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Reseach Article

Analysis of Techniques of Sentiment and Topic Detection

by Supriya Paul, Sachin N. Deshmukh
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 116 - Number 14
Year of Publication: 2015
Authors: Supriya Paul, Sachin N. Deshmukh

Supriya Paul, Sachin N. Deshmukh . Analysis of Techniques of Sentiment and Topic Detection. International Journal of Computer Applications. 116, 14 ( April 2015), 1-4. DOI=10.5120/20401-2709

@article{ 10.5120/20401-2709,
author = { Supriya Paul, Sachin N. Deshmukh },
title = { Analysis of Techniques of Sentiment and Topic Detection },
journal = { International Journal of Computer Applications },
issue_date = { April 2015 },
volume = { 116 },
number = { 14 },
month = { April },
year = { 2015 },
issn = { 0975-8887 },
pages = { 1-4 },
numpages = {9},
url = { },
doi = { 10.5120/20401-2709 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T22:57:05.059593+05:30
%A Supriya Paul
%A Sachin N. Deshmukh
%T Analysis of Techniques of Sentiment and Topic Detection
%J International Journal of Computer Applications
%@ 0975-8887
%V 116
%N 14
%P 1-4
%D 2015
%I Foundation of Computer Science (FCS), NY, USA

User generated media like blogs, discussion forums is used as a tool by people to communicate their experiences with others. Presence of such huge data on Internet demands proper means to generate processed information from the unstructured data. What users need is more than mere sentiments. They need to know public opinion or experience of various aspects of a product like how is camera quality of the phone or energy efficiency of electronic products. For meeting the high demands of users, various techniques have been proposed till date. In this paper we are evaluating, all these techniques that discover topic along with sentiment associated with it. Many models were proposed to incorporate sentiment analysis with topic model to find aspects of a product and users sentiment about the aspect. Results of these models can be beneficial for various industries as well as users.

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Index Terms

Computer Science
Information Sciences


Aspect detection sentiment analysis topic modeling opinion mining latent Dirichlet allocation (LDA).