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Applied Intelligence

, Volume 48, Issue 5, pp 1176–1188 | Cite as

Successes and challenges in developing a hybrid approach to sentiment analysis

  • Orestes Appel
  • Francisco Chiclana
  • Jenny Carter
  • Hamido Fujita
Article

Abstract

This article covers some success and learning experiences attained during the developing of a hybrid approach to Sentiment Analysis (SA) based on a Sentiment Lexicon, Semantic Rules, Negation Handling, Ambiguity Management and Linguistic Variables. The proposed hybrid method is presented and applied to two selected datasets: Movie Review and Sentiment Twitter datasets. The achieved results are compared against those obtained when Naïve Bayes (NB) and Maximum Entropy (ME) supervised machine learning classification methods are used for the same datasets. The proposed hybrid system attained higher accuracy and precision scores than NB and ME, which shows its superiority when applied to the SA problem at the sentence level. Finally, an alternative strategy to calculating the orientation polarity and polarity intensity in one step instead of the two steps method used in the hybrid approach is explored. The analysis of the yielded mixed results achieved with this alternative approach shows its potential as an aid in the computation of semantic orientations and produced some lessons learnt in developing a more effective mechanism to calculating the orientation polarity and polarity intensity.

Keywords

Sentiment analysis Fuzzy sets Semantic rules Natural language processing Computational linguistic Uninorms SentiWordNet Computing with sentiments 

