Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Emotion Recognition in Conversation: Research Challenges, Datasets, and
  Recent Advances   Emotion is intrinsic to humans and consequently emotion understanding is a
key part of human-like artificial intelligence (AI). Emotion recognition in
conversation (ERC) is becoming increasingly popular as a new research frontier
in natural language processing (NLP) due to its ability to mine opinions from
the plethora of publicly available conversational data in platforms such as
Facebook, Youtube, Reddit, Twitter, and others. Moreover, it has potential
applications in health-care systems (as a tool for psychological analysis),
education (understanding student frustration) and more. Additionally, ERC is
also extremely important for generating emotion-aware dialogues that require an
understanding of the user's emotions. Catering to these needs calls for
effective and scalable conversational emotion-recognition algorithms. However,
it is a strenuous problem to solve because of several research challenges. In
this paper, we discuss these challenges and shed light on the recent research
in this field. We also describe the drawbacks of these approaches and discuss
the reasons why they fail to successfully overcome the research challenges in
ERC.

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The categories of this paper are: plasm-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: astro-ph
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The categories of this paper are: funct-an
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The categories of this paper are: solv-int
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The categories of this paper are: math
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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: q-fin
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The categories of this paper are: comp-gas
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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: atom-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: eess
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The categories of this paper are: math-ph