Skip to main content
SpringerLink
Log in

Sentiment Analysis Using Learning Techniques

  • Conference paper
  • First Online:
Mobile Radio Communications and 5G Networks

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 339))

  • 427 Accesses

Abstract

This paper depicts sentiment analysis classification as an efficient process for analysing textual data coming from various internet resources. One of the most prominent research subjects in recent years has been sentiment analysis. It is a critical challenge for both companies and users to provide an accurate and useful overview of the network, which requires a large amount of data in terms of configuration, usage and content; hence, the sentiment analysis principle is proposed to address this problem. Methods of sentiment analysis seek to reveal any feelings, subjectivity and opinions in the text. It is virtually difficult to analyse such a vast number of reviews manually. As a result, SA is used for extracting the general polarity or sentiment of opinions from documents. Sentiment research is used in Twitter, movie reviews, blogs and consumer comments, among other places. For sentiment analysis, three methods are commonly used: Machine Learning-based techniques, lexicon-based techniques and hybrid techniques, but the Machine Learning approach is more efficient and accurate. Many variations and extensions of machine learning methods and software have recently been available in recent times. This paper presents a brief introduction to the sentiment analysis, its classification and levels of sentiment analysis. Further, Machine learning techniques for sentiment analysis are also mentioned in detail in this paper. This paper aims to provide a brief knowledge of the sentiment analysis process, including standard SA methods, from the viewpoint of ML methods, in which machines interpret and identify human sentiments conveyed in speech and text.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Liu B (2010) Sentiment analysis and subjectivity. In: Indurkhya N, Damerau FJ (eds) Handbook of natural language processing, 2nd edn. Taylor and Francis

    Google Scholar 

  2. Liu B (2009) Sentiment analysis and opinion mining. In: 5th Text analytics summit, Boston, June 1–2, 2009

    Google Scholar 

  3. Singh J, Singh G, Singh R (2016) A review of sentiment analysis techniques for opinionated web text, CSI Trans. ICT, 2016

    Google Scholar 

  4. Aydogan E, Akcayol MA (2016) A comprehensive survey for sentiment analysis tasks using machine learning techniques. In: International Symposium on INnovations in Intelligent SysTems and Applications

    Google Scholar 

  5. Medhat W, Hassan A, Korashy H (2014) Sentiment analysis algorithms and applications: a survey. Ain ShamsShams Eng J 5(4):1093–1113

    Article  Google Scholar 

  6. Ravi K, Ravi V (2015) A survey on opinion mining and sentiment analysis: tasks, approaches and applications. Knowl-Based Syst 89:14–46

    Article  Google Scholar 

  7. Rushdi Saleh M, Martín-Valdivia MT, Montejo-Ráez A, Ureña- López LA (2011) Experiments with SVM to classify opinions in different domains. Exp Syst Appl 38(12):14799–14804

    Google Scholar 

  8. Xu T, Qinke P, Yinzhao C (2012) Identifying the semantic orientation of terms using S-HAL for sentiment analysis. Knowl-Based Syst 35:279–289

    Article  Google Scholar 

  9. Yu LC, Wu J, Chang PC, Chu HS (2013) Using a contextual entropy model to expand emotion words and their intensity for the sentiment classification of stock market news. Knowl-Based Syst 41:89–97

    Google Scholar 

  10. Hagenau M, Liebmann M, Neumann D (2013) Automated news reading: stock price prediction based on financial news using context-capturing features. Decis Supp Syst 55(3):685–697

    Google Scholar 

  11. Isa M, Piek V (2012) A lexicon model for deep sentiment analysis and opinion mining applications. Decis Support Syst 53:680–688

    Article  Google Scholar 

  12. Martín-Valdivia MT, Martínez-Cámara E, Perea-Ortega JM, Ureña-López LA (2013) Sentiment polarity detection in Spanish reviews combining supervised and unsupervised approaches. Exp Syst Appl 40(10) 3934–3942

    Google Scholar 

  13. Ortigosa-Hernández J, Rodríguez JD, Alzate L, Lucania M, Inza I, Lozano JA (2012) Approaching sentiment analysis by using semi-supervised learning of multi-dimensional classifiers. Neurocomputing 92:98–115

    Article  Google Scholar 

  14. Liu B (2012) Sentiment analysis and opinion mining. Synth Lect Hum Lang Tech 5(1):1–167

    Google Scholar 

  15. Pang B, Lee L, Vaithyanathan S (2002) Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of the conference on empirical methods in natural language processing (EMNLP), pp 79–86

    Google Scholar 

  16. Pang B, Lee L (2005) Seeing stars: exploiting class relationships for sentiment categorization with respect to rating scales. In: Proceedings of the association for computational linguistics (ACL), pp 115–124

