Skip to main content
Log in

TextJSM: Text Sentiment Analysis Method

  • Information Analysis
  • Published:
Automatic Documentation and Mathematical Linguistics Aims and scope

Abstract

The TextJSM method of text sentiment analysis is proposed, based on JSM method of automated hypothesis generation. Two versions of the TextJSM method are presented, that is, for solving predictive and descriptive problems. Parallel implementation of the main stages of both versions is considered. Experimental studies based on the ROMIP 2011–2012 seminar text corpora show the superiority of the developed method over other data mining methods.

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

Access this article

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Liu, B., Sentiment analysis and opinion mining, Synth. Lect. Hum. Lang. Technol., 2012, vol. 5, no.1.

    Google Scholar 

  2. Mining Text Data, Aggarwal, C. and Zhai, C., Eds., Springer, 2012.

  3. Witten, I.H., Frank, E., Hall, M.A., and Pal, C.J., Data Mining, Practical Machine Learning Tools and Techniques, Elsevier, 2017, 4th ed.

    Google Scholar 

  4. Finn, V.K., Epistemological foundations of the JSM method for automatic hypothesis generation, Autom. Doc. Math. Linguist., 2014, vol. 48, no. 2, pp. 96–148.

    Article  Google Scholar 

  5. Smirnov, I.V., Investigation of methods for establishing the values of syntactic units of natural languages on the basis of intellectual data analysis, Extended Abstract of Cand. Sci. (Phys.-Math.) Dissertation, Moscow: Institute of System Analysis, Russian Academy of Sciences, 2008.

    Google Scholar 

  6. Kozhunova, O.S., Technology of the development of the semantic dictionary of the information monitoring system, Extended Abstract of Cand. Sci. (Phys.-Math.) Dissertation, Moscow: Institute of Informatics Problems, Russian Academy of Sciences, 2009.

    Google Scholar 

  7. Lyfenko, N.D., An approach to text data categorization based on the ideas of J.S. Mill, Autom. Doc. Math. Linguist., 2015, vol. 49, no. 6, pp. 202–212.

    Article  Google Scholar 

  8. Vychegzhanin, S.V. and Kotelnikov, E.V., Analysis of the influence of models of the presentation of texts for the quality of the classification of feedback by tonality, Fundam. Issled., 2015, no. 11, pp. 247–251.

    Google Scholar 

  9. Zabezhailo, M.I., Some capabilities of enumeration control in the JSM method, Sci. Tech. Inf. Process., 2014, vol. 41, no. 6, pp. 335–361.

    Article  Google Scholar 

  10. Vinogradov, D.V., The VKF method of data mining: Review of results and open problems, Iskusstv. Intell. Prinyatie Reshenii, 2017, no. 2, pp. 9–16.

    Google Scholar 

  11. Ol'shanskii, D.L., Selection of an algorithm for the parallel implementation of the similarity method in intelligent JSM systems, Autom. Doc. Math. Linguist., 2015, vol. 49, no. 4, pp. 109–116.

    Article  MathSciNet  Google Scholar 

  12. Kotelnikov, E.V., Increasing the JSM method rate in text processing problems, Trudy Chetyrnadtsatoi natsional’noi konferentsii po iskusstvennomu intellektu s mezhdunarodnym uchastiem KII-2014 (24–27 oktyabrya 2014 goda, g. Kazan’ (Proc. Fourteenth Natl. Conf. on Artificial Intelligence with International Participation KII-2014 (October 24–27, 2014, Kazan)), Kazan, 2014, vol. 2, pp. 274–282.

    Google Scholar 

  13. Anshakov, O.M., The JSM method: A set-theoretical explanation, Autom. Doc. Math. Linguist., 2012, vol. 46, no. 5, pp. 202–220.

    Article  Google Scholar 

  14. Kuznetsov, S.O., Interpretation on graphs and complexity characteristics of problems of finding patterns of a certain type, Nauchno-Tekh. Inf., Ser. 2, 1989, no.1.

  15. Andrews, S., A 'Best-of-Breed’ approach for designing a fast algorithm for computing fixpoints of Galois Connections, Inf. Sci., 2015, vol. 295, no. 20, pp. 633–649.

    Article  MathSciNet  MATH  Google Scholar 

  16. Kotelnikov, E.V., Bushmeleva, N.A., Razova, E.V., Peskisheva, T.A., and Pletneva, M.V., Manually created sentiment lexicons: Research and development, Computational Linguistics and Intellectual Technologies: Papers from the Annual International Conference “Dialogue,” 2016, vol. 15, no. 22, pp. 281–295.

    Google Scholar 

  17. Kotelnikov, E.V., A finite automaton for primary text analysis in the tonality recognition problem, XVI Mezhdunarodnaya konferentsiya “Informatika: Problemy, metodologiya, tekhnologii” (11–12 fevralya 2016 goda) (XVI Int. Conf. Computer Science: Problems, Methodology, and Technologies (February 11–12, 2016)), 2016, vol. 4, pp. 208–211.

