From Semantic to Emotional Space in Probabilistic Sense Sentiment Analysis

Authors

  • Mitra Mohtarami National University of Singapore
  • Man Lan Institute for Infocomm Research
  • Chew Lim Tan National University of Singapore

DOI:

https://doi.org/10.1609/aaai.v27i1.8699

Keywords:

Sentiment Analysis, Sense Sentiment Similarity, Emotional Vectors

Abstract

This paper proposes an effective approach to model the emotional space of words to infer their Sense Sentiment Similarity (SSS). SSS reflects the distance between the words regarding their senses and underlying sentiments. We propose a probabilistic approach that is built on a hidden emotional model in which the basic human emotions are considered as hidden. This leads to predict a vector of emotions for each sense of the words, and then to infer the sense sentiment similarity. The effectiveness of the proposed approach is investigated in two Natural Language Processing tasks: Indirect yes/no Question Answer Pairs Inference and Sentiment Orientation Prediction.

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Published

2013-06-30

How to Cite

Mohtarami, M., Lan, M., & Tan, C. L. (2013). From Semantic to Emotional Space in Probabilistic Sense Sentiment Analysis. Proceedings of the AAAI Conference on Artificial Intelligence, 27(1), 711-717. https://doi.org/10.1609/aaai.v27i1.8699