-cultural similarities and differences from social media data with respect to language use. In: Proceedings of the 8th International Conference on Language Resources and Evaluation. 2012, 4080–4086
6	E Ibeke, C Lin, A Wyner, M H Barawi. Extracting and understanding contrastive opinion through topic relevant sentences. In: Proceedings of the 8th International Joint Conference on Natural Language Processing. 2017, 395–400
7	M H Barawi, C Lin, A Siddharthan. Automatically labelling sentimentbearing topics with descriptive sentence labels. In: Proceedings of the 22nd International Conference on Natural Language and Information Systems. 2017, 299–312 https://doi.org/10.1007/978-3-319-59569-6_38
8	C Zhai, A Velivelli, B Yu. A cross-collection mixture model for comparative text mining. In: Proceedings of the International Conference on Knowledge Discovery and Data Mining. 2004, 743–748 https://doi.org/10.1145/1014052.1014150
9	A Ahmed, E P Xing. Staying informed: supervised and semisupervised multi-view topical analysis of ideological perspective. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. 2010, 1140–1150
10	A Mukherjee, B Liu. Mining contentions from discussions and debates. In: Proceedings of the International Conference on Knowledge Discovery and Data Mining. 2012, 841–849 https://doi.org/10.1145/2339530.2339664
11	T Thonet, G Cabanac, M Boughanem, K Pinel-Sauvagnat. VODUM: a topic model unifying viewpoint, topic and opinion discovery. In: Proceedings of the European Conference on Information Retrieval. 2016, 533–545 https://doi.org/10.1007/978-3-319-30671-1_39
12	M J Paul, C Zhai, R Girju. Summarizing contrastive viewpoints in opinionated text. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. 2010, 66–76
13	M J Paul, R Girju. A two-dimensional topic-aspect model for discovering multi-faceted topics. Urbana, 2010, 51(61801): 36
14	J Guo, Y Lu, T Mori, C Blake. Expert-guided contrastive opinion summarization for controversial issues. In: Proceedings of the 24th International Conference on World Wide Web Companion. 2015, 1105–1110 https://doi.org/10.1145/2740908.2743038
15	Z Ren, M de Rijke. Summarizing contrastive themes via hierarchical non-parametric processes. In: Proceedings of the 38th SIGIR International Conference on Research and Development in Information Retrieval. 2015, 93–102 https://doi.org/10.1145/2766462.2767713
16	L He, W Li, H Zhuge. Exploring differential topic models for comparative summarization of scientific papers. In: Proceedings of COLING International Conference on Computational Linguistics. 2016, 1028–1038
17	H Nakasaki, M Kawaba, T Utsuro, T Fukuhara. Mining crosslingual/cross-cultural differences in concerns and opinions in blogs. In: Proceedings of the 22nd International Conferee on Computer Processing of Oriental Languages. Language Technology for the Knowledgebased Economy. 2009, 213–224 https://doi.org/10.1007/978-3-642-00831-3_20
18	H Guo, H Zhu, Z Guo, X Zhang, Z Su. OpinionIt: a text mining system for cross-lingual opinion analysis. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management. 2010, 1199–1208 https://doi.org/10.1145/1871437.1871589
19	E D Gutiérrez, E Shutova, P Lichtenstein, G deMelo, L Gilardi. Detecting cross-cultural differences using a multilingual topic model. Journal of Transactions of the Association for Computational Linguistics, 2016, 4: 47–60 https://doi.org/10.1162/tacl_a_00082
20	C Lin, Y He. Joint sentiment/topic model for sentiment analysis. In: Proceedings of the 18th ACM conference on Information and Knowledge Management. 2009, 375–384 https://doi.org/10.1145/1645953.1646003
21	C Lin, Y He, R Everson, S Ruger. Weakly supervised joint sentimenttopic detection from text. Journal of IEEE Transactions on Knowledge and Data Engineering, 2012, 24(6): 1134–1145 https://doi.org/10.1109/TKDE.2011.48
22	H Wallach, D Mimno, A McCallum. Rethinking LDA: why priors matter. Advances in Neural Information Processing Systems, 2009, 22: 1973–1981
23	T Minka. Estimating a dirichlet distribution. Technical Report, 2003
24	E Ibeke, C Lin, C Coe, A Wyner, D Liu, M H Barawi, N F A Yusof. A curated corpus for sentiment-topic analysis. In: Proceedings of the LREC 2016 Workshop on Emotion and Sentiment Analysis. 2016, 32–39
25	J Chang, S Gerrish, C Wang, J L Boyd-Graber, L Jordan, D M Blei. Reading tea leaves: how humans interpret topic models. In: Proceedings of the 22nd Conference on Neural Information Processing Systems. 2009, 288–296
26	D Mimno, H M Wallach, E Talley, M Leenders, A McCallum. Optimizing semantic coherence in topic models. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. 2011, 262–272
27	G Bouma. Normalized (pointwise) mutual information in collocation extraction. In: Proceedings of the German Society for Computational Linguistics and Language Technology. 2009, 31–40
28	D Newman, J H Lau, K Grieser, T Baldwin. Automatic evaluation of topic coherence. In: Proceedings of the HLT Annual Conference of the North American Chapter of the Association for Computational Linguistics. 2010, 100–108
29	N Aletras, M Stevenson. Evaluating topic coherence using distributional semantics. In: Proceedings of the 10th International Conference on Computational Semantics. 2013, 13–22
30	M Steyvers, T Griffiths. Probabilistic topic models. Handbook of Latent Semantic Analysis, 2007, 427(7): 424–440
31	A B Cano, Y He, R Xu. Automatic labelling of topic models learned from Twitter by summarisation. In: Proceedings of the 52nd Annual Meeting of Association for Computational Linguists. 2014, 618–624
32	D Ramage, D Hall, R Nallapati, C D Manning. Labeled LDA: a supervised topic model for credit attribution in multi-labeled corpora. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. 2009, 248–256 https://doi.org/10.3115/1699510.1699543
33	J D Mcauliffe, D M Blei. Supervised topic models. In: Proceedings of the 21st Annual Conference on Neural Information Processing Systems. 2008, 121–128
34	Y Kim. Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. 2014, 1746–1751 https://doi.org/10.3115/v1/D14-1181
35	G Ganu, A Marian, N Elhadad. URSA-user review structure analysis: understanding online reviewing trends. DCS Technical Report: Citeseer, 2010

[1]

Article highlights

Download

[1]	Yang-Yen OU, Ta-Wen KUAN, Anand PAUL, Jhing-Fa WANG, An-Chao TSAI. Spoken dialog summarization system with HAPPINESS/SUFFERING factor recognition[J]. Front. Comput. Sci., 2017, 11(3): 429-443.
[2]	Rongrong JI,Donglin CAO,Yiyi ZHOU,Fuhai CHEN. Survey of visual sentiment prediction for social media analysis[J]. Front. Comput. Sci., 2016, 10(4): 602-611.
[3]	Wenge RONG,Baolin PENG,Yuanxin OUYANG,Chao LI,Zhang XIONG. Structural information aware deep semi-supervised recurrent neural network for sentiment analysis[J]. Front. Comput. Sci., 2015, 9(2): 171-184.

Viewed

Full text

Abstract

Cited

Shared

Discussed