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International Journal of Computational Linguistics Research


Topic Extraction using Mixture Model from Online Reviews and Articles
Sana Zawar, Salabat Khan
COMSATS University, Islambad Attock Campus & Pakistan
Abstract: Topic recognition and tracking is one of the problems in the field of Natural Language Processing (NLP). Many methods and techniques have been introduced currently for information retrieval and extracting topic from user generated reviews. Finding out topics from the group of documents is very valuable for several real-world applications.. LDA (Latent Dirichlet Allocation) and PLSA (Probabilistic Latent semantic Analysis) are used for this purpose. This research aims to extract topic from online reviews and articles using mixture model. Mixture Model is probability based model. One thousand articles from four hundred refereed journals are collected and analyzed textually through topic mining techniques. These articles are collected from six major databases namely IEEE, Science Direct, Wiley, SAGE, Cambridge and Springer. The experimental results of proposed method clearly shows that proposed model yields very promising results.
Keywords: Mixture Model, Clustering, Natural Language Processing Topic Extraction using Mixture Model from Online Reviews and Articles
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[1] Padmaja, C. H ., Narayana, S. L. (2018). Probabilistic Topic Modeling And Its Variants—A Survey, International Journal of Advanced Research in Computer Science, p. 35-43, (May 1).
[2] Jia, Hailong., Fang, Lina. (2016). Design of Web Crawler Based on Improved Hidden Markov Model, International Journal of u-and e-Service, Science and Technology, p. 227-36, (August 30).
[3] Cao, Ziqiang Li., Sujian, Liu., Yang, Li., Wenjie, Heng, J. A Novel Neural Topic Model and Its Supervised Extension, In: Twenty-Ninth AAI Conference on Artificial Intelligence NLP and Machine Learning:
[4] Teja Santosha, D., Sudheer Babua, K., Prasada, S.D.V., Vivekananda, A (2016). Opinion mining of online product reviews from traditional LDA Topic Clusters using Feature Ontology Tree and Sentiwordnet, International Journal of Education and Management Engineering (IJEME), p. 34.
[5] Yang, Guangbing., Wen, Dunwe, Kinshu, Chen, Nian-Shing., Sutinen, Erkki . (2015). A novel contextual topic model for multidocument summarization, Expert Systems with Applications, p. 1340-52, (February 15).
[6] Alvarez-Melis, David., Saveski, Martin (2015). Topic Modeling in Twitter: Aggregating Tweets by Conversations. ICWSM, p. 519-22, (March 31).
[7] Chen, Mo., Yang, Xiao-Ping (2016). Research on model of network information extraction based on improved topic-focused web crawler key technology, Tehnicki vjesnik/Technical Gazette, (July 1).
[8] Chen, Xilun., Candan, Kasim., Sapino, Luisa, Maria. (2018). Incremental Multi-Scale Dynamic Topic Models. In: AAAI, p. 5078-5085.
[9] Qian, Shengsheng., Zhang, Tianzhu., Xu, Changsheng., Shao, Jie. (2016). Multi-modal event topic model for social event analysis, IEEE transactions on multimedia, p. 233-46., (February).
[10] Zhang, Yongjun., Ma, Jialin., Wang, Zijian., Chen, Bolun., Yu, Yongtao. (2018). Collective topical PageRank: a model to evaluate the topic-dependent academic impact of scientific papers. Scientometrics, p. 1345-72. (March 1).
[11] Nguyen, Thien Hai., Shirai, Kiyoaki Velcin, Julien (2015). Sentiment analysis on social media for stock movement prediction, Expert Systems with Applications, p. 9603-11, (December 30).
[12] Cai, Zhiqiang., Hu, Xiangen., Li, Haiying., Graesser, Art (2016). Can Word Probabilities from LDA be Simply Added up to Represent Documents, p. 577-578.
[13] Prabhudesai, Kedar., Mainsah, B., Collins, Leslie., Throckmorton, Chandra., S. (2018). Augmented Latent Dirichlet Allocation (Lda) Topic Model with Gaussian Mixture Topics., In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), p. 2, (April 15).

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