Speaker: Areej Alhothali, PhD Candidate
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 present a comprehensive evaluation of four graph-based propagation approaches using different word representations and similarity metrics. This evaluation 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.