Concentration Areas of Sentiment Lexica in the Word Embedding Space

Concentration Areas of Sentiment Lexica in the Word Embedding Space

Elena Razova, Evgeny Kotelnikov
Copyright: © 2019 |Pages: 15
DOI: 10.4018/IJCINI.2019040104
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Abstract

Sentiment lexicons play an important role in opinion mining systems and cognitive linguistics. Previous work aimed mostly at creating sentiment lexicons, but not thorough research into their fundamental properties. In this paper the arrangement of sentiment lexica in the multidimensional space of distributed word representations is studied. A hypothesis on the existence of sentiment lexica concentration areas is introduced and it is tested on the basis of the joint analysis of the distribution of sentiment words and general lexica. The results of the test allow to confirm the proposed hypothesis and discover the words which more than 80% of the sentiment lexica is concentrated around.
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In papers dedicated to the joint application of sentiment lexicons and distributed word representations, there are two main directions: creating dictionaries based on existing models (Blinov & Kotelnikov, 2014; Garten et al., 2016; Ito et al., 2017) and building models taking into account the characteristics of sentiment lexica (Hamilton et al., 2016; Tang et al., 2014; Vo & Zhang, 2016; Wang & Xia, 2017).

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