    Google Scholar 

  18. Segalovich, I., A fast morphological algorithm with unknown word guessing induced by a dictionary for a web search engine, Proceedings of MLMTA-2003, pp. 273–280.

  19. Lan, M., Tan, C.L., Su, J., and Lu, Y., Supervised and traditional term weighting methods for automatic text categorization, IEEE Trans. Pattern Anal. Mach. Intell., 2009, vol. 31, no. 4, pp. 721–735.

    Article  Google Scholar 

  20. Kotelnikov, E.V. and Klekovkina, M.V., Automatic analysis of the tonality of texts based on machine learning methods, Komp’yuternaya lingvistika i intellektual’nye tekhnologii: Po materialam ezhegodnoi mezhdunar. konf. “Dialog” (Computer Linguistics and Intellectual Technologies: Proc. Annual International Conference Dialog), Moscow, 2012, vol. 2, pp. 27–36.

    Google Scholar 

  21. Dhillon, I.S., Co-clustering documents and words using bipartite spectral graph partitioning, Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2001), 2001, pp. 269–274.

    Google Scholar 

  22. Kotelnikov, E.V. and Pletneva, M.V., Text sentiment classification based on genetic algorithm and word and document co-clustering, J. Comput. Sys. Sc. Int., 2016, vol. 55, no. 1, pp. 106–114.

    Article  MATH  Google Scholar 

  23. Kotelnikov, E.V., The function of estimating the informativeness of hypotheses for the analysis of the tonality of texts on the basis of the JSM-method, Fundam. Issled., 2014, no. 11, pp. 2150–2154.

    Google Scholar 

  24. Manning, C.D., Raghavan, P., and Schütze, H., Introduction to Information Retrieval, Cambridge University Press, 2008.

    Book  MATH  Google Scholar 

  25. Kotelnikov, E.V., Abductive logic reasoning for text tonality analysis based on the JSM method, Fund. Issled., 2015, no. 2, pp. 2801–2805.

    Google Scholar 

  26. Karpov, V.E., Introduction to parallelization of algorithms and programs, Komp’yut. Issled. Model., 2010, vol. 2, no. 3, pp. 231–272.

    Google Scholar 

  27. Intel Math Kernel Library (Intel MKL). https://software.intel.com/en-us/mkl. Accessed October 12, 2017.

  28. Kodagoda, N., Andrews, S., and Pulasinghe, K., A parallel version of the in-close algorithm, Proceedings of the 6th National Conference on Technology and Management (NCTM). IEEE, 2017, pp. 1–5.

    Google Scholar 

  29. Krajca, P., Outrata, J., and Vychodil, V., Advances in algorithms based on CbO, Proc. 8th Int. Conf. Concept Lattices and Their Applications (CLA), 2010, vol. 672, pp. 325–337.

    Google Scholar 

  30. Rajput, I.S., Kumar, B., and Singh, T., Performance comparison of sequential quick sort and parallel quick sort algorithms, Int. J. Comput. Appl., 2012, vol. 57, no. 9, pp. 14–22.

    Google Scholar 

  31. Russian Seminar on the Evaluation of Methods of Information Retrieval (ROMIP). http://romip.ru. Accessed October 12, 2017.

  32. Chetviorkin, I., Braslavskiy, P., and Loukachevitch, N., Sentiment Analysis Track at ROMIP 2011, Computational Linguistics and Intellectual Technologies: Papers from the Annual International Conference Dialogue, 2012, vol. 2, no. 11, pp. 1–14.

    Google Scholar 

  33. Chetviorkin, I. and Loukachevitch, N., Sentiment Analysis Track at ROMIP 2012, Computational Linguistics and Intellectual Technologies: Proceedings of the International Conference “Dialog 2013,” Bekasovo, 2013, vol. 2, no. 12, pp. 40–50.

    Google Scholar 

  34. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, É., Scikit-learn: Machine learning in Python, J. Mach. Learn. Res., 2011, vol. 12, pp. 2825–2830.

    MathSciNet  MATH  Google Scholar 

  35. Grama, A., Gupta, A., Karypis, G., and Kumar, V., Introduction to Parallel Computing, Addison-Wesley, 2003, 2nd ed.

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to E. V. Kotelnikov.

Additional information

Original Russian Text © E.V. Kotelnikov, 2018, published in Nauchno-Tekhnicheskaya Informatsiya, Seriya 2: Informatsionnye Protsessy i Sistemy, 2018, No. 2, pp. 8–20.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kotelnikov, E.V. TextJSM: Text Sentiment Analysis Method. Autom. Doc. Math. Linguist. 52, 24–34 (2018). https://doi.org/10.3103/S0005105518010089

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S0005105518010089

Keywords

Navigation