{"id":731419,"date":"2021-03-07T08:00:54","date_gmt":"2021-03-07T16:00:54","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&#038;p=731419"},"modified":"2021-03-07T08:00:54","modified_gmt":"2021-03-07T16:00:54","slug":"identifying-and-analyzing-different-aspects-of-english-hindi-code-switching-in-twitter","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/identifying-and-analyzing-different-aspects-of-english-hindi-code-switching-in-twitter\/","title":{"rendered":"Identifying and Analyzing Different Aspects of English-Hindi Code-Switching in Twitter"},"content":{"rendered":"<p>Code-switching or the juxtaposition of linguistic units from two or more languages in a single utterance, has, in recent times, become very common in text, thanks to social media and other computer mediated forms of communication. In this exploratory study of English-Hindi code-switching on Twitter, we automatically create a large corpus of code-switched tweets and devise techniques to identify the relationship between successive components in a code-switched tweet. More specifically, we identify pragmatic functions such as narrative-evaluative, negative reinforcement, translation or semantically equivalent statements, and so on characterizing the relation between successive components. We analyze the difference\/similarity between switching patterns in code-switched and monolingual multi-component tweets. We observe strong dominance of narrative-evaluative (non-opinion to opinion or vice versa) switching in case of both code-switched and monolingual multi-component tweets in around 40% of cases. Polarity switching appears to be a prevalent switching phenomenon (10%) specifically in code-switched tweets (three to four times higher than monolingual multi-component tweets) where preference of expressing negative sentiment in Hindi is approximately twice compared to English. Positive reinforcement appears to be an important pragmatic function for English multi-component tweets, whereas negative reinforcement plays a key role for Devanagari multi-component tweets. Our results also indicate that the extent and nature of code-switching also strongly depend on the topic (sports, politics, etc.) of discussion.<\/p>\n<div class=\"ms-editor-squiggler\" style=\"color: initial;font: initial;background: initial;background-blend-mode: initial;border: initial;border-radius: initial;border-collapse: initial;caption-side: initial;clear: initial;columns: initial;column-fill: initial;column-rule: initial;column-span: initial;cursor: initial;flex: initial;flex-flow: initial;float: initial;height: 0px;letter-spacing: initial;margin: initial;max-height: initial;max-width: initial;min-height: initial;min-width: initial;overflow: initial;padding: initial;text-align: initial;text-decoration: initial;text-indent: initial;text-transform: initial;vertical-align: initial;border-spacing: initial;width: initial\"><\/div>\n","protected":false},"excerpt":{"rendered":"<p>Code-switching or the juxtaposition of linguistic units from two or more languages in a single utterance, has, in recent times, become very common in text, thanks to social media and other computer mediated forms of communication. In this exploratory study of English-Hindi code-switching on Twitter, we automatically create a large corpus of code-switched tweets and [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"29","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"ACM Transactions on Asian and Low-Resource Language Information Processing 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