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Data Mining of Social Media for Analysis of Product Review

by Mamatha Kothapalli, Ershad Sharifahmadian, Liwen Shih
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 156 - Number 12
Year of Publication: 2016
Authors: Mamatha Kothapalli, Ershad Sharifahmadian, Liwen Shih
10.5120/ijca2016912581

Mamatha Kothapalli, Ershad Sharifahmadian, Liwen Shih . Data Mining of Social Media for Analysis of Product Review. International Journal of Computer Applications. 156, 12 ( Dec 2016), 48-51. DOI=10.5120/ijca2016912581

@article{ 10.5120/ijca2016912581,
author = { Mamatha Kothapalli, Ershad Sharifahmadian, Liwen Shih },
title = { Data Mining of Social Media for Analysis of Product Review },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2016 },
volume = { 156 },
number = { 12 },
month = { Dec },
year = { 2016 },
issn = { 0975-8887 },
pages = { 48-51 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume156/number12/26764-2016912581/ },
doi = { 10.5120/ijca2016912581 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:02:27.741191+05:30
%A Mamatha Kothapalli
%A Ershad Sharifahmadian
%A Liwen Shih
%T Data Mining of Social Media for Analysis of Product Review
%J International Journal of Computer Applications
%@ 0975-8887
%V 156
%N 12
%P 48-51
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Social media plays a crucial role in promoting different products. The data collected from the social media helps to improve the quality of products, and helps the customer to select the best product among available products. In this paper, an algorithm is developed based on text mining and TF-IDF (Term Frequency–Inverse Document Frequency) scores. In this paper, it is focused on removing unwanted words such as stop words, stemming words, then the processed data is used for finding sentiment words using NLTK (Natural Language Toolkit). The Stanford POS tagger is also used to tag the words into different categories like positive and negative. The proposed algorithm is implemented using JAVA NetBeans8.2 and achieved desired results. The proposed method can be expanded for the evaluation of different products based on customer reviews provided on the social media.

References
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Index Terms

Computer Science
Information Sciences

Keywords

iPhone Negative Words Positive Words Sentiment Words Side Effect Words Social Media.