Construction of a Multi-dimensional Vectorized Affective Lexicon

Abstract

Affective analysis has received growing attention from both research community and industry. However, previous works either cannot express the complex and compound states of human’s feelings or rely heavily on manual intervention. In this paper, by adopting Plutchik’s wheel of emotions, we propose a lowcost construction method that utilizes word embeddings and high-quality small seed-sets of affective words to generate multi-dimensional affective vector automatically. And a large-scale affective lexicon is constructed as a verification, which could map each word to a vector in the affective space. Meanwhile, the construction procedure uses little supervision or manual intervention, and could learn affective knowledge from huge amount of raw corpus automatically. Experimental results on affective classification task and contextual polarity disambiguation task demonstrate that the proposed affective lexicon outperforms other state-of-the-art affective lexicons.