Beyond Polarity: The Potential Applications and Impacts of Sentiment Analysis and Emotion Detection
Keywords:
Natural Language Processing, LSTM, RNN, Sentiment Analysis, Contextual Language, Emotion Detection, Deep learningAbstract
Opinion mining and emotion detection are two important techniques in natural language processing that have gained significant attention in recent years. Opinion mining is the process of identifying and extracting subjective information from text, such as opinions, attitudes, and emotions, while emotion detection is the process of identifying and extracting emotions from text. These techniques have a wide range of applications in various domains, including social media analysis, customer feedback analysis, and product reviews. This paper provides an overview of opinion mining and emotion detection techniques in natural language processing. We discuss the various approaches and methods used in opinion mining and emotion detection, including machine learning, deep learning, and natural language processing techniques. We also explore the challenges and limitations of these techniques, including the subjectivity of language, cultural differences, and the lack of labeled data. Furthermore, we examine the current state of the art in opinion mining and emotion detection, highlighting recent research and developments in these areas. We also discuss the potential applications of these techniques in various domains, including marketing, healthcare, and social media analysis. Overall, this paper provides a comprehensive overview of opinion mining and emotion detection in natural language processing. It provides insights into the methods, challenges, and potential applications of these techniques, and highlights the importance of these techniques in understanding and analyzing subjective information in text.
References
Colin Raffe, Noam Shazeer, et al., “Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer”, Journal of Machine Learning Research, vol. 21, pp 1-67, 2020.
Heinsen, Franz A. "An algorithm for routing vectors in sequences." arXiv preprint arXiv:2211.11754, 2022.
Ionescu, R. T., & Butnaru, A. M., "Vector of locally-aggregated word embeddings (vlawe): A novel document-level representation", arXiv preprint arXiv:1902.08850, 2019.
Yang, Zhilin, et al. "Xlnet: Generalized autoregressive pretraining for language understanding.", Advances in neural information processing systems, vol. 32, 2019.
Lee-Thorp, James, et al. "Fnet: Mixing tokens with fourier transforms." arXiv preprint arXiv:2105.03824, 2021.
Abaskohi, Amirhossein, Sascha Rothe, and Yadollah Yaghoobzadeh. "LM-CPPF: Paraphrasing-Guided Data Augmentation for Contrastive Prompt-Based Few-Shot Fine-Tuning." arXiv preprint arXiv:2305.18169, 2023.
Pang, Bo, and Lillian Lee. "Foundations and Trends® in information retrieval." Foundations and Trends® in Information Retrieval, vol. 2, no. 1-2, pp 1-135, 2008.
Mohammad, Saif, and Peter Turney. "Emotions evoked by common words and phrases: Using mechanical turk to create an emotion lexicon." Proceedings of the NAACL HLT 2010 workshop on computational approaches to analysis and generation of emotion in text, pp 26-34, 2010.
Li, Y., Xu, X., Li, Y., & Xu, X, "Attention-based convolutional neural network for sentiment analysis". IEEE Access, vol. 7, pp 17544-17551, 2019.
Cambria, E., & Hussain, A., "Opinion mining and sentiment analysis". IEEE Intelligent Systems, vol. 27, no. 6, pp 46-59, 2012.
Wang, Y., Huang, M., Zhu, X., & Zhao, L, "Attention-based lstm for aspect-level sentiment classification", Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp 606-615, 2017.
Tang, D., Wei, F., Yang, N., Zhou, M., Liu, T., & Qin, B.,"Learning sentiment-specific word embedding for twitter sentiment classification", Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp 1555-1565, 2014.
Schuller, B., Batliner, A., & Burkhardt, F., "Computational paralinguistics: Emotion, affect and personality in speech and language processing", John Wiley & Sons, 2013.
Liu, B., "Sentiment analysis and opinion mining", Synthesis Lectures on Human Language Technologies, vol. 5, no. 1, pp 1-167, 2012.
Medhat, W., Hassan, A., & Korashy, H., "Sentiment analysis algorithms and applications: A survey", Ain Shams Engineering Journal, vol. 5, no. 4, pp 1093-1113, 2014.
Chilkuri, Narsimha Reddy, and Chris Eliasmith. "Parallelizing legendre memory unit training." International Conference on Machine Learning. PMLR, pp 1898-1907, 2021.
Rusch, T. Konstantin, and Siddhartha Mishra. "Unicornn: A recurrent model for learning very long time dependencies." International Conference on Machine Learning. PMLR, pp 9168-9178, 2021.
Rusch, T. Konstantin, and Siddhartha Mishra. "Coupled Oscillatory Recurrent Neural Network (coRNN): An accurate and (gradient) stable architecture for learning long time dependencies." arXiv preprint arXiv:2010.00951, 2020.
Li, Xianming, and Jing Li. "Angle-optimized text embeddings." arXiv preprint arXiv:2309.12871, 2023.
