DEVELOPING AN ALGORITHM FOR SENTIMENT ANALYSIS OF TEXTS BASED ON FACIAL EXPRESSION SYMBOLS (SMILEYS) IN SOCIAL NETWORKS
Keywords:
Sentiment analysis, facial expression symbols, smileys, social networks, machine learning, preprocessing, feature extraction.Abstract
Sentiment analysis is a crucial task in natural language processing that aims to determine the emotional tone or sentiment expressed in text. In social networks, users often utilize facial expression symbols, commonly known as smileys, to convey emotions. This paper proposes the development of an algorithm for sentiment analysis of texts based on smileys in social networks.The proposed algorithm offers a novel approach to sentiment analysis by leveraging facial expression symbols in social networks. By accurately identifying sentiments based on smileys, the algorithm can provide valuable insights into user emotions and opinions expressed in text data from social media platforms.
References
Zhang, Z., & Liu, B. (2010). Sentiment analysis of Chinese documents: From sentence to document level. Journal of the American Society for Information Science and Technology, 61(12), 2474-2487.
Agarwal, A., Xie, B., Vovsha, I., Rambow, O., & Passonneau, R. (2011). Sentiment analysis of Twitter data. In Proceedings of the Workshop on Languages in Social Media (LSM), 30-38.
Cambria, E., & Hussain, A. (2012). Sentic computing: Techniques, tools, and applications. Springer Science & Business Media.
Kiritchenko, S., & Mohammad, S. M. (2018). Examining Emoji Sentiment in Context: A Case Study of Twitter. In Proceedings of the 27th International Conference on Computational Linguistics (COLING), 2744-2755.
Novak, P. K., Smailović, J., Sluban, B., & Mozetič, I. (2015). Sentiment of Emojis. PloS One, 10(12), e0144296.
Hu, M., & Liu, B. (2004). Mining and summarizing customer reviews. In Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 168-177.
Zadeh, R., Mohammadi, E., Soleymani, M., & Jambor, T. (2017). Emojinet: A machine learning approach for analyzing and visualizing Emoji usage on Twitter. In Proceedings of the 26th International Conference on World Wide Web Companion, 1317-1324.
Jiang, L., Yu, M., Zhou, M., Liu, X., & Zhao, T. (2011). Target-dependent Twitter sentiment classification. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, 151-160.
Felbo, B., Mislove, A., Søgaard, A., Rahwan, I., & Lehmann, S. (2017). Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion, and sarcasm. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, 1615-1625.
Kouloumpis, E., Wilson, T., & Moore, J. D. (2011). Twitter sentiment analysis: The good the bad and the OMG! In Proceedings of the International Conference on Weblogs and Social Media (ICWSM), 538-541.
Gavrilovic, M., Kim, S. N., & Sankaranarayanan, J. (2017). Emoji as emotion tags for tweets. In Proceedings of the 26th International Conference on World Wide Web Companion, 167-168.
Barbieri, F., Ballesteros, M., & Saggion, H. (2014). Modelling emoji occurrence in tweets to improve sentiment analysis in a multilingual context. In Proceedings of the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, 92-98.
Kiritchenko, S., Zhu, X., & Mohammad, S. M. (2014). Sentiment analysis of short informal texts. Journal of Artificial Intelligence Research, 50, 723-762.
Li, X., & Lu, Q. (2016). Emotion analysis of Chinese microblogs with emoticons using deep learning. In Proceedings of the 12th International Conference on Computational Intelligence and Security, 74-77.
Abbasi, A., Chen, H., & Salem, A. (2008). Sentiment analysis in multiple languages: Feature selection for opinion classification in Web forums. ACM Transactions on Information Systems, 26(3), Article 12.
