In this paper, we propose an extension to graph-based sentiment lexicon induction methods by incorporating distributed and semantic word representations in building the similarity graph to expand a three-dimensional sentiment lexicon. We also implemented and evaluated the label propagation using four different word representations and similarity metrics. Our comprehensive evaluation of the four approaches was performed on a single data set, demonstrating that all four methods can generate a significant number of new sentiment assignments with high accuracy. The highest correlations (tau=0.51) and the lowest error (mean absolute error < 1.1%), obtained by combining both the semantic and the distributional features, outperformed the distributional-based and semantic-based label-propagation models and approached a supervised algorithm.
@article{arxiv.1703.09825,
title = {Semi-Supervised Affective Meaning Lexicon Expansion Using Semantic and Distributed Word Representations},
author = {Areej Alhothali and Jesse Hoey},
journal= {arXiv preprint arXiv:1703.09825},
year = {2017}
}