Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Emotional Embeddings: Refining Word Embeddings to Capture Emotional
  Content of Words   Word embeddings are one of the most useful tools in any modern natural
language processing expert's toolkit. They contain various types of information
about each word which makes them the best way to represent the terms in any NLP
task. But there are some types of information that cannot be learned by these
models. Emotional information of words are one of those. In this paper, we
present an approach to incorporate emotional information of words into these
models. We accomplish this by adding a secondary training stage which uses an
emotional lexicon and a psychological model of basic emotions. We show that
fitting an emotional model into pre-trained word vectors can increase the
performance of these models in emotional similarity metrics. Retrained models
perform better than their original counterparts from 13% improvement for
Word2Vec model, to 29% for GloVe vectors. This is the first such model
presented in the literature, and although preliminary, these emotion sensitive
models can open the way to increase performance in variety of emotion detection
techniques.

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The categories of this paper are: hep-ph
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The categories of this paper are: solv-int
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The categories of this paper are: hep-th
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The categories of this paper are: nucl-th
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The categories of this paper are: math-ph
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The categories of this paper are: stat
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The categories of this paper are: chem-ph
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The categories of this paper are: econ
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The categories of this paper are: q-bio
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The categories of this paper are: bayes-an
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The categories of this paper are: eess
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The categories of this paper are: funct-an
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The categories of this paper are: cmp-lg
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The categories of this paper are: gr-qc
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The categories of this paper are: alg-geom
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The categories of this paper are: acc-phys