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Twitter Data Retrieval and Preprocessing through R Programming in Social Media: An Overview of Bengaluru Traffic, India

Affiliations

  • Department of Computer Science and Applications, Bangalore University, Bengaluru – 560056, Karnataka, India
  • Department of MCA, Bapuji Institute of Engineering and Technology, Davangere-577004, Karnataka, India

Abstract


Objectives: The development of Web 2.0 has produced interactive social networking sites impacting deliberate increase in both users and social media websites. The emphasis is placed on mining social media for sentiments, opinions, emotions and attitudes. Sentiment analysis is one of major applications of social media and also one of the rapidly emerging technologies used in decision making process. This research paper focuses on data collection and data preprocessing through R programming in Twitter also gives an overview of Bengaluru traffic. Methods: An app is created through twitter account and linked with R Studio followed by data collection and preprocessing and overview of the data. Findings: Overview on Bangalore traffic is given from the tweets collected. Novelty: R serves as an effective tool for data retrieval and preprocessing of tweets and prepares the data for further analysis.

Keywords

Data Preprocessing, R Programming, Social Media, Text Mining

References


  • Raju E, Sravanthi K. Analysis of social networks using the techniques of web mining. International Journal of Advanced Research in Computer Science and Software Engineering. 2012; 2(10):1–8.
  • Gundecha P, Liu H. Mining social media: A brief introduction.Tutorials in Operations Research. 2012; 1(4):1–17.
  • Crossref
  • Haddi E, Liu X, Shi Y. The role of text pre-processing in sentiment analysis. Procedia Computer Science. 2013; 17:26–32. Crossref
  • Witten IH. Text mining. Practical handbook of Internet computing; 2005. p. 1–14.
  • Mamgain N, Mehta E, Mittal A, Bhatt G. Sentiment analysis of top colleges in India using Twitter data. International Conference on Computational Techniques in Information and Communication Technologies (ICCTICT); 2016. p. 525–30. Crossref
  • Shukri SE, Yaghi RI, Aljarah I, Alsawalqah H. Twitter sentiment analysis: A case study in the automotive industry.IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT); 2015. p. 1–5. Crossref
  • Atefeh F, Khreich W. A survey of techniques for event detection in twitter. Computational Intelligence. 2015; 31(1):132–64. Crossref
  • Fayyad U, Piatetsky-Shapiro G, Smyth P. From data mining to knowledge discovery in databases. AI magazine. 1996; 17(3):37.
  • Jham K. Data Mining Concepts and Techniques. 3rd edition. In Morgan Kaufmann; 2012.
  • Agarwal A, Xie B, Vovsha I, Rambow O, Passonneau R.Sentiment analysis of twitter data. In Proceedings of the workshop on languages in social media; 2011. p. 30–8. PMid:21552405 PMCid:PMC3083532
  • Hemalatha I, Varma GS, Govardhan A. Preprocessing the informal text for efficient sentiment analysis. International Journal of Emerging Trends & Technology in Computer Science (IJETTCS). 2012; 1(2):1–4.
  • Shahheidari S, Dong H, Daud MNRB. Twitter sentiment mining: A multi domain analysis. Seventh International Conference Complex, Intelligent, and Software Intensive Systems (CISIS); 2013. p. 144–9. Crossref
  • Javed N. Automating Corpora Generation with Semantic Cleaning and Tagging of Tweets for Multi-dimensional Social Media Analytics. International Journal of Computer Applications. 2015; 127(12):11–6. Crossref
  • Vijayarani S, Ilamathi MJ, Nithya M. Preprocessing Techniques for Text Mining-An Overview. 2016; 5(1):7–16.
  • D’Andrea E, Ducange P, Lazzerini B, Marcelloni F. Realtime detection of traffic from twitter stream analysis.
  • IEEE transactions on intelligent transportation systems. 2015; 16(4):2269–83. Crossref
  • Anastasi G, Antonelli M, Bechini A, Brienza S, D’Andrea E, De Guglielmo D, Ducange P, Lazzerini B, Marcelloni F, Segatori A. Urban and social sensing for sustainable mobility in smart cities. In Sustainable Internet and ICT for Sustainability (SustainIT). 2013 Oct 30-31. p. 1–4. Crossref
  • Zafarani R, Abbasi MA, Liu H. Social media mining: an introduction: Cambridge University Press; 2014. p. 1–382. https://doi.org/10.1017/CBO9781139088510.002 Crossref
  • Ravindran SK, Garg V. Mastering social media mining with R: PACKT Publishing Ltd; 2015. p. 248.
  • Teja R. Data Mining Tools- An Analytical Approach. International Journal of Engineering Research & Technology (IJERT). 2016; 4(27):6.

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