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Opinion Mining over Twitterspace: Classifying Tweets Programmatically using the R Approach
Jinan Fiaidhi1; Osama Mohammed2; Sabah Mohammed1; Simon Fong3; Tai hoon Kim4
2012-12-31
Conference NameSeventh International Conference on Digital Information Management (ICDIM 2012)
Source Publication7th International Conference on Digital Information Management, ICDIM 2012
Pages313-319
Conference Date22-24 Aug. 2012
Conference PlaceMacau, China
Abstract

Today the channels for expressing opinions seem to increase daily. When these opinions are relevant to a company, they are important sources of business insight, whether they represent critical intelligence about a customer's defection risk, the impact of an influential reviewer on other people's purchase decisions, or early feedback on product releases, company news or competitors. Capturing and analyzing these opinions is a necessity for proactive product planning, marketing and customer service and it is also critical in maintaining brand integrity. The importance of harnessing opinion is growing as consumers use technologies such as Twitter to express their views directly to other consumers. Tracking the disparate sources of opinion is hard - but even harder is quickly and accurately extracting the meaning so companies can analyze and act. Tweets' Language is complicated and contextual, especially when people are expressing opinions and requires reliable sentiment analysis based on parsing many linguistic shades of gray. This article argues that using the R programming platform for analyzing tweets programmatically simplifies the task of sentiment analysis and opinion mining. An R programming technique has been used for testing different sentiment lexicons as well as different scoring schemes. Experiments on analyzing the tweets of users over six NHL hockey teams reveals the effectively of using the opinion lexicon and the Latent Dirichlet Allocation (LDA) scoring scheme. © 2012 IEEE.

KeywordClassification Algorithms Data Mining Sentiment Analysis Twitter
DOIhttps://doi.org/10.1109/ICDIM.2012.6360095
URLView the original
Language英语
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Document TypeConference paper
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Affiliation1.Department of Computer Science, Lakehead University, Thunder Bay, Ontario P7B 5E1, Canada
2.Department of Software Engineering, Lakehead University, Thunder Bay, Ontario P7B 5E1, Canada
3.Faculty of Science and Technology University of Macau, Macau, China
4.Department of Computer Engineering, Glocal Campus, Konkuk University, Korea
Recommended Citation
GB/T 7714
Jinan Fiaidhi,Osama Mohammed,Sabah Mohammed,et al. Opinion Mining over Twitterspace: Classifying Tweets Programmatically using the R Approach[C],2012:313-319.
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