Skip to main content
SpringerLink
Log in

Concept-Level Sentiment Analysis with Dependency-Based Semantic Parsing: A Novel Approach

  • Published:
Cognitive Computation Aims and scope Submit manuscript

Abstract

Sentiment analysis from unstructured natural language text has recently received considerable attention from the research community. In the frame of biologically inspired machine learning approaches, finding good feature sets is particularly challenging yet very important. In this paper, we focus on this fundamental issue of the sentiment analysis task. Specifically, we employ concepts as features and present a concept extraction algorithm based on a novel concept parser scheme to extract semantic features that exploit semantic relationships between words in natural language text. Additional conceptual information of a concept is obtained using the ConceptNet ontology: Concepts extracted from text are sent as queries to ConceptNet to extract their semantics. We select important concepts and eliminate redundant concepts using the Minimum Redundancy and Maximum Relevance feature selection technique. All selected concepts are then used to build a machine learning model that classifies a given document as positive or negative. We evaluate our concept extraction approach using a benchmark movie review dataset provided by Cornell University and product review datasets on books, DVDs, and electronics. Comparative experimental results show that our proposed approach to sentiment analysis outperforms existing state-of-the-art methods.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Cambria E, Hussain A. Sentic album: content, concept, and context-based online personal photo management system. Cogn Comput. 2012;4(4):477–96.

    Article  Google Scholar 

  2. Poria S, Cambria E, Hussain A, Huang G-B. Towards an intelligent framework for multimodal affective data analysis. Neural Netw. 2015;63:104–16.

    Article  PubMed  Google Scholar 

  3. Howard N, Cambria E. Intention awareness: improving upon situation awareness in human-centric environments. Hum Centric Comput Inf Sci. 2013;3(9):1–17.

    Google Scholar 

  4. Cambria E, Hussain A, Durrani T, Havasi C, Eckl C, Munro J. Sentic computing for patient centered applications. In: IEEE ICSP, Beijing; 2010. p. 1279–1282.

  5. Cambria E, Hussain A, Havasi C, Eckl C, Munro J. Towards crowd validation of the UK national health service. In: ACM WebSci, Raleigh; 2010.

  6. Liu B. Sentiment analysis and opinion mining. Synthesis lectures on human language technologies. San Rafael: Morgan & Claypool Publishers; 2012.

    Google Scholar 

  7. Erik C, Amir H, Catheine H, Chris E. Common sense computing: From the society of mind to digital intuition and beyond. In: Lecture notes in computer science 5707, Springer; 2009. p. 252–259.

  8. Cambria E, Gastaldo P, Bisio F, Zunino R. An ELM-based model for affective analogical reasoning. Neurocomputing. 2015;149:443–55.

    Article  Google Scholar 

  9. Guyon I, Elisseeff A. An introduction to variable and feature selection. J Mach Learn Res. 2003;3(1):1157–82.

    Google Scholar 

  10. Agarwal B, Mittal N. Enhancing performance of sentiment analysis by semantic clustering of features. IETE J Res. 2014;60(6):414–22.

    Article  Google Scholar 

  11. Peng H, Long F, Ding C. Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell. 2005;27(8):1226–38.

    Article  PubMed  Google Scholar 

  12. Agarwal B, Mittal N. Prominent feature extraction for review analysis: an empirical study. J Exp Theor Artif Intell. 2014. doi:10.1080/0952813X.2014.97783.

  13. Hougue N, Bhattacharyya DK, Kalita JK. MIFS-ND: a mutual information-based feature selection method. Expert Syst Appl. 2014;41(4):6371–85.

    Google Scholar 

  14. Pang B, Lee L, Vaithyanathan S. Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of EMNLP; 2002. p. 79–86.

  15. Matsumoto S, Takamura H, Okumura M. Sentiment classification using word sub-sequences and dependency sub-trees. In: Proceedings of PAKDD; 2005. p. 301–311.

  16. Pak A, Paroubek P. Text representation using dependency tree sub-graphs for sentiment analysis. DASFAA workshop; 2011. p. 323–332.

  17. Nakagawa T, Inui K, Kurohashi S. Dependency tree-based sentiment classification using CRFs with hidden variables. In: Proceeding HLT ’10 human language technologies: annual conference of the North American chapter of the association for computational linguistics; 2010. p. 786–794.

  18. Xia R, Zong C, Li S. Ensemble of feature sets and classification algorithms for sentiment classification. J Inf Sci. 2011;181(6):1138–52.

    Article  Google Scholar 

  19. Riloff E, Patwardhan S, Janyce W. Feature subsumption for opinion analysis. In: EMNLP; 2006. p. 440–448.

  20. Joshi M, Penstein-Rose C. Generalizing dependency features for opinion mining. In: ACL; 2009. p. 313–316.

  21. Mejova Y, Srinivasan P. Exploring feature definition and selection for sentiment classifiers. In: Proceedings of the fifth international AAAI conference on weblogs and social media; 2011. p. 546–549.

  22. Mullen T, Collier N. Sentiment analysis using support vector machines with diverse information sources. In: EMNLP; 2004. p. 412–418.

