Abstract
We present the approach followed by INESC-ID in the SemEval 2015 Twitter Sentiment Analysis challenge, subtask E. The goal was to determine the strength of the association of Twitter terms with positive sentiment. Using two labeled lexicons, we trained a regression model to predict the sentiment polarity and intensity of words and phrases. Terms were represented as word embeddings induced in an unsupervised fashion from a corpus of tweets. Our system attained the top ranking submission, attesting the general adequacy of the proposed approach.
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CITATION STYLE
Amir, S., Astudillo, R. F., Ling, W., Martins, B., Silva, M., & Trancoso, I. (2015). INESC-ID: A Regression Model for Large Scale Twitter Sentiment Lexicon Induction. In SemEval 2015 - 9th International Workshop on Semantic Evaluation, co-located with the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2015 - Proceedings (pp. 613–618). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/s15-2102
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