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A Comprehensive Overview of Sentiment Analysis and Fake Review Detection

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Mobile Radio Communications and 5G Networks

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 140))

Abstract

Sentiment analysis (SA) is based on natural language processing (NLP) techniques used to extract the user’s feelings and opinions about any manufactured goods or services provided. Opinion mining is the other name for sentiment analysis. Sentiment analysis is very useful in the decision-making process. With greater Internet use, SA is a powerful tool for studying the opinions of customers about any product or services provided by any business organization or a company. Several approaches and techniques have came to existence in past years for sentiment analysis. Sentiment analysis is useful in decision making. In this paper, we offer an exhaustive description about techniques used for SA, approaches used for SA and applications of sentiment analysis.

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References

  1. Ravi K, Siddeshwar V, Ravi V, Mohan L (2016) Sentiment analysis applied to educational sector. In: 2015 IEEE International conference on computational intelligence and computing research ICCIC 2015, vol 500046. https://doi.org/10.1109/ICCIC.2015.7435667

  2. Wahyuni ED, Djunaidy A (2016) Fake review detection from a product review using modified method of iterative computation framework. MATEC web of conferences, vol 58.https://doi.org/10.1051/matecconf/20165803003

  3. Hovy EH (2015) What are sentiment, affect, and emotion? Applying the methodology of Michael Zock to sentiment analysis. Lang Prod Cogn Lex 48:13–24. https://doi.org/10.1007/978-3-319-08043-7

    Article  Google Scholar 

  4. Baccianella S, Esuli A, Sebastiani F (2010) SENTIWORDNET 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: Proceedings of 7th conference on language resources and evaluating LREC 2010, pp 2200–2204

    Google Scholar 

  5. Eirinaki M, Pisal S, Singh J (2012) Feature-based opinion mining and ranking. J Comput Syst Sci 78(4):1175–1184. https://doi.org/10.1016/j.jcss.2011.10.007

    Article  MathSciNet  Google Scholar 

  6. Jebaseeli P, Nisha A, Kirubakaran E (2012) A survey on sentiment analysis of (Product) reviews. Int J Comput Appl 47:0975–1888. https://doi.org/10.1007/s10462-017-9597-8

    Article  Google Scholar 

  7. Li F, Huang M, Yang Y, Zhu X (2011) Learning to identify review spam. In: International joint conferences on artificial intelligence, pp 2488–2493. https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-414

  8. Fontanarava J, Pasi G, Viviani M (2017) Feature analysis for fake review detection through supervised classification. In: Proceedings—2017 international conference on data science and advanced analytics, DSAA 2017, vol 2018, Janua, pp 658–666. https://doi.org/10.1109/DSAA.2017.51

  9. Behdenna S, Barigou F, Belalem G (2016) Sentiment analysis at document level. In: International conference on communications, computing and control, vol 628, CCIS, October, pp 159–168. https://doi.org/10.1007/978-981-10-3433-6_20

  10. Tedmori S, Awajan A (2019) Sentiment analysis main tasks and applications: a survey. J Inf Process Syst 15(3):500–519. https://doi.org/10.3745/JIPS.04.0120

    Article  Google Scholar 

  11. Yi J, Nasukawa T, Bunescu R, Niblack W (2003) Sentiment analyzer: extracting sentiments about a given topic using natural language processing techniques. In: Proceedings—IEEE international conference on data mining, ICDM, pp 427–434. https://doi.org/10.1109/icdm.2003.1250949

  12. Akoglu L, Faloutsos C (2012) Opinion fraud detection in online reviews by network effects, pp 2–11

    Google Scholar 

  13. Jindal N, Liu B (2007) Analyzing and detecting review spam, pp 547–552. https://doi.org/10.1109/ICDM.2007.68

  14. Hussain N, Mirza HT, Rasool G, Hussain I (2019) Spam review detection techniques: a systematic literature review, pp 1–26. https://doi.org/10.3390/app9050987

