Abstract
With the exponential growth of critiques available on social media, the distinction between subjective (i.e., emotional terms) and objective (i.e., factual terms) information is a non-trivial natural language processing task. Therefore, we focus in this paper on the subjectivity/objectivity classification of social data precisely Facebook which is the most popular social network. The proposed approach is based on supervised machine learning techniques. Similar to any supervised application, our approach is composed of three main steps which are features extraction, training and prediction. Our main contribution in this work is to introduce new features such as ontology-based N-grams feature which represent good a indicator for predicting subjective text. Multiple classifiers like Naive Bayes, Support Vector Machine, Random Forest and Multi Layer Perceptron are applied on our data set. The experimental results show that the Random Forest model achieved the highest performance metrics 0.676 of accuracy, 0.697 of precision, 0,677 of recall and 0.683 off-score.
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References
Ortigosa, J.M., Carro, R.M.: Sentiment analysis in facebook and its application to e-learning. Comput. Hum. Behav. 31, 527–541 (2014)
Bruce, R.F., Wiebe, J.M.: Recognizing subjectivity: a case study in manual tagging. Nat. Lang. Eng. 5(2), 187–205 (1999)
Hatzivassiloglou, V., Wiebe, J.M.: Effects of adjective orientation and gradability on sentence subjectivity. In: Proceedings of the 18th Conference on Computational Linguistics - Volume 1, Stroudsburg, PA, USA, pp. 299–305 (2000)
Riloff, E., Wiebe, J., Wilson, T.: Learning subjective nouns using extraction pattern bootstrapping. In: Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003 - Volume 4, Stroudsburg, PA, USA, pp. 25–32 (2003)
Kamal, A.: Subjectivity classification using machine learning techniques for mining feature-opinion pairs from web opinion sources. CoRR, abs/1312.6962 (2013)
Chenlo, J.M., Losada, D.E.: An empirical study of sentence features for subjectivity and polarity classification. Inf. Sci. 280, 275–288 (2014)
Palshikar, D.G.K., Apte, M., Singh, V.: Learning to identify subjective sentences. In: Proceedings of the 13th International Conference on Natural Language Processing, pp. 239–248 (2016)
Barbosa, L., Feng, J.: Robust sentiment detection on Twitter from biased and noisy data. In: Proceedings of the 23rd International Conference on Computational Linguistics: Posters, Stroudsburg, PA, pp. 36–44 (2010)
Davidov, D., Tsur, O., Rappoport, A.: Enhanced sentiment learning using Twitter hashtags and smileys. In: Proceedings of the 23rd International Conference on Computational Linguistics, Posters, Stroudsburg, PA, USA, pp. 241–249 (2010)
Sixto, J., Almeida, A., López-de-Ipiña, D.: An approach to subjectivity detection on Twitter using the structured information. In: Nguyen, N.-T., Manolopoulos, Y., Iliadis, L., Trawiński, B. (eds.) ICCCI 2016. LNCS (LNAI), vol. 9875, pp. 121–130. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45243-2_11
Hajj, N., Rizk, Y., Awad, M.: A subjectivity classification framework for sports articles using improved cortical algorithms. Neural Comput. Appl. 31, 8069–8085 (2019). https://doi.org/10.1007/s00521-018-3549-3
Kontopoulos, E., Berberidis, C., Dergiades, T., Bassiliades, N.: Ontology-based sentiment analysis of Twitter posts. Expert Syst. Appl. 40(10), 4065–4074 (2013)
Baccianella, S., Esuli, A., Sebastiani, F.: SentiWordNet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: Proceedings of LREC, pp. 2200–2204 (2010)
Agarwal, A., Xie, B., Vovsha, I., Rambow, O., Passonneau, R.: Sentiment analysis of Twitter data. In: Proceedings of the Workshop on Languages in Social Media, Stroudsburg, PA, USA, pp. 30–38 (2011)
Jindal, N., Liu, B.: Identifying comparative sentences in text documents. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, New York, NY, USA, pp. 244–251 (2006)
Jünger, J., Keyling, T.: Facepager. An application for generic data retrieval through APIs. Source code and releases (2019). https://github.com/strohne/Facepager/
Wilson, T., et al.: OpinionFinder: a system for subjectivity analysis. In: Proceedings of HLT/EMNLP on Interactive Demonstrations, Stroudsburg, PA, USA, pp. 34–35 (2005)
Acknowledgment
The research leading to these results has received funding from the Ministry of Higher Education and Scientific Research of Tunisia under the grant agreement number LR11ES48.
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Chiha, R., Ben Ayed, M. (2020). Supervised Machine Learning Approach for Subjectivity/Objectivity Classification of Social Data. In: Themistocleous, M., Papadaki, M. (eds) Information Systems. EMCIS 2019. Lecture Notes in Business Information Processing, vol 381. Springer, Cham. https://doi.org/10.1007/978-3-030-44322-1_15
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