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Data Mining of Social Media for Analysis of Product Review
| 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
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
- Li, Chun-Wen, Hui-Chi Chuang, and Sheng-Tun Li, 2016, "Hedonic Analysis for Consumer Electronics Using Online Product Reviews." 2016 5th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI).
- Jin, Jian, and Ping Ji., 2015, "Mining Online Product Reviews to Identify Consumers' Fine-grained Concerns." 12th International Symposium on Operations Research and Its Applications in Engineering, Technology and Management (ISORA 2015).
- Simsek, Atakan, and Pinar Karagoz, 2014 "Sentiment Enhanced Hybrid TF-IDF for Microblogs." 2014 IEEE Fourth International Conference on Big Data and Cloud Computing.
- Seshathriaathithyan, S., M. V. Sriram, S. Prasanna, and R. Venkatesan 2016 "Affective — Hierarchical Classification of Text — An Approach Using NLP Toolkit." 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT).
- Sari, Yunita, Mohd Fadzil Hassan, and Norshuhani Zamin, 2009, "Creating Extraction Pattern by Combining Part of Speech Tagger and Grammatical Parser." 2009 International Conference on Computer Technology and Development, 2009.
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- Aravindan, Siddharth, and Asif Ekbal, 2014, “Feature Extraction and Opinion Mining in Online Product Reviews.” 2014 International Conference on Information Technology.
- Akay, Atlug, Andrei Dragomir, and Bjorn-Erik Erlandsson, 2015 “Network-Based Modeling and Intelligent Data Mining of Social Media for Improving Care.” IEEE Journal of Biomedical and Health Informatics 19, no. 1: 210-218.
- Sudhakaran, Periakaruppan, Shanmugasundaram Hariharan, and Joan Lu., 2014, "Classifying Product Reviews from Balanced Datasets for Sentiment Analysis and Opinion Mining." 2014 6th International Conference on Multimedia, Computer Graphics and Broadcasting
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