  15. Ho-Dac NN, Carson SJ, Moore WL (2013) The effects of positive and negative online customer reviews: do brand strength and category maturity matter? J Mark 77(6):37–53. https://doi.org/10.1509/jm.11.0011

    Article  Google Scholar 

  16. Heydari A, Tavakoli MA, Salim N, Heydari Z (2015) Detection of review spam: a survey. Expert Syst Appl 42(7):3634–3642. https://doi.org/10.1016/j.eswa.2014.12.029

    Article  Google Scholar 

  17. Holla L, Kavitha KS (2019) Opinion spam detection and analysis by identifying domain features in product reviews. SSRN Electron J, pp 571–575. https://doi.org/10.2139/ssrn.3352429

  18. Mukherjee A, Liu B, Glance N (2012) Spotting fake reviewer groups in consumer reviews. In: WWW’12—Proceedings of 21st annual conference on world wide web, pp 191–200. https://doi.org/10.1145/2187836.2187863

  19. Pang B, Lee L, Rd H, Jose S (2002) Thumbs up ? Sentiment classification using machine learning techniques. July, pp 79–86

    Google Scholar 

  20. Turney PD (2002) Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews, presented at the Association for Computational Linguistics 40th Anniversary Meeting, New Brunswick. In: ACL ’02 Proceedings of 40th annual meeting on association for computational linguistics, pp 417–424, July, pp 417–424

    Google Scholar 

  21. Yi J, Niblack W (2003) Sentiment analyzer: extracting sentiments about a given topic using natural language processing techniques

    Google Scholar 

  22. Ghose A, Designing novel review ranking systems: predicting usefulness and impact of reviews categories and subject descriptors

    Google Scholar 

  23. Hu M, Liu B, Street SM (2004) Mining and summarizing customer reviews

    Google Scholar 

  24. Zhou G, Lee SYM, Li S, Wang Z (2012) Semi-supervised learning for imbalanced sentiment classification Shoushan. In: Proceeding of twenty-second international joint conferences on artificial intelligence semi-supervised, pp 13–16. https://doi.org/10.1109/IALP.2012.53

  25. Peng Q, Zhong M (2014) Detecting spam review through sentiment analysis. J Softw 9(8). https://doi.org/10.4304/jsw.9.8.2065-2072

  26. Liu B, Mukherjee A, Shao J (2014) Spotting fake reviews using positive-unlabeled learning, vol 18, no 3, pp 2–10

    Google Scholar 

  27. Deng X, Chen R (2014) Sentiment analysis based online restaurants fake reviews hype detection. Lecture notes in computer science (including its subseries lecture notes in artificial intelligence (LNAI) and lecture notes in bioinformatics, vol 8710 LNCS, pp 1–10. https://doi.org/10.1007/978-3-319-11119-3_1

  28. Godsay M (2015) The process of sentiment analysis: a study. Int J Comput Appl 126(7):26–30. https://doi.org/10.5120/ijca2015906091

    Article  Google Scholar 

  29. Zvarevashe K (2018) A framework for sentiment analysis with opinion mining of hotel reviews, pp 0–3

    Google Scholar 

  30. Krishna GJ (2019) Sentiment classification of Indian Banks’ customer complaints. In: TENCON 2019—2019 IEEE Region 10 Conference, pp 427–432. https://doi.org/10.1109/TENCON.2019.8929703

  31. Andrea AD, Ferri F, Grifoni P (2015) Approaches, tools and applications for sentiment analysis implementation, January 2016. https://doi.org/10.5120/ijca2015905866

  32. Devika MD, Sunitha C, Ganesh A (2016) Sentiment analysis: a comparative study on different approaches. Procedia Comput Sci 87:44–49. https://doi.org/10.1016/j.procs.2016.05.124

    Article  Google Scholar 

  33. Chaturvedi I, Cambria E, Welsch RE, Herrera F (2018) Distinguishing between facts and opinions for sentiment analysis: survey and challenges. Information Fusion, vol 44, June 2017, pp 65–77. https://doi.org/10.1016/j.inffus.2017.12.006

