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Levels and Classification Techniques for Sentiment Analysis: A Review

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Advances in Communication and Computational Technology (ICACCT 2019)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 668))

Abstract

Sentimental Analysis (SA) is a process by which one can examine the feelings towards services, products, movies with the help of reviews. SA is a computing treatment of feeling, opinion, and subjectivity of contents. In this survey paper, we explain the overview of the sentiment analysis. For finding the sentiment analysis of reviews, different types of levels and classification of text data are explained. Three types of levels are explained and for classification two approaches machine learning approach and lexicon-based approach are explained. Some latest articles are used to show the accuracy of the classifiers.

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References

  1. Alessia D, Ferri F, Grifoni P, Guzzo T (2015) Approaches, tools and applications for sentiment analysis implementation. Int J Comput Appl 125(3)

    Google Scholar 

  2. Kaur R, Verma P (2017) Sentiment analysis of movie reviews: a study of machine learning algorithms with various feature selection methods. ICSE 5(9):113–121. E-ISSN: 2347-2693

    Google Scholar 

  3. Dhande LL, Patnaik GK (2014) Analyzing sentiment of movie review data using Naive Bayes neural classifier. IJETTCS 3(4):313–320

    Google Scholar 

  4. Schouten K, Frasincar F (2016) Survey on aspect-level sentiment analysis. IEEE Trans Knowl Data Eng 28(3):813–830

    Google Scholar 

  5. Mumtaz D, Ahuja B (2016) A lexical approach for opinion mining in twitter. Int J Educ Manage Eng 6(4):20

    Google Scholar 

  6. Fang X, Zhan J (2015) Sentiment analysis using product review data. J Big Data 2(1):5

    Google Scholar 

  7. Medagoda N, Shanmuganathan S, Whalley J (2015) Sentiment lexicon construction using SentiWordNet 3.0. In: 2015 11th ICNC, pp 802–807

    Google Scholar 

  8. Wijesinghe A (2015) Sentiment analysis on movie reviews

    Google Scholar 

  9. Khobragade P, Jethani V (2017) Sentiment analysis of movie reviews. Int J Adv Res Comput Sci 8(5)

    Google Scholar 

  10. Sharma H, Kumar S (2016) A survey on decision tree algorithms of classification in data mining. IJSR 5(4):2094–2097

    Google Scholar 

  11. Kaur S, Kaur H (2017) Review of decision tree data mining algorithms: CART and C4. 5. Int J Adv Res Comput Sci 8(4)

    Google Scholar 

  12. Brijain M, Patel R, Kushik M, Rana K (2014) A survey on decision tree algorithm for classification. IJEDR 2(1)

    Google Scholar 

  13. Yong Z, Youwen L, Shixiong X (2009) An improved KNN text classification algorithm based on clustering. J Comput 4(3):230–237

    Google Scholar 

  14. Serrano-Guerrero J, Olivas JA, Romero FP, Herrera-Viedma E (2015) Sentiment analysis: a review and comparative analysis of web services. Inf Sci 311:18–38

    Google Scholar 

  15. Imandoust SB, Bolandraftar M (2013) Application of k-nearest neighbor (KNN) approach for predicting economic events: theoretical background. Int J Eng Res Appl 3(5):605–610

    Google Scholar 

  16. Lamba A, Kumar D (2016) Survey on KNN and its variants. IJARCCE 5(5)

    Google Scholar 

  17. Dey L, Chakraborty S, Biswas A, Bose B, Tiwari S (2016) Sentiment analysis of review datasets using naïve bays and k-NN classifier. Preprint at arXiv:1610.09982

  18. Zhang GP (2000) Neural networks for classification: a survey. IEEE Trans Syst Man Cybern Part C (Appl Rev) 30(4):451–462

    Article  Google Scholar 

  19. Stojanovski D, Strezoski G, Madjarov G, Dimitrovski I, Chorbev I (2018) Deep neural network architecture for sentiment analysis and emotion identification of twitter messages. Multimedia Tools Appl 77(24):32213–32242

    Google Scholar 

  20. Deshwal A, Sharma SK (2016) Twitter sentiment analysis using various classification algrithms. In: 2016 5th international conference on reliability, infocom technologies and optimization (Trends and Future Directions) (ICRITO), pp 251–257

    Google Scholar 

  21. Ahmad M, Aftab S, Muhammad SS, Ahmad S (2017) Machine learning techniques for sentiment analysis: a review. Int J Multidiscip Sci Eng 8(3):27–32

    Google Scholar 

  22. Paul A, Mukherjee DP, Das P, Gangopadhyay A, Chintha AR, Kundu S (2018) Improved random forest for classification. IEEE Trans Image Process 27(8):4012–4024

