Skip to main content
SpringerLink
Log in

A semantic approach based on domain knowledge for polarity shift detection using distant supervision

  • Regular Paper
  • Published:
Progress in Artificial Intelligence Aims and scope Submit manuscript

Abstract

One of the main challenges in sentiment analysis is the polarity shift. Studies have shown that the detection of polarity shifts is very effective to improve the accuracy of sentiment analysis. However, the problem of polarity shift detection has not been well studied, and most studies have only focused on detecting negations, one kind of polarity shifts. This paper aims to provide a semantic method based on domain knowledge for the detection of polarity shifts. In the proposed method, a polarity shift-tagged corpus is created using the idea of distant supervision. Thereafter, the polarity shifts are detected by training the machine learning classifiers on the resulting corpus, based on the semantic features extracted from the domain knowledge. The experimental results reveal that the SVM classifier with training on the constructed corpus is capable of detecting the polarity shifts with 79.33% accuracy and 81.21% F-measure, which are 24.6% and 17.5% more accurate than the best-performing existing method, respectively. Also, the results show that with the use of the polarity shift tag as a feature, SVM classifier F-measure for sentiment analysis has been improved up to 1.2%.

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
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Agarwal, A., Biadsy, F., Mckeown, K.R.: Contextual phrase-level polarity analysis using lexical affect scoring and syntactic n-grams. In: Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics, pp. 24–32. Association for Computational Linguistics (2009)

  2. Agarwal, A., Xie, B., Vovsha, I., Rambow, O., Passonneau, R.: Sentiment analysis of twitter data. In: Proceedings of the Workshop on Language in Social Media (LSM 2011), pp. 30–38 (2011)

  3. Aronson, A.R.: Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program. In: Proceedings of the AMIA Symposium, p. 17. American Medical Informatics Association (2001)

  4. Asmi, A., Ishaya, T.: Negation identification and calculation in sentiment analysis. In: The second international conference on advances in information mining and management, pp. 1–7 (2012)

  5. Atzeni, M., Dridi, A., Recupero, D.R.: Using frame-based resources for sentiment analysis within the financial domain. Prog. Artif. Intell. 7(4), 273–294 (2018)

    Article  Google Scholar 

  6. Baccianella, S., Esuli, A., Sebastiani, F.: Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. Lrec 10, 2200–2204 (2010)

    Google Scholar 

  7. Benamara, F., Cesarano, C., Picariello, A., Recupero, D.R., Subrahmanian, V.S.: Sentiment analysis: adjectives and adverbs are better than adjectives alone. In: ICWSM, pp. 1–7. Citeseer (2007)

  8. Bodenreider, O., McCray, A.T.: Exploring semantic groups through visual approaches. J. Biomed. Inf. 36(6), 414–432 (2003). https://doi.org/10.1016/j.jbi.2003.11.002

    Article  Google Scholar 

  9. Boubel, N., François, T., Naets, H.: Automatic extraction of contextual valence shifters. In: Proceedings of the international conference recent advances in natural language processing RANLP 2013, pp. 98–104 (2013)

  10. Cambria, E., Das, D., Bandyopadhyay, S., Feraco, A.: Affective computing and sentiment analysis. Pract. Guide Sentim. Anal. (2017). https://doi.org/10.1007/978-3-319-55394-8_2

    Article  Google Scholar 

  11. Chalothom, T., Ellman, J.: Simple approaches of sentiment analysis via ensemble learning. Inf. Sci. Appl. (2015). https://doi.org/10.1007/978-3-662-46578-3_74

    Article  Google Scholar 

  12. Chapman, W.W., Bridewell, W., Hanbury, P., Cooper, G.F., Buchanan, B.G.: A simple algorithm for identifying negated findings and diseases in discharge summaries. J. Biomed. Inf. 34(5), 301–310 (2001). https://doi.org/10.1006/jbin.2001.1029

    Article  Google Scholar 

  13. Dadvar, M., Hauff, C., de Jong, F.: Scope of negation detection in sentiment analysis. In: Proceedings of the Dutch-Belgian Information Retrieval Workshop (DIR 2011), pp. 16–20. Citeseer (2011)

  14. De Marneffe, M.C., MacCartney, B., Manning, C.D., et al.: Generating typed dependency parses from phrase structure parses. Lrec 6, 449–454 (2006)

    Google Scholar 

  15. Diamantini, C., Mircoli, A., Potena, D.: A negation handling technique for sentiment analysis. In: 2016 International Conference on Collaboration Technologies and Systems (CTS), pp. 188–195. IEEE (2016). https://doi.org/10.1109/CTS.2016.0048

