Skip to main content
SpringerLink
Log in
Book cover

Machine Learning Paradigms: Theory and Application pp 189–210Cite as

Towards Objective-Dependent Performance Analysis on Online Sentiment Review

  • Chapter
  • First Online:

Part of the book series: Studies in Computational Intelligence ((SCI,volume 801))

Abstract

This chapter represents a new object dependent for online review evaluation for improving performance by a proposed performance criterion. This criterion can introduce an alternative solution for measuring sentiment accuracy. The problem illustrates in the accuracy comparison measurement between different sentiment techniques and frameworks. Each technique or framework targets one sentiment challenge or more. Another challenge appears in constructing database and its characteristics as memorability. For example, if two sentiment techniques are equal percentage of accuracy, the problem is that meaning they are achieved to the same classification, polarity and score. Is the sentiment challenge accuracy with 10% is bad? So, this study compares between several techniques based on proposed performance assessment. This assessment puts them in the same environment with respect three perspective. It is a new proposed criterion for performance measurement, which includes aggregation of performance measurement types: F-measure and Runtime with respect to speed of run time, memorability, and sentiment analysis challenge type. A comparison between several sentiment techniques is in movie domain in English language. It works on word-level sentiment analysis to measure the proposed performance criteria. It applies two experiments to evaluate the percentage degree of different techniques performance on measuring sentiments.

This is a preview of subscription content, 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   139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   179.99
Price excludes VAT (USA)
  • Durable hardcover 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

Learn about institutional subscriptions

References

  1. Medhat, W., Hassan, A., Korashy, H.: Sentiment analysis algorithms and applications: a survey. Ain Shams Eng. J. 5, 1093–1113 (2014). https://doi.org/10.1016/j.asej.2014.04.011

    Article  Google Scholar 

  2. Montoyo, A., Martínez-Barco, P., Balahur, A.: Subjectivity and sentiment analysis: an overview of the current state of the area and envisaged developments. Decis. Support Syst. 53, 675–679 (2012). https://doi.org/10.1016/j.dss.2012.05.022

    Article  Google Scholar 

  3. Parkhe, V., Biswas, B.: Aspect based sentiment analysis of movie reviews: finding the polarity directing aspects. In: International Conference on Soft Computing and Machine Intelligence, pp. 28–32. IEEE (2014)

    Google Scholar 

  4. Bird, S., Klein, E., Loper, E.: Natural Language Processing with Python. O’Reilly Media, Sebastopol (2009)

    MATH  Google Scholar 

  5. Hussein DME-DM: A survey on sentiment analysis challenges. J. King Saud Univ. Eng. Sci. (2016). https://doi.org/10.1016/j.jksues.2016.04.002

  6. Kiritchenko, S., Zhu, X., Mohammad, S.M.: Sentiment analysis of short informal texts. J. Artif. Intell. Res. 50, 723–762 (2014)

    Article  Google Scholar 

  7. Fu, G., Wang, X.: Chinese sentence-level sentiment classification based on fuzzy sets. In: Proceedings of the 23rd International Conference on Computational Linguistics: Posters. Association for Computational Linguistics, Stroudsburg, PA, USA, pp. 312–319 (2010)

    Google Scholar 

  8. Yumusak, S., Dogdu, E., Kodaz, H.: Tagging accuracy analysis on part-of-speech taggers. J. Comput. Commun. 2, 157–162 (2014). https://doi.org/10.4236/jcc.2014.24021

    Article  Google Scholar 

  9. Mohamed D.M.E.-D., El-Din M.H.N.: Performance analysis for sentiment techniques evaluation perspectives. In: Advances in Intelligent Systems and Computing, pp. 448–457 (2018). https://doi.org/10.1007/978-3-319-64861-3_42

  10. Angelova, G., Bontcheva, K., Mitkov, R.: Improving sentiment analysis in Twitter using multilingual machine translated data. In: Conference on Recent Advances in Natural Language Processing (RANLP), pp. 49–55. INCOMA Ltd., Hissar (2013)

    Google Scholar 

  11. Mohey, D.: Enhancement bag-of-words model for solving the challenges of sentiment analysis. Int. J. Adv. Comput. Sci. Appl. 7 (2016). https://doi.org/10.14569/ijacsa.2016.070134

  12. Liu, B.: Sentiment analysis and opinion mining. Synth. Lect. Hum. Lang. Technol. 5, 1–167 (2012). https://doi.org/10.2200/S00416ED1V01Y201204HLT016

    Article  Google Scholar 

  13. Feldman, R., Sanger, J.: Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data. Cambridge University Press, New York, NY, USA (2006)

