Research Article
Twitter Sentiment Analysis on E-commerce Websites in India
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 127 - Issue 18 |
| Published: October 2015 |
| Authors: Devang Jhaveri, Aunsh Chaudhari, Lakshmi Kurup |
10.5120/ijca2015906730
|
Devang Jhaveri, Aunsh Chaudhari, Lakshmi Kurup . Twitter Sentiment Analysis on E-commerce Websites in India. International Journal of Computer Applications. 127, 18 (October 2015), 14-18. DOI=10.5120/ijca2015906730
@article{ 10.5120/ijca2015906730,
author = { Devang Jhaveri,Aunsh Chaudhari,Lakshmi Kurup },
title = { Twitter Sentiment Analysis on E-commerce Websites in India },
journal = { International Journal of Computer Applications },
year = { 2015 },
volume = { 127 },
number = { 18 },
pages = { 14-18 },
doi = { 10.5120/ijca2015906730 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2015
%A Devang Jhaveri
%A Aunsh Chaudhari
%A Lakshmi Kurup
%T Twitter Sentiment Analysis on E-commerce Websites in India%T
%J International Journal of Computer Applications
%V 127
%N 18
%P 14-18
%R 10.5120/ijca2015906730
%I Foundation of Computer Science (FCS), NY, USA
Abstract
In today’s era, social media has become a valuable source of information, where people express their opinions. Analysis of such opinion-related data can provide productive insights. When these opinions are relevant to a company, accurate analysis can provide them with information like product quality, influencers affecting other customer decisions, early feedback on newly launched products, company news, trends and also knowledge about their competitors. Hence, harnessing and extracting insights from these sentiments is necessary for these companies to implement effective marketing strategies and better customer service. Carrying the same notion forward, we decided to extract sentiments from Twitter relevant to two e-commerce giants in India, Flipkart and Snapdeal. In this paper, various lexicon based approaches are applied and their accuracy is investigated.
References
- Michelle Annett and Grzegorz Kondrak. A Comparison of Sentiment Analysis Techniques: Polarizing Movie Blogs in CiteSeerx at Proceedings of the Twenty-First Canadian Conference on Artificial Intelligence, 2008.
- Swathi Chandrasekar, Emmanuel Charon, Alexandre Ginet “CS229 Project Predicting The US Presidential Election using Twitter data” in CS229 Machine Learning course at Stanford University, 2012.
- Xing Fang and Justin Zhan “Sentiment analysis using product review data” in Journal of Big Data by Springer, 2015.
- Andrea Esuli and Fabrizio Sebastiani “SentiWordNet: A publicly Available Lexical Resource for Opinion Mining”, 2006.
- Jon Tatum and Jonhn Travis Sanchez “Twitter Sentiment Analysis” in CS29 Machine Learning course at Stanford University, 2013.
- Sitaram Asur and Bernardo A. Huberman “Predicting Future With Social Media”
- Finn Årup Nielsen. 2011. A new ANEW: Evaluation of a word list for sentiment analysis in microblogs in Proceedings of the ESWC Workshop on Making Sense of Microposts.
- F. Camastra, J. A. Hernandez, P. Kokol, J. Wang, and S. ZhuData cleaning, “Bag-of-Words Representation in Image Annotation: A Review”, ISRN Artificial Intelligence, Volume 2012.
- Ann Taylor, Mitchell Marcus, Beatrice Santorini, “The Penn Tree Bank: An Overview”, 2003.
- Maite Taboada, Julian Brooke, Milan Tofiloski, Kimberly Voll, Manfred Stede, “Lexicon-Based Methods for Sentiment Analysis”, Association for Computational Linguistics, 2011.
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