Building a robust sentiment lexicon with (almost) no resource

Mickael Rouvier; Benoit Favre

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arXiv · 1612.05202

Building a robust sentiment lexicon with (almost) no resource

Mickael Rouvier·Benoit Favre

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Abstract

Creating sentiment polarity lexicons is labor intensive. Automatically translating them from resourceful languages requires in-domain machine translation systems, which rely on large quantities of bi-texts. In this paper, we propose to replace machine translation by transferring words from the lexicon through word embeddings aligned across languages with a simple linear transform. The approach leads to no degradation, compared to machine translation, when tested on sentiment polarity classification on tweets from four languages.

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Mickael Rouvier, Benoit Favre. 2016-12-15. Building a robust sentiment lexicon with (almost) no resource. https://arxiv.org/abs/1612.05202

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