    Book  Google Scholar 

  14. Isola, P., Parikh, D., Torralba, A., Oliva, A.: Understanding the intrinsic memorability of images. In: Proceedings of the 24th International Conference on Neural Information Processing Systems, pp. 2429–2437. Curran Associates Inc., USA (2011)

    Google Scholar 

  15. Cortis, K., Freitas, A., Daudert, T., et al.: SemEval-2017 task 5: fine-grained sentiment analysis on financial microblogs and news. In: Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pp. 519–535 (2017). https://doi.org/10.18653/v1/s17-2144

  16. Collomb, A., Costea, C., Joyeux, D., et al.: A study and comparison of sentiment analysis methods for reputation evaluation. Research report RR-LIRIS-2014-002 (2013)

    Google Scholar 

  17. Mohey, D., Mokhtar, H.M.O., Ismael, O.: Online paper review analysis. Int. J. Adv. Comput. Sci. Appl. 6 (2015). https://doi.org/10.14569/ijacsa.2015.060930

  18. Kaya, M., Conley, S.: Comparison of sentiment lexicon development techniques for event prediction. Soc. Netw. Anal. Min. 6, 7 (2016). https://doi.org/10.1007/s13278-015-0315-8

    Article  Google Scholar 

  19. 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—EMNLP ’02. Association for Computational Linguistics, Morristown, NJ, USA, pp. 79–86 (2002)

    Google Scholar 

  20. Pang, B., Lee, L.: A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics—ACL ’04. Association for Computational Linguistics, Morristown, NJ, USA, p. 271–es (2004)

    Google Scholar 

  21. Pang, B., Lee, L.: Seeing stars: exploiting class relationships for sentiment categorization with respect to rating scales. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics—ACL ’05. Association for Computational Linguistics, Morristown, NJ, USA, pp. 115–124 (2005)

    Google Scholar 

  22. Huangfu, Y., Wu, G., Su, Y., et al.: An improved sentiment analysis algorithm for Chinese news. In: 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), pp. 1366–1371. IEEE (2015)

    Google Scholar 

  23. Singh, V.K., Piryani, R., Uddin, A., Waila, P.: Sentiment analysis of movie reviews and blog posts. In: 3rd IEEE International Advance Computing Conference (IACC), pp. 893–898. IEEE (2013)

    Google Scholar 

  24. Sahu, T.P., Ahuja, S.: Sentiment analysis of movie reviews: a study on feature selection & classification algorithms. In: International Conference on Microelectronics, Computing and Communications (MicroCom), pp. 1–6. IEEE (2016)

    Google Scholar 

  25. Mandal, S., Gupta, S.: A Lexicon-based text classification model to analyse and predict sentiments from online reviews. In: International Conference on Computer, Electrical & Communication Engineering (ICCECE), pp. 1–7. IEEE (2016)

    Google Scholar 

  26. Rahmath, H.: Opinion mining and sentiment analysis—challenges and applications. Int. J. Appl. Innov. Engi. Manag. (IJAIEM) 3, 401–403 (2014)

    Google Scholar 

  27. Hatzivassiloglou, V., McKeown, K.R.: Predicting the semantic orientation of adjectives. In: Proceedings of the 35th Annual Meeting on Association for Computational Linguistics, pp. 174–181. Association for Computational Linguistics, Morristown, NJ, USA (1997)

    Google Scholar 

  28. Tang, H., Tan, S., Cheng, X.: A survey on sentiment detection of reviews. Expert Syst. Appl. 36, 10760–10773 (2009). https://doi.org/10.1016/j.eswa.2009.02.063

    Article  Google Scholar 

  29. Dhande, L.L., Patnaik, G.K.: Analyzing sentiment of movie review data using Naive Bayes neural classifier. Int. J. Emerg. Trends Technol. Comput. Sci. (IJETTCS) 3, 313–320 (2014)

    Google Scholar 

  30. Gamon, M.: Sentiment classification on customer feedback data. In: Proceedings of the 20th International Conference on Computational Linguistics—COLING ’04, pp. 841–847. Association for Computational Linguistics, Morristown, NJ, USA (2004)

    Google Scholar 

  31. Matsumoto, S., Takamura, H., Okumura, M.: Sentiment classification using word sub-sequences and dependency sub-trees. In: In: Advances in Knowledge Discovery and Data Mining. PAKDD 2005. Lecture Notes in Computer Science, pp. 301–311. Springer, Berlin, Heidelberg (2005)

