In this paper we have explained the detailed work done in developing a system which can be used for the purpose of opinion analysis of a product or a service. The system readily processes the tweets by pulling data from tweeter posts, preprocessing it and connecting to Alchemy API by REST call method. and showing the result graphically. We have given the analysis for the product Samsung Galaxy Our proposed system access the public tweets by API and filters them for Samsung Galaxy. The analysis is being carried out as to classify the sentiment as positive, negative or neutral. © 2014 IEEE.

Tapan Kumar Das

Department of Digital Communications

School of Information Technology and Engineering

Vellore Campus

Debi Prasanna Acharjya

Department of Computational Intelligence

School of Computer Science and Engineering

Patra M.R.

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Opinion mining about a product by analyzing public tweets in Twitter

2014 International Conference on Computer Communication and Informatics

Most of the service providers and product based companies while launching brand new products, services or releasing new versions of existent products need to campaign to reach at the potential customers. While doing so they target their already existing customers who are the ambassadors of their company. To address the existing customers, they maintain the detailed customer data at all levels as customer maser data [9]. In this paper, we have built a prediction model to identify the customers who would most likely respond to the prospective offerings of the company basing on their past purchasing trends. Experiments have been conducted using the well known classifiers, viz., Naïve Bayes, KNN and SVM to classify a bank customer data. Subsequently, we have compared the effectiveness of these techniques and found out which one produces the maximum accuracy for the existing data set. © 2015 IEEE.

2015 International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT)

A customer classification prediction model based on machine learning techniques

International Conference on Advances in ICT for Emerging Regions (ICTer2012)

Opinion mining and sentiment analysis on a Twitter data stream

Foundations and Trends® in Information Retrieval

Opinion Mining and Sentiment Analysis

Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics  - ACL '04

A sentimental education

Proceedings of the ACL-02 conference on Empirical methods in natural language processing - EMNLP '02

Thumbs up?

Psychological Medicine

Emotion: Theory, Research and Experience. Volume 1. Theories of Emotion. Edited by R. Plutchik and H. Kellerman. (Pp. 399; illustrated; £19.00.) Academic Press: London. 1980.

Journal	Data powered by Typeset2014 International Conference on Computer Communication and Informatics
Publisher	Data powered by TypesetIEEE
Open Access	No