Abstract
Emotion detection from text in social networks is an important tool for monitoring and analyzing discussions on social networks. However, the difficulty of the problem related to emotional analysis from texts is caused by the lack of additional features such as facial expressions or tone of voice, and the use of colloquial language and various signs and symbols in the texts contributes to misinterpretation of the content of the statement. For this reason, this article proposes an approach to automatically detect emotions from real text data from social networking sites and classify them into appropriate emotion categories. The aim of the article is to develop a model for detecting and classifying emotions through the use of natural language processing techniques and machine learning algorithms. In addition, it was decided to analyze the impact of the number of types of emotions on the classification results. Our research was tested for six classifiers (CART, SVM, AdaBoost, Bagging, Random Forest and K-NN) and three word weighting measures (TF, TF-IDF and Binary). Four measures of classification quality were used to evaluate the classifiers, i.e. accuracy, precision, recall and F1-score. Two datasets from different social networking sites were used for the research - one is a collection of comments from a social networking site and the other is tweets from Twitter. The analysis of the results confirmed that the proposed solution allows detecting emotions from the actual content of tweets, and the results obtained are better for a smaller number of emotion categories.
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References
Acheampong, F.A., Wenyu, C., Nunoo-Mensah, H.: Text-based emotion detection: advances, challenges, and opportunities. Eng. Rep. 2(7), e12189 (2020)
Agbehadji, I.E., Ijabadeniyi, A.: Approach to sentiment analysis and business communication on social media. In: Bio-inspired Algorithms for Data Streaming and Visualization, Big Data Management, and Fog Computing, pp. 169–193 (2021)
Arbieu, U., Helsper, K., Dadvar, M., Mueller, T., Niamir, A.: Natural language processing as a tool to evaluate emotions in conservation conflicts. Biol. Cons. 256, 109030 (2021)
Calvo, R.A., D’Mello, S.: Affect detection: an interdisciplinary review of models, methods, and their applications. IEEE Trans. Affect. Comput. 1(1), 18–37 (2010)
Cambria, E., White, B.: Jumping NLP curves: a review of natural language processing research [review article]. IEEE Comput. Intell. Mag. 9(2), 48–57 (2014). https://doi.org/10.1109/MCI.2014.2307227
Dash, P., Mishra, J., Dara, S.: Sentiment analysis on social network data and its marketing strategies: a review. ECS Trans. 107(1), 7417 (2022)
Garcia-Garcia, J.M., Penichet, V.M., Lozano, M.D.: Emotion detection: a technology review. In: Proceedings of the XVIII International Conference on Human Computer Interaction, pp. 1–8 (2017)
Gosai, D.D., Gohil, H.J., Jayswal, H.S.: A review on a emotion detection and recognization from text using natural language processing. Int. J. Appl. Eng. Res. 13(9), 6745–6750 (2018)
Kaur, J., Saini, J.R.: Emotion detection and sentiment analysis in text corpus: a differential study with informal and formal writing styles. Int. J. Comput. Appl. 101, 1–9 (2014). ISSN 0975–8887
Li, Q., et al.: A survey on text classification: from traditional to deep learning. ACM Trans. Intell. Syst. Technol. (TIST) 13(2), 1–41 (2022)
Madhuri, S., et al.: Detecting emotion from natural language text using hybrid and NLP pre-trained models. Turkish J. Comput. Math. Educ. (TURCOMAT) 12(10), 4095–4103 (2021)
Nandwani, P., Verma, R.: A review on sentiment analysis and emotion detection from text. Soc. Netw. Anal. Min. 11(1), 1–19 (2021). https://doi.org/10.1007/s13278-021-00776-6
Panicker, S.S., Gayathri, P.: A survey of machine learning techniques in physiology based mental stress detection systems. Biocybernet. Biomed. Eng. 39(2), 444–469 (2019)
Saffar, A.H., Mann, T.K., Ofoghi, B.: Textual emotion detection in health: advances and applications. J. Biomed. Inf. 137, 104258 (2022)
Santini, S., Schettini, R.: Internet imaging iv. Internet Imaging IV 5018 (2003)
Saxena, A., Khanna, A., Gupta, D.: Emotion recognition and detection methods: a comprehensive survey. J. Artif. Intell. Syst. 2(1), 53–79 (2020)
Zad, S., Heidari, M., James Jr., H., Uzuner, O.: Emotion detection of textual data: an interdisciplinary survey. In: 2021 IEEE World AI IoT Congress (AIIoT), pp. 0255–0261. IEEE (2021)
Zad, S., Heidari, M., Jones, J.H., Uzuner, O.: A survey on concept-level sentiment analysis techniques of textual data. In: 2021 IEEE World AI IoT Congress (AIIoT), pp. 0285–0291. IEEE (2021)
Zhang, T., Schoene, A.M., Ji, S., Ananiadou, S.: Natural language processing applied to mental illness detection: a narrative review. NPJ Dig. Med. 5(1), 46 (2022)
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Probierz, B., Kozak, J., Juszczuk, P. (2023). Emotion Detection from Text in Social Networks. In: Nguyen, N.T., et al. Intelligent Information and Database Systems. ACIIDS 2023. Lecture Notes in Computer Science(), vol 13995. Springer, Singapore. https://doi.org/10.1007/978-981-99-5834-4_29
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DOI: https://doi.org/10.1007/978-981-99-5834-4_29
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