Sentimental analysis on social media data using R programming

  • Authors

    • Mandava Geetha Bhargava
    • Duvvada Rajeswara Rao
    2018-05-29
    https://doi.org/10.14419/ijet.v7i2.31.13402
  • Text Mining, Polarity, Emotions, Bitcoin, Litecoin

  • Abstract

    Sentimental Analysis is an ongoing research field in Text Mining Arena to determine the situation of market on particular entity such as Product, Services...Etc. and it can be called as computational treatment of reviews, subjectivity and sentiment of text. Cryptocurrency can be explained as a type of digital estate and devised to mechanize as a form of trade and exchanges that uses cryptography as an encryption technique to secure the transactions and acts as decentralized controlled transaction which is opposed to centralized transactions. Cryptocurrency are a type of virtual currency, digital currency and alternative currency, On basis of categorical, there are different architecture and security protocols which are used in the cryptocurrencies to secure transactions, the different types of cryptocurrency are available in the market such as Bitcoin, Litecoin, and Namecoin…etc. This paper focuses on survey on different types of sentimental analysis methods and main contribution of this paper include sentimental analysis of  social media data on different types of cryptocurrencies on basis of categorical and different terms of cryptocurrency such as Cryptocurrency, virtual currency, digital currency and discussed on trends of crypto currency in present market.

     

     

  • References

    1. [1] Devika MD, Sunitha C & Ganesh A, “Sentimental Analysis: A comparative Study on Different Approachesâ€, procedia computer Science, Vol.87, (2016), pp.44-49.

      [2] Jandail RRS, Sharma P & Agrawal C, “A Survey on Sentiment Analysis and Opinion Mining: A need for an Organization and Requirement of a Customerâ€, IJETAE, Vol.4, No.3,(2014), pp.17-24.

      [3] Rao NP, Nitin Srinivas S & Prashanth CM, “Real Time Opinion Mining of Twitter Dataâ€, International Journal of Computer Science and Information Technologies(IJCSIT),Vol.6, No.3, (2015).

      [4] Singh T & Kumari M, “Role of Text Pre-Processing in Twitter Sentiment Analysisâ€, Procedia Computer Science, Vol.89,(2016), 549-554.

      [5] Fillbrunn A, Dietz C, Pfeuffer J, Rahn R, Landrum GA & Berthold MR, “KNIME for reproducible cross-domain analysis of life science dataâ€, Journal of biotechnology, Vol.261, (2017), pp.149-156.

      [6] Awrahman B & Alatas B, “Sentimental Analysis and Opinion Mining within the social networks using Konstanz Information Minerâ€, Journal of Telecommunication, Electronic and Computer Engineering ,Vol.9, (2016), pp.15-22.

      [7] Sangwan N, Malik V, Sunesh & Sangwan S, “A Survey for Text Analysis Using R languageâ€, Advances in Computational Sciences and Technology, Vol.10, (2017), pp.2173-2182.

      [8] Khanna P, Kumar S, Mishra S & Sinha A, “Sentiment Analysis: An Approach to Opinion Mining from Twitter Data Using Râ€, International Journal of Advanced Research in Computer Science, Vol.8, (2017), pp.252-256.

      [9] Thanvi PR, Sontakke NS, Waghmare SR, Patel ZS & Gavhane S, “Sentimental Analysis for Political Reviews using AAVN Combinationsâ€, International Research Journal of Engineering and Technology (IRJET),Vol.04, (2017), pp.72-74.

      [10] Arun K, Srinagesh A & Ramesh M, “Twitter Sentiment Analysis on Demonetization tweets in India Using R languageâ€, International Journal of Computer Engineering In Research Trends,Vol.4, No.6, (2017), pp.252-258.

      [11] sandhyarani K & kavitha D, “Sentiment Analysis of Social Media Dataâ€, International Journal of Emerging Trends and Technology in Computer Science(IJETTCS),Vol.6, No.5,(2017), pp.151-156.

      [12] Hutto CJ & Eric Gilbert, “VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text â€, Proceedings of the Eighth International AAAI Conference on weblogs and social media, (2014), pp.216-225.

      [13] Abdul-Mageed, M Diab M & Kubler S, “SAMAR: Subjectivity and Sentimental Analysis for Arabic Social Mediaâ€, Computer Speech and Language, Vol.28, No.1, (2014), pp.20-37.

      [14] Nguyen TH, Shirai K & Velcin J, “Sentimental analysis on Social Media for stock movement predictionâ€, Expert Systems with Applications, Vol.42, No.24, (2015), pp.9603-9611.

      [15] Sinha H & Kaur A, “A detailed survey and comparative study of sentiment analysis algorithmsâ€, 2nd International Conference on Communication Control and Intelligent Systems (CCIS),(2016), pp. 94-98.

      [16] Wang H, Hanafy A, Bahgat M, Noeman S, Emam OS & Bommireddipalli VR, “A System for Extracting Sentiment from Large-Scale Arabic Social Dataâ€, First International Conference on Arabic Computational Linguistics (ACLing), (2015), pp.71-77.

      [17] Kikuchi S & Bhalla S, “Data model for reputational analysis for combined medias social networks and IPTVâ€, IEEE 3rd Global Conference on Consumer Electronics (GCCE), (2014), pp. 51-52.

      [18] Kumar S, Singh P & Rani S, “Sentimental analysis of social media using R language and Hadoop: Rhadoopâ€, 5th International Conference on Reliability Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), (2016), pp.207-213.

      [19] Yang N, Cao S & Zhang S, “Data analysis system for online short video commentsâ€, IEEE/ACIS 15th International Conference on Computer and Information Science, (2016), pp.1-6.

      [20] Parveen H & Pandey S, “Sentiment analysis on Twitter Data-set using Naive Bayes algorithmâ€, 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), (2016), pp.416-419.

      [21] Soni AK, “Multi-lingual sentiment analysis of Twitter data by using classification algorithmsâ€, Second International Conference on Electrical Computer and Communication Technologies (ICECCT), (2017), pp.1-5.

      [22] Zhang L, Liu B & Vaithyanathan S, “Identifying noun product features that imply opinionsâ€, Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers, Vol.2, (2011), pp.575-580.

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  • How to Cite

    Geetha Bhargava, M., & Rajeswara Rao, D. (2018). Sentimental analysis on social media data using R programming. International Journal of Engineering & Technology, 7(2.31), 80-84. https://doi.org/10.14419/ijet.v7i2.31.13402

    Received date: 2018-05-28

    Accepted date: 2018-05-28

    Published date: 2018-05-29