Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: FEUP at SemEval-2017 Task 5: Predicting Sentiment Polarity and Intensity
  with Financial Word Embeddings   This paper presents the approach developed at the Faculty of Engineering of
University of Porto, to participate in SemEval 2017, Task 5: Fine-grained
Sentiment Analysis on Financial Microblogs and News. The task consisted in
predicting a real continuous variable from -1.0 to +1.0 representing the
polarity and intensity of sentiment concerning companies/stocks mentioned in
short texts. We modeled the task as a regression analysis problem and combined
traditional techniques such as pre-processing short texts, bag-of-words
representations and lexical-based features with enhanced financial specific
bag-of-embeddings. We used an external collection of tweets and news headlines
mentioning companies/stocks from S\&P 500 to create financial word embeddings
which are able to capture domain-specific syntactic and semantic similarities.
The resulting approach obtained a cosine similarity score of 0.69 in sub-task
5.1 - Microblogs and 0.68 in sub-task 5.2 - News Headlines.

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The categories of this paper are: cond-mat
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The categories of this paper are: supr-con
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The categories of this paper are: physics
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The categories of this paper are: acc-phys
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The categories of this paper are: dg-ga
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The categories of this paper are: ao-sci
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The categories of this paper are: q-bio
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The categories of this paper are: quant-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: adap-org
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The categories of this paper are: math
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The categories of this paper are: mtrl-th
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The categories of this paper are: hep-ex
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The categories of this paper are: solv-int
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The categories of this paper are: bayes-an
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The categories of this paper are: nucl-ex