TY - GEN
T1 - Explainability in User Sentiment Analysis with CoreNLP
AU - De La Cuadra Lozano, Marta
AU - Jaiswal, Rajesh
AU - Perez Tellez, Fernando
N1 - Publisher Copyright:
© 2023 Owner/Author.
PY - 2023/12/14
Y1 - 2023/12/14
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85183327107
U2 - 10.1145/3633083.3633226
DO - 10.1145/3633083.3633226
M3 - Conference contribution
AN - SCOPUS:85183327107
T3 - ACM International Conference Proceeding Series
SP - 55
BT - HCAIep 2023 - Proceedings of the 2023 Conference on Human Centered Artificial Intelligence - Education and Practice
PB - Association for Computing Machinery (ACM)
T2 - 2023 Conference on Human Centered Artificial Intelligence - Education and Practice, HCAIep 2023
Y2 - 15 December 2023
ER -