Abstract: 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.

Introduction: “Laughter is the best Medicine” is a saying which is popular with most of the people. Humor is a form of communication that bridges the gap between various languages, cultures, ages and demographics. That's why humorous content with funny and witty hashtags are so much popular on social media. It is a very powerful tool to connect with the audience. Automatic Humor Recognition is the task of determining whether a text contains some level of humorous content or not. First conference on Computational humor was organized in 1996, since then many research have been done in this field. kao2016computational does pun detection in one-liners and dehumor detects humor in Yelp reviews. Because of the complex and interesting aspects involved in detecting humor in texts, it is one of the challenging research field in Natural Language Processing BIBREF3 . Identifying humor in a sentence sometimes require a great amount of external knowledge to completely understand it. There are many types of humor, namely anecdotes, fantasy, insult, irony, jokes, quote, self deprecation etc BIBREF4 , BIBREF5 . Most of the times there are different meanings hidden inside a sentence which is grasped differently by individuals, making the task of humor identification difficult, which is why the development of a generalized algorithm to classify different type of humor is a challenging task. Majority of the researches on social media texts is focused on English. A study by schroeder2010half shows that, a high percentage of these texts are in non-English languages. fischer2011language gives some interesting information about the languages used on Twitter based on the geographical locations. With a huge amount of such user generated data available on social media, there is a need to develop technologies for non-English languages. In multilingual regions like South Asia, majority of the social media users speak more than two languages. In India, Hindi is the most spoken language (spoken by 41% of the population) and English is the official language of the country. Twitter has around 23.2 million monthly active users in India. Native speakers of Hindi often put English words in the sentences and transliterate the whole sentence to Latin script while posting on social media, thereby making the task of automatic text classification a very challenging problem. Linguists came up with a term for any type of language mixing, known as `code-mixing' or `code-switching' BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 . Both the terms are used interchangeably, but there is a slight difference between the two terms. Code-mixing refers to the insertion of words, phrases, and morphemes of one language into a statement or an expression of another language, whereas transliteration of every word in a sentence to another script ( here Devanagari to Latin) is coined code-switching BIBREF10 . The first tweet in Figure 1 is an example of code-mixing and second is an example of code-switching. In this paper, we use code-mixing to denote both cases. In this paper, we present a freely available corpus containing code-mixed tweets in Hindi and English language with tweets written in Latin script. Tweets are manually classified into humorous and non-humorous classes. Moreover, each token in the tweets is also given a language tag which determines the source or origin language of the token (English or Hindi). The paper is divided in sections as follows, we start by describing the corpus and the annotation scheme in Section 2. Section 3 summarizes our supervised classification system which includes pre-processing of the tweets in the dataset and the feature extraction followed by the method used to identify humor in tweets. In the next subsection, we describe the classification model and the results of the experiments conducted using character and word level features. In the last section, we conclude the paper followed by future work and references.

Question: What type of system does the baseline classification use?
Answer: Classification system use n-grams, bag-of-words, common words and hashtags as features and SVM, random forest, extra tree and NB classifiers.