Abstract
Human emotion analysis has always stimulated studies in different disciplines, such as Cognitive Sciences, Psychology, and thanks to the diffusion of the social media, it is attracting the interests of computer scientists too. Particularly, the growing popularity of Microblogging platforms, has generated large amounts of information which in turn represent an attractive source of data to be further subjected to opinion mining and sentiment analysis. In our research, we leverage the analysis performed on micro-blogging texts and postings in Albanian language, which enables the use of technologies to monitor and follow the feelings and perception of the people with respect to products, issues, events, etc. Our approach to emotion analysis tackles the problem of classifying a text fragment into a set of pre-defined emotion categories and therefore aims at detecting the emotional state of the writer conveyed through the text. In order to achieve this goal, we perform a comparative analysis between different classifiers, using deep learning and other classical machine learning classification algorithms. We also adopt a domestic stemming tool for Albanian language in order to preprocess the datasets used in a second round of experiments. Experimental evaluation shows that deep learning produces overall better results compared with the other methods in terms of classification accuracy. We present also other findings related to the length of the texts being processed and the impact on the classifiers’ accuracy.
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We do not report their names due to privacy reasons.
References
Agrawal, A., An, A.: Unsupervised emotion detection from text using semantic and syntactic relations. In: 2012 IEEE/WIC/ACM International Conferences on Web Intelligence, WI 2012, Macau, China, 4–7 December 2012, pp. 346–353 (2012). https://doi.org/10.1109/WI-IAT.2012.170
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/s12559-017-9503-3
Beci, B.: Gramatika e gjuhes shqipe. Publisher, Logos-A (2005)
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_20
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)
Das, D., Bandyopadhyay, S.: Sentence-level emotion and valence tagging. Cogn. Comput. 4(4), 420–435 (2012). https://doi.org/10.1007/s12559-012-9173-0
Ekman, P.: Facial expression and emotion. Am. Psychol. 48(4), 384–392 (1993). https://doi.org/10.1037/0003-066X.48.4.384
Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, 25–29 October 2014, Doha, Qatar, pp. 1746–1751 (2014)
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)
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)
Loglisci, C.: Time-based discovery in biomedical literature: mining temporal links. IJDATS 5(2), 148–174 (2013). https://doi.org/10.1504/IJDATS.2013.053679
Loglisci, C., Ceci, M., Malerba, D.: Relational mining for discovering changes in evolving networks. Neurocomputing 150, 265–288 (2015). https://doi.org/10.1016/j.neucom.2014.08.079
Loglisci, C., Ienco, D., Roche, M., Teisseire, M., Malerba, D.: Toward geographic information harvesting: Extraction of spatial relational facts from web documents. In: 12th IEEE International Conference on Data Mining Workshops, ICDM Workshops, Brussels, Belgium, 10 December 2012, pp. 789–796 (2012). https://doi.org/10.1109/ICDMW.2012.20
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.12024
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
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
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, 2009, pp. 1–7 (2009). https://doi.org/10.1109/NLPKE.2009.5313834
Sadiku, J., Biba, M.: Automatic stemming of albanian through a rule-based approach. J. Int. Res. Publ. Lang. Individuals Soc. 6 (2012)
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. AISC, vol. 366, pp. 707–713. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-08422-0_100
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
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
Acknowledgments
This work has been partially supported by the Apulia Regional Government through the project “Computer-mediated collaboration in creative projects” (8GPS5R0) collocated in“Intervento cofinanziato dal Fondo di Sviluppo e Coesione 2007–2013 – APQ Ricerca Regione Puglia - Programma regionale a sostegno della specializzazione intelligente e della sostenibilita’ sociale ed ambientale - FutureInResearch”.
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Skenduli, M.P., Biba, M., Loglisci, C., Ceci, M., Malerba, D. (2018). User-Emotion Detection Through Sentence-Based Classification Using Deep Learning: A Case-Study with Microblogs in Albanian. In: Ceci, M., Japkowicz, N., Liu, J., Papadopoulos, G., Raś, Z. (eds) Foundations of Intelligent Systems. ISMIS 2018. Lecture Notes in Computer Science(), vol 11177. Springer, Cham. https://doi.org/10.1007/978-3-030-01851-1_25
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