Marta De La Cuadra Lozano
ABSTRACT
This project introduces an innovative approach to user sentiment analysis and explainability, using a Natural Language Processing technique, Stanford’s CoreNLP sentiment analysis tool. The project accurately determines user sentiment utilizing a pre-labelled dataset, by integrating CoreNLP features. Emphasizing transparency and interpretability, it provides valuable insights into sentiment predictions and the factors influencing each classification.
- Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. 2011. Learning Word Vectors for Sentiment Analysis. In Proc. of the 49th Annual Meeting of the Assoc. for Comp. Linguistics: Human Language Technologies. Association for Computational Linguistics, Portland, Oregon, USA, 142–150. http://www.aclweb.org/anthology/P11-1015Google Scholar
Digital Library
- C D. Manning, Mihai Surdeanu, John Bauer, Jenny Finkel, Steven J. Bethard, and David McClosky. 2014. The Stanford CoreNLP NLP Toolkit. In Proc. of 52nd Annual Meeting of the Assoc. for Comp. Linguistics: System Demonstrations. 55–60.Google ScholarCross Ref
Index Terms
- Explainability in User Sentiment Analysis with CoreNLP
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