Semi-Supervised Affective Meaning Lexicon Expansion Using Semantic and Distributed Word Representations
This work addresses sentiment analysis for NLP researchers by providing an incremental improvement in lexicon expansion methods.
The paper tackled the problem of expanding a three-dimensional sentiment lexicon by extending graph-based sentiment lexicon induction methods with semantic and distributed word representations, achieving the highest correlation (tau=0.51) and lowest error (mean absolute error < 1.1%) when combining both features.
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.