  16. Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 international conference on web search and data mining, pp. 231–240. ACM (2008). https://doi.org/10.1145/1341531.1341561

  17. Fleiss, J.L., Levin, B., Paik, M.C.: Statistical methods for rates and proportions. Wiley, Hoboken (2013)

    MATH  Google Scholar 

  18. Grefenstette, G., Qu, Y., Shanahan, J.G., Evans, D.A.: Coupling niche browsers and affect analysis for an opinion mining application. Proceedings of Recherche d’Information Assistée par Ordinateur (RIAO) pp. 186–194 (2004)

  19. Guo, X., Li, J.: A novel twitter sentiment analysis model with baseline correlation for financial market prediction with improved efficiency. In: 2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 472–477. IEEE (2019)

  20. Hasan, M., Rundensteiner, E., Agu, E.: Automatic emotion detection in text streams by analyzing Twitter data. Int. J. Data Sci. Anal. 7(1), 35–51 (2019). https://doi.org/10.1007/s41060-018-0096-z

    Article  Google Scholar 

  21. Heerschop, B., van Iterson, P., Hogenboom, A., Frasincar, F., Kaymak, U.: Analyzing sentiment in a large set of web data while accounting for negation. In: Advances in Intelligent Web Mastering–3, pp. 195–205. Springer (2011). https://doi.org/10.1007/978-3-642-18029-3_20

  22. Hogenboom, A., Van Iterson, P., Heerschop, B., Frasincar, F., Kaymak, U.: Determining negation scope and strength in sentiment analysis. In: 2011 IEEE International Conference on Systems, Man, and Cybernetics, pp. 2589–2594. IEEE (2011). https://doi.org/10.1109/ICSMC.2011.6084066

  23. Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 168–177. ACM (2004). https://doi.org/10.1145/1014052.1014073

  24. Huang, S., Niu, Z., Shi, C.: Automatic construction of domain-specific sentiment lexicon based on constrained label propagation. Knowl -Based Syst 56, 191–200 (2014). https://doi.org/10.1016/j.knosys.2013.11.009

    Article  Google Scholar 

  25. Hussein, D.M.E.D.M.: A survey on sentiment analysis challenges. J. King Saud Univ. Eng. Sci. 30(4), 330–338 (2018). https://doi.org/10.1016/j.jksues.2016.04.002

    Article  Google Scholar 

  26. Ikeda, D., Takamura, H., Ratinov, L.A., Okumura, M.: Learning to shift the polarity of words for sentiment classification. In: Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-I (2008)

  27. Kaji, N., Kitsuregawa, M.: Automatic construction of polarity-tagged corpus from HTML documents. In: Proceedings of the COLING/ACL on Main conference poster sessions, pp. 452–459. Association for Computational Linguistics (2006)

  28. Kennedy, A., Inkpen, D.: Sentiment classification of movie reviews using contextual valence shifters. Comput. Intell. 22(2), 110–125 (2006). https://doi.org/10.1111/j.1467-8640.2006.00277.x

    Article  MathSciNet  Google Scholar 

  29. Kim, S.M., Hovy, E.: Determining the sentiment of opinions. In: Proceedings of the 20th international conference on Computational Linguistics, p. 1367. Association for Computational Linguistics (2004). https://doi.org/10.3115/1220355.1220555

  30. Li, S., Lee, S.Y.M., Chen, Y., Huang, C.R., Zhou, G.: Sentiment classification and polarity shifting. In: Proceedings of the 23rd International Conference on Computational Linguistics, pp. 635–643. Association for Computational Linguistics (2010)

  31. Liu, B.: Sentiment analysis and opinion mining. Synth. Lectures Human Lang. Tech. 5(1), 1–167 (2012). https://doi.org/10.2200/S00416ED1V01Y201204HLT016

    Article  Google Scholar 

  32. Milea, V., Sharef, N.M., Almeida, R.J., Kaymak, U., Frasincar, F.: Prediction of the MSCI EURO index based on fuzzy grammar fragments extracted from European central bank statements. In: 2010 International Conference of Soft Computing and Pattern Recognition, pp. 231–236. IEEE (2010). https://doi.org/10.1109/SOCPAR.2010.5686083

  33. Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995). https://doi.org/10.1145/219717.219748

    Article  Google Scholar 

  34. Mintz, M., Bills, S., Snow, R., Jurafsky, D.: Distant supervision for relation extraction without labeled data. In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2-Volume 2, pp. 1003–1011. Association for Computational Linguistics (2009)