References

  1. 1.
    Anbananthen KSM, Elyasir AMH (2013) Evolution of opinion mining. Aust J Basic Appl Sci 7(6):359–370Google Scholar
  2. 2.
    Appel O, Chiclana F, Carter J (2015) Main concepts, state of the art and future research questions in sentiment analysis. Acta Polytechnica Hungarica - J Appl Sci 12(3):87–108Google Scholar
  3. 3.
    Brenga C, Celotto A, Loia V, Senatore S (2016) Capturing digest emotions by means of fuzzy linguistic aggregation. Springer International Publishing, Cham, pp 113–139Google Scholar
  4. 4.
    Buchanan B, Shortliffe EE (1984) Rule-based expert systems: the MYCIN experiments of the stanford heuristic programming project. Addison-Wesley, Reading, MAGoogle Scholar
  5. 5.
    Chiclana F, Herrera-Viedma E, Alonso S, Herrera F (2009) Cardinal consistency of reciprocal preference relations: a characterization of multiplicative transitivity. IEEE Trans Fuzzy Syst 17(1):14–23CrossRefGoogle Scholar
  6. 6.
    Das SR, Chen MY, Agarwal TV, Brooks C, shee Chan Y, Gibson D, Leinweber D, Martinez-Jerez A, Raghubir P, Rajagopalan S, Ranade A, Rubinstein M, Tufano P (2001) Yahoo! for amazon: sentiment extraction from small talk on the web 8th Asia Pacific finance association annual conferenceGoogle Scholar
  7. 7.
    Dzogang F, Lesot M-J, Rifqi M, Bouchon-Meunier B (2010) Expressions of graduality for sentiments analysis – a survey IEEE international conference on fuzzy systems (FUZZ), 2010, pp 1–7Google Scholar
  8. 8.
    Esuli A, Sebastiani F (2006) Sentiwordnet – a publicly available lexical resource for opinion mining Proceedings of the 5th conference on language resources and evaluation (LREC06), pp 417–422Google Scholar
  9. 9.
    Fodor J (2003) On rational uninorms Proceedings of the first Slovakian-Hungarian joint symposium on applied machine intelligence, Herlany, Slovakia, February 12–14, 2003, pp 139– 147Google Scholar
  10. 10.
    Hatzivassiloglou V, McKeown K (1993) Towards the automatic identification of adjectival scales: clustering adjectives according to meaning. In: LK Schubert (ed) ACL: 31st annual meeting of the association for computational linguistics, 22–26 June 1993, Ohio State University, Columbus, Ohio, USA, proceedings. ACL, pp 172–182Google Scholar
  11. 11.
    Hatzivassiloglou V, McKeown K (1997) Predicting the semantic orientation of adjectives Proceedings of the 35th annual meeting of the ACL and the 8th conference of the european chapter of the ACL. New Brunswick, NJ, USA: ACL, pp 174– 181Google Scholar
  12. 12.
    Hu M, Liu B (2004) Mining and summarizing customer reviews Proceedings – ACM SIGKDD international conference on knowledge discovery and data mining (KDD-2004 full paper), Seattle, Washington, USA, Aug 22–25Google Scholar
  13. 13.
    Kamps J, Marx M, Mokken RJ, de Rijke M (2004) Using WordNet to measure semantic orientations of adjectives Proceedings of LREC-04, 4th international conference on language resources and evaluation, volume IV of LREC ’04, pp 1115–1118Google Scholar
  14. 14.
    Klement E, Mesiar R, Pap E (1996) On the relationship of associative compensatory operators to triangular norms and conorms. Int J Uncert Fuzziness Knowl-Based Syst 4:129–144MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Liu B (2012) Sentiment analysis and opinion mining, 1st edn. Morgan and Claypool Publishers Synthesis Lectures on Human Language TechnologiesGoogle Scholar
  16. 16.
    Loia V, Senatore S (2014) A fuzzy-oriented sentic analysis to capture the human emotion in web-based content. Knowl-Based Syst 58:75–85CrossRefGoogle Scholar
  17. 17.
    Miller G (1956) The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychological Rev 63:81–97CrossRefGoogle Scholar
  18. 18.
    Nadali S, Murad M, Kadir R (2010) Sentiment classification of customer reviews based on fuzzy logic International symposium in information technology (ITSim), 2010. Kuala Lumpur, Malaysia, vol 2, pp 1037–1040CrossRefGoogle Scholar
  19. 19.
    Pang B, Lee L (2005) Seeing stars: exploiting class relationships for sentiment categorization with respect to rating scales Proceedings of the 43rd annual meeting on association for computational linguistics (ACL ’05), ACL’05, pp 115–124Google Scholar
  20. 20.
    Pang B, Lee L (2008) Opinion mining and sentiment analysis. NOW: the essence of knowledge. Found Trends Inf Retr 2(1–2):1–135CrossRefGoogle Scholar
  21. 21.
    Pang B, Lee L, Vaithyanathan S (2002) Thumbs up? Sentiment classification using machine learning techniques Proceedings of the ACL-02 conference on empirical methods in natural language processing (EMNLP), vol 10, pp 79–86CrossRefGoogle Scholar
  22. 22.
    Pérez-Asurmendi P, Chiclana F (2004) Linguistic majorities with difference in support. Appl Soft Comput 18:196–208CrossRefGoogle Scholar
  23. 23.
    Perkins J (2010) Python text processing with NLTK 2.0 cookbook. Packt PublishingGoogle Scholar
  24. 24.
    Potts C (2011) Sentiment symposium tutorial: linguistic structure (part of the Sentiment Analysis Symposium held at San Francisco, November 8–9, 2011). Stanford Department of Linguistics, Stanford University, Accessed date: December 2014Google Scholar
  25. 25.
    Rudas IJ, Fodor J (2006) Information aggregation in intelligent systems using generalized operators. Intern J Comput Commun Control I(1):47–57CrossRefGoogle Scholar
  26. 26.
    Subasic P, Huettner A (2001) Affect analysis of text using fuzzy semantic typing. IEEE Trans Fuzzy Syst 9(4):483–496CrossRefGoogle Scholar
  27. 27.
    Wang S, Manning CD (2012) Baselines and bigrams: simple, good sentiment and topic classification. In: Proceedings of the 50th annual meeting of the association for computational linguistics (ACL 2012): short papers, vol 2, pp 90–94Google Scholar
  28. 28.
    Wiebe J (2000) Learning subjective adjectives from corpora Proceedings of the seventeenth national conference on artificial intelligence and twelfth conference on innovative applications of artificial intelligence. AAAI Press, pp 735–740Google Scholar
  29. 29.
    Xie Y, Chen Z, Zhang K, Cheng Y, Honbo DK, Agrawal A, Choudhary AN (2014) MuSES: a multilingual sentiment elicitation system for social media data. IEEE Intell Syst 29(4):34–42CrossRefGoogle Scholar
  30. 30.
    Yager RR, Rybalov A (1996) Uninorm aggregation operators. Fuzzy Sets Syst 80(1):111–120. Fuzzy ModelingMathSciNetCrossRefzbMATHGoogle Scholar
  31. 31.
    Zadeh LA (2001) A new direction in ai: toward a computational theory of perceptions. AI Mag 22(1):73–84zbMATHGoogle Scholar
  32. 32.
    Zadeh LA (2002) From computing with numbers to computing with words - from manipulation of measurements to manipulation of perceptions. Int J Appl Math Comput Sci (AMCS) 12(3):307–324MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Orestes Appel
    • 1
    • 2
  • Francisco Chiclana
    • 2
  • Jenny Carter
    • 2
  • Hamido Fujita
    • 3
  1. 1.Bissett School of BusinessMount Royal UniversityCalgaryCanada
  2. 2.Centre for Computational Intelligence (CCI), Faculty of TechnologyDe Montfort UniversityLeicesterUK
  3. 3.Iwate Prefectural University (IPU)TakizawaJapan

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