Topic Extraction using Mixture Model from Online Reviews and Articles
Sana Zawar, Salabat Khan
International Journal of Computational Linguistics Research, Vol. 11, No. 2, 2020.
DOI: https://doi.org/10.6025/jcl/2020/11/2/51-59
Link: http://www.dline.info/jcl/fulltext/v11n2/jclv11n2_1.pdf
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.