Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Squared English Word: A Method of Generating Glyph to Use Super
  Characters for Sentiment Analysis   The Super Characters method addresses sentiment analysis problems by first
converting the input text into images and then applying 2D-CNN models to
classify the sentiment. It achieves state of the art performance on many
benchmark datasets. However, it is not as straightforward to apply in Latin
languages as in Asian languages. Because the 2D-CNN model is designed to
recognize two-dimensional images, it is better if the inputs are in the form of
glyphs. In this paper, we propose SEW (Squared English Word) method generating
a squared glyph for each English word by drawing Super Characters images of
each English word at the alphabet level, combining the squared glyph together
into a whole Super Characters image at the sentence level, and then applying
the CNN model to classify the sentiment within the sentence. We applied the SEW
method to Wikipedia dataset and obtained a 2.1% accuracy gain compared to the
original Super Characters method. For multi-modal data with both structured
tabular data and unstructured natural language text, the modified SEW method
integrates the data into a single image and classifies sentiment with one
unified CNN model.

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The categories of this paper are: q-fin
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The categories of this paper are: quant-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: cmp-lg
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The categories of this paper are: alg-geom
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The categories of this paper are: astro-ph
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The categories of this paper are: physics
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The categories of this paper are: adap-org
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The categories of this paper are: eess
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The categories of this paper are: atom-ph
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The categories of this paper are: funct-an
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The categories of this paper are: chao-dyn
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The categories of this paper are: gr-qc
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The categories of this paper are: hep-ph
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The categories of this paper are: ao-sci