Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: How Hateful are Movies? A Study and Prediction on Movie Subtitles   In this research, we investigate techniques to detect hate speech in movies.
We introduce a new dataset collected from the subtitles of six movies, where
each utterance is annotated either as hate, offensive or normal. We apply
transfer learning techniques of domain adaptation and fine-tuning on existing
social media datasets, namely from Twitter and Fox News. We evaluate different
representations, i.e., Bag of Words (BoW), Bi-directional Long short-term
memory (Bi-LSTM), and Bidirectional Encoder Representations from Transformers
(BERT) on 11k movie subtitles. The BERT model obtained the best macro-averaged
F1-score of 77%. Hence, we show that transfer learning from the social media
domain is efficacious in classifying hate and offensive speech in movies
through subtitles.

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The categories of this paper are: funct-an
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The categories of this paper are: q-alg
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The categories of this paper are: physics
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The categories of this paper are: cmp-lg
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The categories of this paper are: supr-con
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The categories of this paper are: eess
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The categories of this paper are: nucl-th
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The categories of this paper are: nucl-ex
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The categories of this paper are: chem-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: q-bio
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The categories of this paper are: math
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The categories of this paper are: adap-org
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The categories of this paper are: mtrl-th
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The categories of this paper are: acc-phys
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The categories of this paper are: patt-sol