  23. Osgood CE, Succi GJ, Tannenbaum PH. The measurement of meaning. Champaign: University of Illinois Press; 1957.

    Google Scholar 

  24. Turney PD. Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. ACL-2002; 2002. p. 417–424.

  25. Dang Y, Zhang Y, Chen H. A lexicon enhanced method for sentiment classification: an experiment on online product reviews. IEEE Intell Syst. 2010;25(4):46–53.

    Article  Google Scholar 

  26. Gelfand B, Wulfekuler M, Punch WF. Automated concept extraction from plain text. In: AAAI workshop on text categorization; 1998. p. 13–17.

  27. Hatzivassiloglou V, McKeown KR. Predicting the semantic orientation of adjectives. In: ACL; 1997. p. 174–181.

  28. Sidorov G, Velasquez F, Stamatatos E, Gelbukh A, Chanona-Hernandez L. Syntactic n-grams as machine learning features for natural language processing. Expert Syst Appl. 2014;41(3):853–60.

    Article  Google Scholar 

  29. Sidorov G. Non-continuous syntactic n-grams. Polibits. 2013;48(1):67–75.

    Google Scholar 

  30. de Marneffe M-C, Manning CD. The stanford typed dependencies representation. In: Coling 2008: proceedings of the workshop on cross-framework and cross-domain parser evaluation. Association for Computational Linguistics; 2008. p. 1–8.

  31. Soujanya P, Basant A, Alexander G, Amir H, Newton H. Dependency-based semantic parsing for concept-level text analysis. In: CICLing 2014; 2014. p. 113–27.

  32. Poria S, Cambria E, Winterstein G, Huang G-B. Sentic patterns: dependency-based rules for concept-level sentiment analysis. Knowl Based Syst. 2014;69:45–63.

    Article  Google Scholar 

  33. Havasi C, Speer R, Alonso JB. Conceptnet 3: a flexible, multilingual semantic network for common sense knowledge. In: Recent advances in natural language processing; 2007. p. 27–29.

  34. Wang Q-F, Cambria E, Liu C-L, Hussain A. Common sense knowledge for handwritten Chinese text recognition. Cogn Comput. 2013;5(2):234–42.

    Article  Google Scholar 

  35. Manning CD, Raghvan P, Schutze H. Introduction to information retrieval. Cambridge: Cambridge University Press; 2008.

    Book  Google Scholar 

  36. Pang B, Lee L. A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the association for computational linguistics (ACL); 2004. p. 271–278.

  37. Blitzer J, Dredze M, Pereira F. Biographies, bollywood, boom-boxes and blenders: domain adaptation for sentiment classification. In: ACL; 2007. p. 440–447.

  38. Xia R, Zong C. Exploring the use of word relation features for sentiment classification. In: COLING-2010; 2010. p. 1336–1344.

  39. Prabowo R, Thelwall M. Sentiment analysis: a combined approach. J Informetr. 2009;3(2):143–57.

    Article  Google Scholar 

  40. O’Keefe T, Koprinska I. Feature selection and weighting methods in sentiment analysis. In: Proceedings of the 14th Australasian document computing symposium, Sydney, Australia, ACL; 2009.

  41. Ng V, Dasgupta S, Arifin SM. Examining the role of linguistic knowledge sources in the automatic identification and classification of reviews. In: Proceedings of the COLING/ACL 2006 main conference poster sessions; 2006. p. 611–618.

  42. Tu Z, Jiang W, Liu Q, Lin S. Dependency forest for sentiment analysis. In: First CCF conference, natural language processing and Chinese computing; 2012. p 69–77.

  43. Abbasi A. Intelligent feature selection for opinion classification. IEEE Intell Syst. 2010;25(4):75–9.

    Google Scholar 

  44. Abbasi A, Chen H, Salem A. Sentiment analysis in multiple languages: feature selection for opinion classification in Web forums. ACM Trans Inf Syst. 2008;26(3):12.

    Article  Google Scholar 

  45. Poria S, Gelbukh A, Cambria E, Hussain A, Huang G-B. EmoSenticSpace: a novel framework for affective common-sense reasoning. Knowl Based Syst. 2014;69:108–23.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, MNIT, Jaipur, India

    Basant Agarwal & Namita Mittal

  2. Department of Computing Science and Mathematics, University of Stirling, Stirling, FK9 4LA, UK

    Soujanya Poria & Amir Hussain

  3. Centro de Investigación en Computación, Instituto Politécnico Nacional, Mexico City, Mexico

    Alexander Gelbukh

Authors
  1. Basant Agarwal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Soujanya Poria
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Namita Mittal
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Alexander Gelbukh
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Amir Hussain
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Basant Agarwal.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Agarwal, B., Poria, S., Mittal, N. et al. Concept-Level Sentiment Analysis with Dependency-Based Semantic Parsing: A Novel Approach. Cogn Comput 7, 487–499 (2015). https://doi.org/10.1007/s12559-014-9316-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12559-014-9316-6

Keywords

Search

Navigation