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A Study of Sentiment Analysis: Concepts, Techniques, and Challenges

  • Ameen Abdullah Qaid AqlanEmail author
  • B. Manjula
  • R. Lakshman Naik
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 28)

Abstract

Sentiment analysis (SA) is a process of extensive exploration of data stored on the Web to identify and categorize the views expressed in a part of the text. The intended outcome of this process is to assess the author attitude toward a particular topic, movie, product, etc. The result is positive, negative, or neutral. These study illustrated different techniques in SA approach for extracting and analytics sentiments associated with the polarity of positive, negative, or neutral on the topic selected. Social networks SA can be a useful source of information and data. SA acquires important in many areas of business, politics, and thought. So, this study contains a comprehensive overview of the most important studies in this field from the past to the recent studies till 2017. The main aim of this study is to provide full concept about SA techniques and its classification and methods used it. Also, we give a brief overview of big data techniques and its relation and use in SA field. Because the recent period has witnessed a remarkable development in the use of Big Data (Hadoop) in the process collection of data and reviews from social networks for analysis.

Keywords

Big data Classification Challenges Sentiment analysis Social media Twitter 

References

  1. 1.
    Nasukawa Y (2003) Sentiment analysis: capturing favorability using natural language processing, IBM Almaden Research Center, CA 95120,  https://doi.org/10.1145/945645.945658
  2. 2.
    Mohey D (2016) A survey on sentiment analysis challenges. J King Saud Univ Eng  https://doi.org/10.1016/j.jksues.2016.04.002CrossRefGoogle Scholar
  3. 3.
    Alessia D (2015) Approaches, tools and applications for sentiment analysis implementation. Int J Comput Appl 125(3)Google Scholar
  4. 4.
    Xu W , Ritter A, Grishman R (2013) Gathering and generating paraphrases from twitter with application to normalizationGoogle Scholar
  5. 5.
    Hazra TK (2015) Mitigating the adversities of social media through real time tweet extraction system, IEEE,  https://doi.org/10.1109/iemcon.2015.7344483
  6. 6.
    Semih Y (2014) Tagging accuracy analysis on part-of-speech taggers. J Comput Commun 2:157–162,  https://doi.org/10.4236/jcc.2014.24021CrossRefGoogle Scholar
  7. 7.
    El-Din DM (2015) Online paper review analysis. Int J Adv Comput Sci Appl 6(9)Google Scholar
  8. 8.
    Kaushik L (2013) Sentiment extraction from natural audio streams, IEEE  https://doi.org/10.1109/icassp.2013.6639321
  9. 9.
    Vaghela VB (2016) Analysis of various sentiment classification techniques. Int J Comput Appl 140(3)Google Scholar
  10. 10.
    BiltawiL M (2016) Sentiment classification techniques for Arabic language a survey, IEEE,  https://doi.org/10.1109/iacs.2016.7476075
  11. 11.
    Goel A (2016) Real time sentiment analysis of tweets using naive bayes, IEEE,  https://doi.org/10.1109/ngct.2016.7877424
  12. 12.
    Hu M, Liu B (2004) Mining and summarizing customer reviews, seattle, Washington, USA,  https://doi.org/10.1145/1014052.1014073
  13. 13.
    Kim S-M (2004) Determining the sentiment of opinions, ACM Digital Library,  https://doi.org/10.3115/1220355.1220555
  14. 14.
    Mohammad S (2009) Generating high-coverage semantic orientation lexicons from overtly marked words and a thesaurus. In: Conference on empirical methods in natural language processing, pp 599–608Google Scholar
  15. 15.
    Miller GA (1993) Introduction to word net: an on-line lexical databaseGoogle Scholar
  16. 16.
    Hatzivassiloglou V, McKeown R (1998) Predicting the semantic orientation of adjectives, New York, N.Y.10027, USAGoogle Scholar
  17. 17.
    Medhat W (2014) Sentiment analysis algorithms and applications a survey. Ain Shams Eng J (Elsevier B.V.), 5(4):1093–1113CrossRefGoogle Scholar
  18. 18.
    Soo-Min Kim, Determining the Sentiment of Opinions, International Journal, doi=10.1.1.68.1034, (2004)Google Scholar
  19. 19.
    Pang B, Lee L (2008) Opinion mining and sentiment analysis.  https://doi.org/10.1561/1500000011CrossRefGoogle Scholar
  20. 20.
    Niu Y (2005) Analysis of polarity information in medical text, PMC JurnalGoogle Scholar
  21. 21.
    Park S (2016) Building thesaurus lexicon using dictionary based approach for sentiment classification, IEEE,  https://doi.org/10.1109/sera.2016.7516126
  22. 22.
    Ramsingh J (2016) Data analytic on diabetic awareness with Hadoop streaming using map reduce in Python, IEEE,  https://doi.org/10.1109/icaca.2016.7887979
  23. 23.
    Kim S-M, Hovy E (2006) Automatic identification of pro and con reasons in online reviews, ACM Digital LibraryGoogle Scholar
  24. 24.
    Trupthi M (2017) Sentiment analysis on twitter using streaming API, IEEE,  https://doi.org/10.1109/iacc.2017.0186
  25. 25.
    Cambria E, Hussain A (2015) Group Using Lexicon Based Approach. Springer J  https://doi.org/10.1007/978-3-319-23654-4CrossRefGoogle Scholar
  26. 26.
    Akter S (2016) Sentiment analysis on Facebook group using lexicon based approach, IEEE,  https://doi.org/10.1109/ceeict.2016.7873080
  27. 27.
    Yoshizawa A (2016) Machine-learning approach to analysis of driving simulation data, IEEE,  https://doi.org/10.1109/icci-cc.2016.7862067
  28. 28.
    Istiaq Ahsan MN (2016) An ensemble approach to detect review spam using hybrid machine learning technique, IEEE,  https://doi.org/10.1109/iccitechn.2016.7860229
  29. 29.
    Kumar M (2016) Analyzing Twitter sentiments through big data, IEEE,  https://doi.org/10.1109/sysmart.2016.7894530
  30. 30.
    Abhinandan P, Shirahatti (2015) Sentiment analysis on Twitter data using Hadoop. Int J Eng Res Gen Sci 3(6)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Ameen Abdullah Qaid Aqlan
    • 1
    Email author
  • B. Manjula
    • 1
  • R. Lakshman Naik
    • 2
  1. 1.Department of Computer ScienceKakatiya UniversityWarangalIndia
  2. 2.Department of Information TechnologyKakatiya UniversityWarangalIndia

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