    Google Scholar 

  17. Turney P (2005) Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the association for computational linguistics (ACL), pp 417–424

    Google Scholar 

  18. Dave K, Lawrence S, Pennock DM (2003) Mining the peanut gallery: opinion extraction and semantic classification of product reviews. In: Proceedings of of the 12th international conference on World Wide Web (WWW), pp 519–528

    Google Scholar 

  19. Yu H, Hatzivassiloglou V (2003) Towards answering opinion questions: separating facts from opinions and identifying the polarity of opinion sentences. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)

    Google Scholar 

  20. Liu B, Hu M, Cheng J (2005) Opinion observer: analyzing and comparing opinions on the web. In: Proceedings of the 14th international World Wide web conference (WWW-2005). ACM Press, pp 10–14

    Google Scholar 

  21. Kim S, Hovy E (2004) Determining the sentiment of opinions. In: Proceedings of the international conference on computational linguistics (COLING)

    Google Scholar 

  22. Kamps J, Marx M, Mokken RJ, de Rijke M (2004) Using WordNet to measure semantic orientation of adjectives. In: Language resources and evaluation (LREC)

    Google Scholar 

  23. Hatzivassiloglou V, McKeown K (2004) Predicting the semantic orientation of adjectives. In: Proceedings of the Joint ACL/EACL conference, pp 174–181

    Google Scholar 

  24. Esuli A, Sebastiani, F (2005) Determining the semantic orientation of terms through gloss classification. In: Proceedings of CIKM-05, the ACM SIGIR conference on information and knowledge management, Bremen, DE

    Google Scholar 

  25. Day M, Lee C (2016) Deep learning for financial sentiment analysis on finance news providers, no. 1, pp 11271134

    Google Scholar 

  26. Vateekul and Koomsubha (2016) A study of sentiment analysis using deep learning techniques on Thai Twitter data

    Google Scholar 

  27. Zhou S, Chen Q, Wang X (2013) Active deep learning method for semi-supervised sentiment classification. Neurocomputing 120:536546

    Google Scholar 

  28. Deng L, Hinton G, Kingsbury B (2013) New types of deep neural network learning for speech recognition and related applications: an overview. In: IEEE Int. Conf. Acoust. Speech Signal Process., pp 85998603

    Google Scholar 

  29. Bengio S, Deng L, Larochelle H, Lee H, Salakhutdinov R (2013) Guest editors introduction: special section on learning deep architectures. IEEE Trans Pattern Anal Mach Intell 35(8):17951797

    Google Scholar 

  30. Arnold L, Rebecchi S, Chevallier S, Paugam-Moisy H (2011) An introduction to deep learning, Esann, no. April, p 12

    Google Scholar 

  31. Ouyang X, Zhou P, Li CH, Liu L (2015) Sentiment analysis using convolutional neural network, Comput. Inf. Technol. Ubiquitous Comput. Commun. Dependable, Auton. Secur. Comput. Pervasive Intell. Comput. (CIT/IUCC/DASC/PICOM), 2015 IEEE Int. Conf., pp 23592364

    Google Scholar 

  32. Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space, Arxiv, no. 9, pp 112

    Google Scholar 

  33. Kalchbrenner N, Grefenstette E, Blunsom P (2014) A convolutional neural network for modelling sentences, Acl, pp 655665

    Google Scholar 

  34. Kim Y (2014) Convolutional neural networks for sentence classification. In: Proceedings of 2014 Conf. Empir. Methods Nat. Lang. Process. (EMNLP 2014),pp 17461751

    Google Scholar 

  35. Mikolov T, Chen K, Corrado G, Dean J (2013) Distributed representations of words and phrases and their compositionality, Nips, pp 19

    Google Scholar 

  36. Wu Z, Virtanen T, Kinnunen, T, Chng ES, Li H (2013) Exemplar-based unit selection for voice conversion utilizing temporal Information. In: Proc. Annu. Conf. Int. Speech Commun. Assoc. INTERSPEECH, pp 30573061

    Google Scholar 

  37. Tai KS, Socher R, Manning CD (2015) Improved semantic representations from tree-structured long short-term memory networks. In: Proc. ACL, pp. 15561566

    Google Scholar 

  38. Piryani R, Madhavi D, Singh VK (2017) Analytical mapping of opinion mining and sentiment analysis research. Inf Process Manage 53(1):122–150. https://doi.org/10.1016/j.ipm.2016.07.001

    Article  Google Scholar 

  39. Hussein DMEDM (2016) A survey on sentiment analysis challenges. J King Saud University - Engineering Sciences, 34(4). doi:https://doi.org/10.1016/j.jksues.2016.04.002

