Social Media Mining for Opinion Analysis
Saloni Bindra1, Priyanka Karmarkar2, Abhishek Kumar Verma3, Laxmi Grover4

1Saloni Bindra, Department of Computer Engineering, University of Mumbai, Mumbai (Maharashtra), India.
2Priyanka Karmarkar, Department of Computer Engineering, University of Mumbai, Mumbai (Maharashtra), India.
3Abhishek Kumar, Department of Computer Engineering, Sir M. Visvesvaraya Institute of Technology, Bangaluru (Karnataka), India.
4Verma Laxmi Grover, Department of Computer Engineering, Dr. B. R. Ambedkar National Institute of Technology Jalandhar (Punjab), India

Manuscript received on 15 October 2015 | Revised Manuscript received on 25 October 2015 | Manuscript Published on 30 October 2015 | PP: 73-77 | Volume-5 Issue-1, October 2015 | Retrieval Number: A4302105115/15©BEIESP
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (

Abstract: Here we describe a method which involves determining the sentiment of a review about Banks by extracting the phrases with a noun-adjective relationship, Identifying if the noun is present in the domain specific Ontology tree and then determining the polarity of the adjective, aggregating the polarity. The results so obtained are thus summarized and then categorized by characteristic feature pertaining to the Bank. This reduces the human efforts to go through them and a result specific to a particular Bank; sub-categorized by Peculiar features of it with polarity alongside each individual characteristic. Thus the fruits of the reviews are gained even without reading them.
Keywords: Sentiment, Polarity, Domain Ontology, Opinion Mining.

Scope of the Article: Data Mining