Advertisement

Classification and Clustering of Emotive Microblogs in Albanian: Two User-Oriented Tasks

  • Marjana Prifti SkenduliEmail author
  • Marenglen Biba
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 880)

Abstract

Human emotion analysis has continuously stimulated studies in different disciplines and it is spurring interest among the computer scientists too. Particularly, the growing popularity of Micro-blogging platforms, has generated large amounts of data, which in turn represent an attractive source to study social media users, especially in user-generated content analysis, such as opinion mining and sentiment analysis. In this paper, we propose to analyze micro-blogging content in order to characterize the users individually when writing posts with emotional content. The analysis is two-fold and considers the emotional content at different granularity levels, one refers to the textual units and allows us to capture the emotional state expressed by the user, the other one refers to the collections of textual units and allows us to summarize the lexicon used by the user. In particular, in the first case, we focus on a sentence-based emotion detection problem, aimed at classifying the textual units into a set of pre-defined emotion categories. The second analysis is performed through a keyword extraction approach, aimed at finding representative generic word sets in the form of prototypes of textual unit clusters. Extensive experiments conducted under different perspectives, yet always centered around the user, reveal interesting findings in terms of classification accuracy, clustering incoherence versus classifications perspectives and valuable efforts in user emotion profiling.

References

  1. 1.
    Abilhoa, W.D., de Castro, L.N.: A keyword extraction method from twitter messages represented as graphs. Appl. Math. Comput. 240, 308–325 (2014).  https://doi.org/10.1016/j.amc.2014.04.090CrossRefGoogle Scholar
  2. 2.
    Anand, D., Mampilli, B.S.: User profiling based on keyword clusters for improved recommendations. In: Natarajan, R. (ed.) Distributed Computing and Internet Technology, pp. 176–187. Springer, Cham (2014)CrossRefGoogle Scholar
  3. 3.
    Asghar, M.Z., Khan, A., Bibi, A., Kundi, F.M., Ahmad, H.: Sentence-level emotion detection framework using rule-based classification. Cogn. Comput. 9(6), 868–894 (2017).  https://doi.org/10.1007/s12559-017-9503-3CrossRefGoogle Scholar
  4. 4.
    Beci, B.: Gramatika e gjuhes shqipe. Shkup, Logos-A (2005)Google Scholar
  5. 5.
    Biba, M., Mane, M.: Sentiment analysis through machine learning: an experimental evaluation for albanian. In: Proceedings of the Second International Symposium on Intelligent Informatics, ISI 2013, India, pp. 195–203 (2013).  https://doi.org/10.1007/978-3-319-01778-5_20Google Scholar
  6. 6.
    Bordoloi, M., Biswas, S.K.: Keyword extraction from micro-blogs using collective weight. Soc. Netw. Anal. Min. 8(1), 58:1–58:16 (2018).  https://doi.org/10.1007/s13278-018-0536-8
  7. 7.
    Ceci, M., Loglisci, C., Macchia, L.: Ranking sentences for keyphrase extraction: a relational data mining approach. IRCDL 38, 52–59 (2014).  https://doi.org/10.1016/j.procs.2014.10.011CrossRefGoogle Scholar
  8. 8.
    Das, D., Bandyopadhyay, S.: Sentence to document level emotion tagging a coarse-grained study on bengali blogs. In: Advances in Pattern Recognition—Second Mexican Conference on Pattern Recognition, Mexico, pp. 332–341 (2010)Google Scholar
  9. 9.
    Das, D., Bandyopadhyay, S.: Sentence-level emotion and valence tagging. Cogn. Comput. 4(4), 420–435 (2012).  https://doi.org/10.1007/s12559-012-9173-0CrossRefGoogle Scholar
  10. 10.
    Ekman, P.: Facial expression and emotion. Am. Psychol. 48(4), 384–392 (1993).  https://doi.org/10.1037/0003-066X.48.4.384CrossRefGoogle Scholar
  11. 11.
    Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, October 25–29, 2014, Doha, Qatar, pp. 1746–1751 (2014)Google Scholar
  12. 12.
    Li, Q., Shah, S., Liu, X., Nourbakhsh, A.: Data sets: word embeddings learned from tweets and general data. In: Proceedings of the Eleventh International Conference on Web and Social Media, ICWSM 2017, Canada, 2017, pp. 428–436 (2017)Google Scholar
  13. 13.
    Li, S., Huang, L., Wang, R., Zhou, G.: Sentence-level emotion classification with label and context dependence. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics, ACL 2015, China, pp. 1045–1053 (2015)Google Scholar
  14. 14.
    Loglisci, C., Andresini, G., Impedovo, A., Malerba, D.: Analyzing microblogging posts for tracking collective emotional trajectories. In: Proceedings of the AI*IA 2018—Advances in Artificial Intelligence—XVIIth International Conference of the Italian Association for Artificial Intelligence, Trento, Italy, November 20–23, 2018, pp. 123–135 (2018).  https://doi.org/10.1007/978-3-030-03840-3_10CrossRefGoogle Scholar
