Abstract
Recently by the development of the Internet and the Web, different types of social media such as web blogs become an immense source of text data. Through the processing of these data, it is possible to discover practical information about different topics, individual’s opinions and a thorough understanding of the society. Therefore, applying models which can automatically extract the subjective information from documents would be efficient and helpful. Topic modeling methods and sentiment analysis are the raised topics in natural language processing and text mining fields. In this paper a new structure for joint sentiment-topic modeling based on a Restricted Boltzmann Machine (RBM) which is a type of neural networks is proposed. By modifying the structure of RBM as well as appending a layer which is analogous to sentiment of text data to it, we propose a generative structure for joint sentiment topic modeling based on neural networks. The proposed method is supervised and trained by the Contrastive Divergence algorithm. The new attached layer in the proposed model is a layer with the multinomial probability distribution which can be used in text data sentiment classification or any other supervised application. The proposed model is compared with existing models in the experiments such as evaluating as a generative model, sentiment classification, information retrieval and the corresponding results demonstrate the efficiency of the method.
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Available at: https://github.com/Masoud-Fatemi/Sentiment-20NG
Available at: https://github.com/Masoud-Fatemi/MRMDS-dataset
References
Blei DM (2012) Probabilistic topic models. Commun ACM 55(4):77–84. https://doi.org/10.1145/2133806.2133826
Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3(Jan):993–1022
Blitzer J, Dredze M, Pereira F, et al (2007) Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification. ACL 7:440–447
Carreira-Perpinan MA, Hinton G (2005) On contrastive divergence learning. 33–40
Hinton G (2010) A practical guide to training restricted boltzmann machines. Momentum 9(1):926
Hinton GE (2002) Training products of experts by minimizing contrastive divergence. Neural Comput 14(8):1771–1800
Hinton GE, Salakhutdinov RR (2009) Replicated softmax: an undirected topic model. In: Advances in neural information processing systems, pp. 1607–1614
Jain TI, Nemade D (2010) Recognizing contextual polarity in phrase-level sentiment analysis. Int J Comput Appl IJCA 7(5):5–11
Jo Y, Oh AH (2011) Aspect and sentiment unification model for online review analysis. pp 815–824. ACM
Lang K (1995) Newsweeder: Learning to filter netnews. In: Proceedings of the 12th international conference on machine learning, pp 331–339
Larochelle H, Lauly S (2012) A neural autoregressive topic model. pp 2708–2716
Larochelle H, Murray I (2011) The neural autoregressive distribution estimator. 29–37
Lewis DD, Yang Y, Rose TG, Li F (2004) Rcv1: A new benchmark collection for text categorization research. J Mach Learn Res 5(Apr):361–397
Li Q, Yang Y (2016) Topic correlation model for cross-modal multimedia information retrieval. Pattern Anal Appl 19:1007–1022
Lin C, He Y, Everson R, Ruger S (2012) Weakly supervised joint sentiment-topic detection from text. IEEE Trans Knowl Data Eng 24(6):1134–1145
Lyang T, Zhiwei N Emerging opinion leaders in crowd unfollow crisis: a case study of mobile brands in twitter. Pattern Analysis and Application
Mohr JW, Bogdanov P (2013) Introduction—topic models: What they are and why they matter. Poetics 41(6):545–569
Pang B, Lee L (2004) A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the ACL, polarity dataset v2.0. http://www.cs.cornell.edu/people/pabo/movie-review-data/. Accessed: 2017-04
Pang B, Lee L, Vaithyanathan S (2002) Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 conference on empirical methods in natural language processing-volume 10, pp 79–86. Association for Computational Linguistics
Pang B, Lee L, et al (2008) Opinion mining and sentiment analysis. Found Trends Inf Retr 2:1–135
Smolensky P (1986) Parallel distributed processing: Explorations in the microstructure of cognition. In: Information processing in dynamical systems: Foundations of harmony theory. http://dl.acm.org/citation.cfm?id=104279.104290, vol 1. MIT Press, Cambridge, pp 194–281
Steyvers M, Griffiths T (2007) Probabilistic topic models. Handbook of latent semantic analysis 427(7):424–440
Woodford O (2013) Notes on contrastive divergence. Department of Engineering Science. University of Oxford, Tech. Rep, Oxford
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Fatemi, M., Safayani, M. Joint sentiment/topic modeling on text data using a boosted restricted Boltzmann Machine. Multimed Tools Appl 78, 20637–20653 (2019). https://doi.org/10.1007/s11042-019-7427-5
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DOI: https://doi.org/10.1007/s11042-019-7427-5