  34. Bhuta S, Doshi U, Doshi A, Narvekar M (2014) A review of techniques for sentiment analysis of Twitter data. In: Proceedings of the 2014 international conference on issues and challenges in intelligent computing techniques, ICICT 2014, pp 583–591. https://doi.org/10.1109/ICICICT.2014.6781346

  35. Haddi E, Liu X, Shi Y (2013) The role of text pre-processing in sentiment analysis. Procedia Comput Sci 17:26–32. https://doi.org/10.1016/j.procs.2013.05.005

    Article  Google Scholar 

  36. Vryniotis V (2013) Machine learning tutorial: the max entropy text classifier. Datumbox [Online]. Available https://blog.datumbox.com/machine-learning-tutorial-the-max-entropy-text-classifier/

  37. Nigam K, Lafferty J, Mccallum A, Using maximum entropy for text classification. Science (80)

    Google Scholar 

  38. Ding X, Liu B, Yu PS (2008) A holistic lexicon-based approach to opinion mining. In: WSDM’08—Proceedings 2008 international conference web search data mining, pp 231–239. https://doi.org/10.1145/1341531.1341561

  39. O’Connor B, Balasubramanyan R, Routledge BR, Smith NA (2010) From Tweets to polls: linking text sentiment to public opinion time series Brendan. In: Fourth international AAAI conference on weblogs and social media

    Google Scholar 

  40. Khan FH, Bashir S, Qamar U (2014) TOM: Twitter opinion mining framework using hybrid classification scheme. Decis Support Syst 57(1):245–257. https://doi.org/10.1016/j.dss.2013.09.004

    Article  Google Scholar 

  41. Sentiment Analysis Examples

    Google Scholar 

  42. Li B, Wang Y, Wang S, Tang J, Liu H (2015) Unsupervised sentiment analysis for social media images. In: 24th International joint conference on artificial intelligence, Buenos Aries, Argentina, 2015, pp 2378–2379. https://doi.org/10.1109/ICDMW.2015.142

  43. Senkul P, Ozge Tokgoz Z, Gural Vural A, Barla Cambazoglu B (2012) A framework for sentiment analysis in Turkish: application to polarity detection of movie reviews in Turkish. In: Computer and information sciences III. https://doi.org/10.1007/978-1-4471-4594-3

  44. Bapat P (2014) A comprehensive review of sentiment analysis of stocks. Int J Comput Appl 106(18):1–3. https://doi.org/10.5120/18702-9870

    Article  Google Scholar 

  45. Teraiya J, Vohra S (2013) Applications and challenges for sentiment analysis: a survey. Int J Eng Res Technol 2(2)

    Google Scholar 

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Authors and Affiliations

  1. CT University, Ludhiana, India

    Gurpreet Kaur & Kamal Malik

Authors
  1. Gurpreet Kaur
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  2. Kamal Malik
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Corresponding author

Correspondence to Gurpreet Kaur .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, University Institute of Engineering and Technology (UIET), Kurukshetra University, Kurukshetra, Haryana, India

    Nikhil Marriwala

  2. University Institute of Engineering and Technology (UIET), Kurukshetra University, Kurukshetra, Haryana, India

    C. C. Tripathi

  3. Department of Electrical and Computer System Engineering, RMIT University, Melbourne, VIC, Australia

    Dinesh Kumar

  4. Department of Electronics and Communication Engineering, Jaypee University of Information Technology, Waknaghat, Himachal Pradesh, India

    Shruti Jain

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Kaur, G., Malik, K. (2021). A Comprehensive Overview of Sentiment Analysis and Fake Review Detection. In: Marriwala, N., Tripathi, C.C., Kumar, D., Jain, S. (eds) Mobile Radio Communications and 5G Networks. Lecture Notes in Networks and Systems, vol 140. Springer, Singapore. https://doi.org/10.1007/978-981-15-7130-5_22

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  • DOI: https://doi.org/10.1007/978-981-15-7130-5_22

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-7129-9

  • Online ISBN: 978-981-15-7130-5

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