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Paper Title
A Study on Feature extraction and summarization using Machine Learning and Opinion Mining
  Bansari Dadhaniya,  Maulik Dhamecha

With the blooming of microblogs on the web. People have begun to express their opinions on a wide variety of topics and another similar services. With increasing popularity of aspect-level sentiment analysis is attributed to the actual aspects or features sentiment analysis is taking the task of sentence or text level aspects. To differencing out with two phase as aspect detection and sentiment polarity classification phase. Aspect extraction is a subtask of sentiment analysis which consists in identifying opinion targets in opinionated text. Also it becomes one of the most active, progressive and popular area in informational retrieval and text mining due to expansion of www. With the rapid development of World Wide Web, electronic web of mouth interaction has made consumers as active participants. The information is very valuable not only for prospective consumers to make decision but also for business in predicting the success and sustainability.

Keywords- Latent Dirichlet Allocation (LDA) algorithm, Sentiment analysis, Implicit feature, explicit feature, opinion mining , Feature extraction, machine learning technique, polarity, aspects words.
Publication Details
Unique Identification Number - IJEDR1701060
Page Number(s) - 384-387
Pubished in - Volume 5 | Issue 1 | March 2017
DOI (Digital Object Identifier) -   
Publisher - IJEDR (ISSN - 2321-9939)
Cite this Article
  Bansari Dadhaniya,  Maulik Dhamecha,   "A Study on Feature extraction and summarization using Machine Learning and Opinion Mining", International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.5, Issue 1, pp.384-387, March 2017, Available at :http://www.ijedr.org/papers/IJEDR1701060.pdf
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