# Semi-Supervised Affective Meaning Lexicon Expansion Using Semantic and Distributed Word Representations

arXiv: https://arxiv.org/abs/1703.09825 · HTML: https://mlsanity.com/paper/1703.09825/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Mar 2017.

## Built on by (most cited first, 2 of 2)

- [How emoji and word embedding helps to unveil emotional transitions during online messaging](https://mlsanity.com/paper/2104.11032/) (2021)
- [Learning affective meanings that derives the social behavior using Bidirectional Encoder Representations from Transformers](https://mlsanity.com/paper/2202.00065/) (2022)

## Abstract (arXiv)

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.