    Google Scholar 

  32. Ng, V., Dasgupta, S., Arifin, S.M.N.: Examining the role of linguistic knowledge sources in the automatic identification and classification of reviews. In: Proceedings of the COLING/ACL on Main Conference Poster Sessions, pp. 611–618. Association for Computational Linguistics, Stroudsburg, PA, USA (2006)

    Google Scholar 

  33. Al Masum, S.M., Prendinger, H., Ishizuka, M.: SenseNet: a linguistic tool to visualize numerical-valence based sentiment of textual data. In: Proceedings of the International Conference on Natural Language Processing (ICON), pp 147–152 (2007)

    Google Scholar 

  34. 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 on—EACL ’09, pp 24–32. Association for Computational Linguistics, Morristown, NJ, USA (2009)

    Google Scholar 

  35. Socher, R., Perelygin, A., Wu, J., et al.: Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp 1631–1642 (2013)

    Google Scholar 

  36. Narayanan, V., Arora, I., Bhatia, A.: Fast and accurate sentiment classification using an enhanced Naive Bayes model. In: Yin, H., Tang, K., Gao, Y., et al. (eds.) Intelligent Data Engineering and Automated Learning—IDEAL 2013, pp. 194–201. Springer, Berlin, Heidelberg (2013)

    Chapter  Google Scholar 

  37. Cassani, G., Grimm, R., Gillis, S., Daelemans, W.: Constraining the search space in cross-situational word learning: different models make different predictions. In: Conference: Proceedings of the 38th Annual Conference of the Cognitive Science Society (2016)

    Google Scholar 

  38. Deshpande, R., Vaze, K., Rathod, S., Jarhad, T.: Comparative study of document similarity algorithms and clustering algorithms for sentiment analysis. Int. J. Emerg. Trends Technol. Comput. Sci. (IJETTCS) 3, 196–199 (2014)

    Google Scholar 

  39. Homan, J.V., Kovacs, P.J.: A comparison of the relational database model and the associative database model. Iss. Inf. Syst. X, 208–213 (2009)

    Google Scholar 

  40. Kouloumpis, E., Wilson, T., Moore, J.: Twitter sentiment analysis: the good the bad and the OMG! In: Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media, pp. 538–541 (2011)

    Google Scholar 

  41. Kushwanth Ram, K.S., Araballi, S., Shambhavi, B.R.: Sentiment analysis of Twitter data. Int. J. Adv. Res. Comput. Eng. Technol. 3, 4337–4342 (2014)

    Google Scholar 

  42. Paltoglou, G., Thelwall, M.: More than bag-of-words: sentence-based document representation for sentiment analysis. In: Proceedings of the International Conference Recent Advances in Natural Language Processing RANLP 2013, Hissar, Bulgaria, pp. 546–552 (2013)

    Google Scholar 

  43. Thet, T.T., Na, J.-C., Khoo, C.S.G.: Sentiment classification of movie reviews using multiple perspectives. In: Buchanan, G., Masoodian, M., Cunningham, S.J. (eds.) Digital Libraries: Universal and Ubiquitous Access to Information, pp. 184–193. Springer, Berlin, Heidelberg (2008)

    Chapter  Google Scholar 

  44. Chavan, S., Morwal, A., Patanwala, S., Janrao, P.: Sentiment analysis of movie ratings system. IOSR J. Comput. Eng. 43–47 (2017)

    Google Scholar 

  45. Kapukaranov, B., Nakov, P.: Fine-grained sentiment analysis for movie reviews in Bulgarian. In: Proceedings of Recent Advances in Natural Language Processing, pp. 266–274 (2015). https://doi.org/10.1126/science.aaa8685

Download references

Author information

Authors and Affiliations

  1. Faculty of Computers and Information, Cairo University, Giza, Egypt

    Doaa Mohey El-Din, Mohamed Hamed N. Taha & Nour Eldeen M. Khalifa

Authors
  1. Doaa Mohey El-Din
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohamed Hamed N. Taha
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nour Eldeen M. Khalifa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Doaa Mohey El-Din .

Editor information

Editors and Affiliations

  1. Faculty of Computers and Information, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

El-Din, D.M., Taha, M.H.N., Khalifa, N.E.M. (2019). Towards Objective-Dependent Performance Analysis on Online Sentiment Review. In: Hassanien, A. (eds) Machine Learning Paradigms: Theory and Application. Studies in Computational Intelligence, vol 801. Springer, Cham. https://doi.org/10.1007/978-3-030-02357-7_9

Download citation

Publish with us

Policies and ethics

Search

Navigation