Codemixed hindi english annoted tweets data for humor, sentiment analysis
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The tremendous amount of user generated data through social networking sites led to the gaining popularity of automatic text
classification in the field of computational linguistics over the past decade. Within this domain, one problem that has drawn the attention
of many researchers is automatic humor detection in texts. In depth semantic understanding of the text is required to detect humor
which makes the problem difficult to automate. With increase in the number of social media users, many multilingual speakers often
interchange between languages while posting on social media which is called code-mixing. It introduces some challenges in the field of
linguistic analysis of social media content (Barman et al., 2014), like spelling variations and non-grammatical structures in a sentence.
Past researches include detecting puns in texts (Kao et al., 2016) and humor in one-lines (Mihalcea et al., 2010) in a single language,
but with the tremendous amount of code-mixed data available online, there is a need to develop techniques which detects humor in
code-mixed tweets. In this paper, we analyze the task of humor detection in texts and describe a freely available corpus containing
English-Hindi code-mixed tweets annotated with humorous(H) or non-humorous(N) tags. We also tagged the words in the tweets
with Language tags (English/Hindi/Others). Moreover, we describe the experiments carried out on the corpus and provide a baseline
classification system which distinguishes between humorous and non-humorous texts
Answer:
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