  15. 15.
    Loglisci, C., Ceci, M., Malerba, D.: Discovering evolution chains in dynamic networks. In: Appice, A., Ceci, M., Loglisci, C., Manco, G., Masciari, E., Ras, Z.W. (eds.) New Frontiers in Mining Complex Patterns—First International Workshop, NFMCP 2012, Held in Conjunction with ECML/PKDD 2012, Bristol, UK, September 24, 2012, Revised Selected Papers. Lecture Notes in Computer Science, vol. 7765, pp. 185–199. Springer, Berlin (2012).  https://doi.org/10.1007/978-3-642-37382-4_13CrossRefGoogle Scholar
  16. 16.
    Mohammad, S.M., Kiritchenko, S.: Using hashtags to capture fine emotion categories from tweets. Comput. Intell. 31(2), 301–326 (2015).  https://doi.org/10.1111/coin.12024MathSciNetCrossRefGoogle Scholar
  17. 17.
    Oleri, O., Karagoz, P.: Detecting user emotions in twitter through collective classification. In: Proceedings of the 8th International Joint Conference on Knowledge Discovery, Portugal, 2016, pp. 205–212 (2016).  https://doi.org/10.5220/0006037502050212
  18. 18.
    Piton, O., Lagji, K., Përnaska, R.: Electronic dictionaries and transducers for automatic processing of the albanian language. In: 12th International Conference on Applications of Natural Language to Information Systems, NLDB 2007, France, 2007, pp. 407–413 (2007).  https://doi.org/10.1007/978-3-540-73351-5_38
  19. 19.
    Quan, C., Ren, F.: Recognizing sentence emotions based on polynomial kernel method using Ren-CECps. In: Proceedings of the 5th International Conference on Natural Language Processing and Knowledge Engineering, NLPKE 2009, China, pp. 1–7 (2009).  https://doi.org/10.1109/NLPKE.2009.5313834
  20. 20.
    Sadiku, J., Biba, M.: Automatic stemming of albanian through a rule-based approach. In: Journal of International, Research Publications: Language, Individuals and Society, vol. 6 (2012)Google Scholar
  21. 21.
    Shaila, S.G., Vadivel, A.: Cognitive based sentence level emotion estimation through emotional expressions. In: Selvaraj, H., Zydek, D., Chmaj, G. (eds.) Progress in Systems Engineering, pp. 707–713. Springer, Berlin (2015)CrossRefGoogle Scholar
  22. 22.
    Tang, X., Zeng, Q.: Keyword clustering for user interest profiling refinement within paper recommender systems (Dynamic Analysis and Testing of Embedded Software). J. Syst. Softw. 85(1), 87–101 (2012).  https://doi.org/10.1016/j.jss.2011.07.029, http://www.sciencedirect.com/science/article/pii/S0164121211001981CrossRefGoogle Scholar
  23. 23.
    Wang, J., Li, S., Jiang, M., Wu, H., Zhou, G.: Cross-media user profiling with joint textual and social user embedding. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, COLING 2018, Santa Fe, New Mexico, USA, August 20–26, 2018, pp, 1410–1420. Association for Computational Linguistics (2018), https://aclanthology.info/volumes/proceedings-of-the-27th-international-conference-on-computational-linguistics
  24. 24.
    Wilks, D.: Chapter 15—cluster analysis. In: Wilks, D.S. (ed.) Statistical Methods in the Atmospheric Sciences, International Geophysics, vol. 100, pp. 603–616. Academic Press, Cambridge (2011). http://www.sciencedirect.com/science/article/pii/B9780123850225000154Google Scholar
  25. 25.
    Williams, G., Mahmoud, A.: Analyzing, classifying, and interpreting emotions in software users’ tweets. In: 2nd IEEE/ACM International Workshop on Emotion Awareness in Software Engineering, Argentina, 2017, pp. 2–7 (2017).  https://doi.org/10.1109/SEmotion.2017.1
  26. 26.
    Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques (The Morgan Kaufmann Series in Data Management Systems), 2nd edn. Morgan Kaufmann, Burlington (2005)Google Scholar
  27. 27.
    Wu, X., Kumar, V., Ross Quinlan, J., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G.J., Ng, A., Liu, B., Yu, P.S., Zhou, Z.H., Steinbach, M., Hand, D.J., Steinberg, D.: Top 10 algorithms in data mining. Knowl. Inf. Syst. 14(1), 1–37 (2008).  https://doi.org/10.1007/s10115-007-0114-2CrossRefGoogle Scholar
  28. 28.
    Xu, J., Xu, R., Lu, Q., Wang, X.: Coarse-to-fine sentence-level emotion classification based on the intra-sentence features and sentential context. In: 21st ACM International Conference on Information and Knowledge Management, USA, pp. 2455–2458 (2012).  https://doi.org/10.1145/2396761.2398665
  29. 29.
    Zhao, D., Du, N., Chang, Z., Li, Y.: Keyword extraction for social media short text. In: 14th Web Information Systems and Applications Conference, WISA 2017, Liuzhou, Guangxi Province, China, November 11–12, 2017, pp. 251–256 (2017).  https://doi.org/10.1109/WISA.2017.12
  30. 30.
    Zhou, Q., Zhang, C.: Emotion evolutions of sub-topics about popular events on microblogs. Electron. Libr. 35(4), 770–782 (2017).  https://doi.org/10.1108/EL-09-2016-0184CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.University of New York TiranaTiranaAlbania

Personalised recommendations