SOCIAL MEDIA E-CUSTOMERS’ BEHAVIOUR MINING

Ms. Rekha K Dimke , Mr. Aniruddh Fataniya

Abstract


As nowadays, Social media has become an important source of information, where people can express their opinions and their views. To analyze social media data of e-customer’s there is need to extract information for making predictions of how they behaves during shopping in  an e-commerce market place. After collecting data from an e-commerce market, performed a data mining application for extracting about how customers’ behaves online whether to buy product or not. The model which is  presented  predicts whether customers will not or will buy their items or products added to shopping baskets on a market place. As there is massive growth of online social networks (OSN) like Twitter, Facebook and other social networking portals have created a need to determine people’s opinion and moods. Posting user feedback on products has become increasingly popular for people to express their opinions toward products and services. The companies think that there is a chance for an improvement in market for a product to people to aware and feel about it. In this study, there is use of sentiment dictionary as an Affine for tokenization and preprocessing process and also there is use of machine learning techniques to find about e-commerce site that is more useful and good for e-customers by predicting and analyzing through their reviews.


Keywords


Twitter Data, Sentiment dictionary, Social media, Support Vector Machine, Artificial Neural Network.

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References


Lokmanyathilak Govindan Shankar Selvan, Tang-Sheng Moh, “A Framework for Fast-Feedback Opinion Mining on Twitter Data Streams”, IEEE 2015

Gökhan Solahtarolu,“Analysis and Prediction of E-Customers’ Behavior by Mining Clickstream Data”, IEEE 2015

Koith Douglas Stuart and Macioj “Intelligent Opinion Mining and Sentiment Analysis Using ANN”, Springer 2015

G. Vinodhini, R M Chandrasekaran “Opinion mining using principal component analysis based ensemble model for e-commerce application”, Springer 2014

Divakar Yadav, Geetika Gautam “Sentiment Analysis of Twitter Data Using ML Approaches and Semantic Analysis”, IEEE 2014

Jun Yang, Lan Jiang, ChongJun Wan and Junyuan Xie “Multi-Label Emotion Classification for Tweets in Weibo: Chinese site”, IEEE 2014

Hauma Isah,Paul Trundle,Daneil Neagu “Social media analytics for product safety Using text mining and sentiment analysis”, IEEE 2014

Neethu M S, Rajasree R “Sentiment Analysis in Twitter using Machine Learning Techniques” , IEEE 2013.

Xujuan Zhou, Xiahui Tao, Jianming, Zhemyu “Sentiment Analysis on Tweets for Social Events” , IEEE 2013

Uma Nagarsekar, Priyanka Kulkarni “Emotion Detection from “The SMS of the Internet”, IEEE 2013

K Unnumalia “Analysis of product using web”, Elsevier 2012

M. Rushdi Saleh, M.T. Martín-Valdivia, A. Montejo-Ráez, L.A. Ureña-López “Experiments with SVM to classify opinions in different domains” ,Elsevier 2011

Ziqiong Zhang, Qiang Ye, Zili Zhang, Yijun Li “Sentiment classification of Internet restaurant reviews written in Cantonese” Elsevier 2011

A Survey Paper on “Social Media E-Customers Behaviour Mining”, IJSART- Volume-2 Issue-12, Dec-2016, ISSN: 2395-1052

M. Rushdi Saleh, M.T. Martín-Valdivia, A. Montejo-Ráez, L.A. Ureña-López “Experiments with SVM to classify opinions in different domains” ,Elsevier 2011

Ziqiong Zhang, Qiang Ye, Zili Zhang, Yijun Li “Sentiment classification of Internet restaurant reviews written in Cantonese” Elsevier 2011


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