  40. Devika MD, Sunitha C, Ganesh A (2016) Sentiment analysis: a comparative study on different approaches. Procedia Computer Science 87:44–49

    Article  Google Scholar 

  41. Kharde VA, Sonawane SS (2016) Sentiment analysis of twitter data: a survey of techniques. Int J Comput Appl 139(11):975–8887

    Google Scholar 

  42. Rajput Q, Haider S, Ghani S (2016) Lexicon-based sentiment analysis of teachers ’ evaluation. Hindawi Appl Comput Intell Soft Comput 6:2016

    Google Scholar 

  43. Pradhan VM, Vala J, Balani P (2016) A survey on sentiment analysis algorithms for opinion mining. Int J Comp Appl 133(9):7–11. https://doi.org/10.1016/j.jksues.2016.04.002

    Article  Google Scholar 

  44. Wang Z, Cui X, Gao L, Yin Q, Ke L, Zhang S (2016) A hybrid model of sentimental entity recognition on mobile social media. EURASIP J Wirel Commun Netw 2016(1):253. https://doi.org/10.1186/s13638-016-0745-7

    Article  Google Scholar 

  45. Jotheeswaran J, Kumaraswamy YS (2013) Opinion mining using decision tree based feature selection through Manhattan hierarchical cluster measure. J Theor Appl Inform Technol 58(1):72–80

    Google Scholar 

  46. Kaur J, Vashisht S (2012) Analysis and indentifying variation in human emotion through data mining. Int J Comp Technol Appl 133(9):121–126

    Google Scholar 

  47. Bhadane C, Dalal H, Doshi H (2015) Sentiment analysis: measuring opinions, Science Direct

    Google Scholar 

  48. Li W, Xu H (2013) Text-based emotion classification using emotion cause extraction, Elsevier

    Google Scholar 

  49. Bhadane C, Dalal H, Doshi H (2015) Sentiment analysis: measuring opinions. International conference on advanced computing technologies and applications (ICACTA2015). Procedia Computer Science 45:808–814

    Google Scholar 

  50. dos Santos CN, Gatti M (2014) Deep convolutional neural networks for sentiment analysis of short texts, Coling-2014, pp 6978

    Google Scholar 

  51. Gao K, Xu H, Wanga J (2015) A rule-based approach to emotion cause detection for Chinese micro-blogs, Elsevier

    Google Scholar 

  52. Smeureanu I, Bucur C (2012) Applying supervised opinion mining techniques on online user reviews. Informatica Economică 16(2):81–91

    Google Scholar 

  53. Pang B, Lee L (2008) Opinion mining and sentiment analysis, foundations and trends in information retrieva l. 2:1–2

    Google Scholar 

  54. Nithya R, Maheswari D (2014) Sentiment analysis on unstructured review. In: International Conference in Intelligent Computing Applications (ICICA), pp 367–371

    Google Scholar 

  55. Fersini E, Messina E, Pozzi FA (2014) Sentiment analysis: Bayesian ensemble learning. Dec Supp Syst 68:26–38

    Google Scholar 

  56. Cheong M, Lee VCS (2011) A microblogging-based approach to terrorism informatics: exploration and chronicling civilian sentiment and response to terrorism events via Twitter. Inf Syst Front 13(1):45–59

    Article  Google Scholar 

  57. Go A, Bhayani R, Huang L (2009) Twitter sentiment classification using distant supervision. Processing 150(12):1–6

    Google Scholar 

  58. Pang B, Lee L (2008) Opinion mining and sentiment analysis. Found Trends Inf Retr 2(1–2):1–135

    Article  Google Scholar 

  59. Nigam K, Lafferty J, McCallum A (1999) Using maximum entropy for text classification. In: IJCAI-99 workshop on machine learning for information filtering, vol 1, pp 61–67

    Google Scholar 

  60. Vinodhini G, Chandrasekaram RM (2012) Sentiment analysis and opinion mining: a survey. Int J Adv Res Comp Sci Softw Eng 2(6):28–35

    Google Scholar 

  61. Popescu AM, Etzioni O (2005) Extracting product features and opinions from reviews. In: Proceedings of international conference on human language technology and empirical methods in natural language processing, pp 339–346

    Google Scholar 

  62. Benamara F, Cesarano C, Reforgiato D (2006) Sentiment analysis: Adjectives and Adverbs are better than Adjectives Alone. In: Proceedings of international conference on Weblogs and social media, pp 1–7

    Google Scholar 

  63. Kaya M (2013) Sentiment analysis of Turkish political columns with transfer learning. Middle East Technical University, Diss

    Google Scholar 

  64. Çetin M, Amasyali MF (2013) Active learning for Turkish sentiment analysis. In: IEEE international symposium on innovations in intelligent systems and applications (INISTA), pp 1–4