    Google Scholar 

  23. Vaghela VB, Jadav BM, Scholar ME (2016) Analysis of various sentiment classification techniques. Int J Comput Appl 140(3):0975–8887

    Google Scholar 

  24. Mumtaz D, Ahuja B (2016) Sentiment analysis of movie review data using senti-lexiconalgorithm. In: 2016 2nd international conference on applied and theoretical computing and communication technology (iCATccT), IEEE, pp 592–597

    Google Scholar 

  25. Shaziya H, Kavitha G, Zaheer R (2015) Text categorization of movie reviews for sentiment analysis. Int J Innovative Res Sci Eng Technol 4:11255–11262

    Google Scholar 

  26. Sharma P, Mishra N (2016) Feature level sentiment analysis on movie reviews. In: 2016 2nd international conference on next generation computing technologies (NGCT), IEEE, pp 306–311

    Google Scholar 

  27. Sahu TP, Ahuja S (2016) Sentiment analysis of movie reviews: a study on feature selection and classification algorithms. In: 2016 international conference on microelectronics computing and communications (MicroCom), IEEE, pp 1–6

    Google Scholar 

  28. Devi DN, Kumar CK, Prasad S (2016) A feature based approach for sentiment analysis by using support vector machine. In: 2016 IEEE 6th international conference on advanced computing (IACC), IEEE, pp 3–8

    Google Scholar 

  29. Tomar DS, Sharma P (2016) A text polarity analysis using sentiWordNet based an algorithm. (IJCSIT) Int J Comput Sci Inf Technol 7(1):190–193

    Google Scholar 

  30. Singh V, Saxena P, Singh S, Rajendran S (2017) Opinion mining and analysis of movie reviews. Indian J Sci Technol 10(19):1–6

    Google Scholar 

  31. Baid P, Gupta A, Chaplot N (2017) Sentiment analysis of movie reviews using machine learning techniques. Int J Comput Appl 179(7):0975–8887

    Google Scholar 

  32. Naiknaware BR, Kawathekar S, Deshmukh SN (2017) Sentiment analysis of movie ratings system. IOSR J Comput Eng (IOSR-JCE) 43–47

    Google Scholar 

  33. Wankhede R, Thakare AN (2017) Design approach for accuracy in movies reviews using sentimentanalysis. In: 2017 international conference of electronics, communication and aerospace technology (ICECA), pp. 6–11

    Google Scholar 

  34. Wankhede R, Thakare AN (2017) To improve accuracy in movies reviews using sentiment analysis. IOSR J Comput Eng (IOSR-JCE) 19(4):80–89

    Google Scholar 

  35. Garje AR, Kale KV (2017) Sentiment polarity with sentiwordnet and machine learning classifiers. Int J Adv Res Comput Sci 8(9)

    Google Scholar 

  36. Singla Z, Randhawa S, Jain S (2017) Sentiment analysis of customer product reviews using machinelearning. In: 2017 international conference on intelligent computing and control (I2C2), pp 1–5

    Google Scholar 

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Author information

Authors and Affiliations

  1. Computer Science and Engineering, MNNIT Allahabad, Prayagraj, Uttar Pradesh, India

    Devendra Sharma

  2. MNNIT Allahabad, Prayagraj, Uttar Pradesh, India

    Anoj Kumar

Authors
  1. Devendra Sharma
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  2. Anoj Kumar
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Corresponding author

Correspondence to Devendra Sharma .

Editor information

Editors and Affiliations

  1. Department of Mathematics and Computer Science, University of Maryland Eastern Shore, Princess Anne, MD, USA

    Gurdeep Singh Hura

  2. Department of Master of Computer Applications, National Institute of Technology Kurukshetra, Kurukshetra, Haryana, India

    Ashutosh Kumar Singh

  3. Department of Information Science and Technology, Multimedia University, Jalan Ayer Keroh Lama, Melaka, Malaysia

    Lau Siong Hoe

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Sharma, D., Kumar, A. (2021). Levels and Classification Techniques for Sentiment Analysis: A Review. In: Hura, G.S., Singh, A.K., Siong Hoe, L. (eds) Advances in Communication and Computational Technology. ICACCT 2019. Lecture Notes in Electrical Engineering, vol 668. Springer, Singapore. https://doi.org/10.1007/978-981-15-5341-7_27

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  • DOI: https://doi.org/10.1007/978-981-15-5341-7_27

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-5340-0

  • Online ISBN: 978-981-15-5341-7

  • eBook Packages: EngineeringEngineering (R0)

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