  35. Mohammad, S.M., Kiritchenko, S., Zhu, X.: NRC-Canada: building the state-of-the-art in sentiment analysis of tweets. arXiv preprint arXiv:1308.6242 (2013)

  36. Morante, R., Blanco, E.: * SEM 2012 shared task: resolving the scope and focus of negation. In: * SEM 2012: The First Joint Conference on Lexical and Computational Semantics–Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation (SemEval 2012), pp. 265–274 (2012)

  37. Morsy, S.A., Rafea, A.: Improving document-level sentiment classification using contextual valence shifters. In: International conference on application of natural language to information systems, pp. 253–258. Springer (2012). https://doi.org/10.1007/978-3-642-31178-9_30

  38. Noferesti, S., Shamsfard, M.: Resource construction and evaluation for indirect opinion mining of drug reviews. PloS one 10(5), e0124993 (2015). https://doi.org/10.1371/journal.pone.0124993

    Article  Google Scholar 

  39. Noferesti, S., Shamsfard, M.: Using data mining techniques for sentiment shifter identification. In: Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016), pp. 2716–2720 (2016)

  40. Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10, pp. 79–86. Association for Computational Linguistics (2002). https://doi.org/10.3115/1118693.1118704

  41. Rahimi, Z., Noferesti, S., Shamsfard, M.: Applying data mining and machine learning techniques for sentiment shifter identification. Lang. Res. Eval. 53(2), 279–302 (2019). https://doi.org/10.1007/s10579-018-9432-0

    Article  Google Scholar 

  42. Saif, H., Fernandez, M., He, Y., Alani, H.: Evaluation datasets for twitter sentiment analysis. Emotion and Sentiment in Social and Expressive Media p. 9 (2013)

  43. Schulder, M., Wiegand, M., Ruppenhofer, J., Roth, B.: Towards bootstrapping a polarity Shifter Lexicon using linguistic features. In: Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 624–633 (2017)

  44. Toprak, C., Jakob, N., Gurevych, I.: Sentence and expression level annotation of opinions in user-generated discourse. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp. 575–584. Association for Computational Linguistics (2010)

  45. Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity: an exploration of features for phrase-level sentiment analysis. Comput. Linguist. 35(3), 399–433 (2009). https://doi.org/10.1162/coli.08-012-R1-06-90

    Article  Google Scholar 

  46. Wu, Y., Zhang, Q., Huang, X., Wu, L.: Phrase dependency parsing for opinion mining. In: Proceedings of the 2009 conference on empirical methods in natural language processing: Volume 3-volume 3, pp. 1533–1541. Association for Computational Linguistics (2009)

  47. Xia, R., Xu, F., Yu, J., Qi, Y., Cambria, E.: Polarity shift detection, elimination and ensemble: a three-stage model for document-level sentiment analysis. Inf. Process. Manag. 52(1), 36–45 (2016). https://doi.org/10.1016/j.ipm.2015.04.003

    Article  Google Scholar 

  48. Yu, H., Shang, J., Hsu, M., Castellanos, M., Han, J.: Data-driven contextual valence shifter quantification for multi-theme sentiment analysis. In: Proceedings of the 25th ACM international on conference on information and knowledge management, pp. 939–948. ACM (2016). https://doi.org/10.1145/2983323.2983793

  49. Yue, L., Chen, W., Li, X., Zuo, W., Yin, M.: A survey of sentiment analysis in social media. Knowl Inf Syst (2018). https://doi.org/10.1007/s10115-018-1236-4

    Article  Google Scholar 

  50. Zhang, X., Li, S., Zhou, G., Zhao, H.: Polarity shifting: Corpus construction and analysis. In: 2011 International Conference on Asian Language Processing, pp. 272–275. IEEE (2011). Publisher: IEEE Conference

  51. Zirpe, S., Joglekar, B.: Negation handling using stacking ensemble method. In: 2017 International Conference on Computing, Communication, Control and Automation (ICCUBEA), pp. 1–5. IEEE (2017). https://doi.org/10.1109/ICCUBEA.2017.8463946

Download references

Author information

Authors and Affiliations

  1. Faculty of Electrical and Computer Engineering, University of Sistan and Baluchestan, Zahedan, Iran

    Zahra Ayeste  & Samira Noferesti

Authors
  1. Zahra Ayeste
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Samira Noferesti
    View author publications

    You can also search for this author in PubMed Google Scholar

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ayeste , Z., Noferesti, S. A semantic approach based on domain knowledge for polarity shift detection using distant supervision. Prog Artif Intell 11, 169–180 (2022). https://doi.org/10.1007/s13748-021-00267-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13748-021-00267-x

Keywords

Search

Navigation