    Google Scholar 

  65. Moraes R, Valiati JF, Gavião Neto WP (2013) Document-level sentiment classification: an empirical comparison between SVM and ANN. Exp Sys Appl 40(2):621–633

    Google Scholar 

  66. Seker SE, Al-Naami K (2013) Sentimental analysis on Turkish blogs via ensemble classifier. In: Proceedings the international conference on data mining

    Google Scholar 

  67. Rui H, Liu Y, Whinston A (2013) Whose and what chatter matters? The effect of tweets on movie sales. Decis Support Syst 55(4):863–870

    Article  Google Scholar 

  68. Cârdei C, Manior F, Rebedea T (2013) Opinion mining for social media and news items in Romanian. In: 2nd international conference on systems and computer science (ICSCS), pp 240–245

    Google Scholar 

  69. Akba F, Uçan A, Sezer E, Sever H (2014) Assessment of feature selection metrics for sentiment analyses: Turkish movie reviews. In: 8th European conference on data mining, vol 191, pp 180–184

    Google Scholar 

  70. Nizam H, Akın SS (2014) Sosyal medyada makine öğrenmesi ile duygu analizinde dengeli ve dengesiz veri setlerinin performanslarının karşılaştırılması. XIX. Türkiye'de İnternet Konferansı, pp 1–6

    Google Scholar 

  71. Meral M. Diri B (2014) Sentiment analysis on Twitter. In: Signal processing and communications applications conference (SIU), pp 690–693

    Google Scholar 

  72. Tripathy A, Agrawal A, Rath SK (2015) Classification of sentimental reviews using machine learning techniques. Procedia Computer Science 57:821–829

    Article  Google Scholar 

  73. Vinodhini G, Chandrasekaran RM (2015) A comparative performance evaluation of neural network based approach for sentiment classification of online reviews. J King Saud University - Comp Inform Sci 28(1):2–12

    Google Scholar 

  74. Shahana PH, Omman B (2015) Evaluation of features on sentimental analysis. Procedia Computer Science 46:1585–1592

    Google Scholar 

  75. Florian B, Schultze F, Strauch L (2015) Semantic search: sentiment analysis with machine learning algorithms on German news articles

    Google Scholar 

  76. Tian, F, Wu F, Chao KM, Zheng Q, Shah N, Lan T, Yue J (2015) A topic sentence-based instance transfer method for imbalanced sentiment classification of Chinese product reviews. Elect Comm Res Appl

    Google Scholar 

  77. Lee S, Choeh JY (2014) Predicting the helpfulness of online reviews using multilayer perceptron neural networks. Expert Syst Appl 41(6):3041–3046

    Article  Google Scholar 

  78. Chen CC, De Tseng Y (2011) Quality evaluation of product reviews using an information quality framework. Decis Support Syst 50(4):755–768

    Article  Google Scholar 

  79. Ghose A, Ipeirotis PG (2011) Estimating the helpfulness and economic impact of product reviews: mining text and reviewer characteristics. IEEE Trans Knowl Data Eng 23(10):1498–1512

    Article  Google Scholar 

  80. Lin Y, Zhu T, Wu H, Zhang J, Wang X, Zhou A (2014) Towards online anti-opinion spam: spotting fake reviews from the review sequence. In: Proceedings of the 2014 IEEE/ACM international conference on advances in social networks analysis and mining, ASONAM, pp 261– 264, 2014

    Google Scholar 

  81. Ott M, Cardie C, Hancock JT (2013) Negative deceptive opinion spam. In: HLT-NAACL, pp 497–501

    Google Scholar 

  82. Costa H, Merschmann LHC, Barth F, Benevenuto F (2014) Pollution, badmouthing, and local marketing: the underground of location-based social networks. Inf Sci 279:123–137

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Maharishi Markandeshwar Engineering College, Maharishi Markandeshwar (Deemed Too Bee) University, Haryana, India

    A. Kathuria & A. Sharma

Authors
  1. A. Kathuria
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

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

    Nikhil Marriwala

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

    C.C Tripathi

  3. Department of Electronics and Communication Engineering, Jaypee University of Information Technology, Solan, Himachal Pradesh, India

    Shruti Jain

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

    Dinesh Kumar

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kathuria, A., Sharma, A. (2022). Sentiment Analysis Using Learning Techniques. In: Marriwala, N., Tripathi, C., Jain, S., Kumar, D. (eds) Mobile Radio Communications and 5G Networks. Lecture Notes in Networks and Systems, vol 339. Springer, Singapore. https://doi.org/10.1007/978-981-16-7018-3_42

Download citation

  • DOI: https://doi.org/10.1007/978-981-16-7018-3_42

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-7017-6

  • Online ISBN: 978-981-16-7018-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation