{"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,"footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13545],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-field-of-study":[246694,252919,246691,252922,248011,252913,246808,248944,252511,247534,252499],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-731419","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-human-language-technologies","msr-locale-en_us","msr-field-of-study-artificial-intelligence","msr-field-of-study-code-switching","msr-field-of-study-computer-science","msr-field-of-study-devanagari","msr-field-of-study-exploratory-research","msr-field-of-study-hindi","msr-field-of-study-natural-language-processing","msr-field-of-study-phenomenon","msr-field-of-study-semantic-equivalence","msr-field-of-study-social-media","msr-field-of-study-utterance"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2019-7-24","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"doi","viewUrl":"false","id":"false","title":"10.1145\/3314935","label_id":"243106","label":0},{"type":"url","viewUrl":"false","id":"false","title":"https:\/\/dl.acm.org\/doi\/abs\/10.1145\/3314935","label_id":"243109","label":0}],"msr_related_uploader":"","msr_attachments":[],"msr-author-ordering":[{"type":"text","value":"Koustav Rudra","user_id":0,"rest_url":false},{"type":"text","value":"Ashish Sharma","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Kalika Bali","user_id":32477,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Kalika Bali"},{"type":"user_nicename","value":"Monojit Choudhury","user_id":32996,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Monojit Choudhury"},{"type":"text","value":"Niloy Ganguly","user_id":0,"rest_url":false}],"msr_impact_theme":[],"msr_research_lab":[199562],"msr_event":[],"msr_group":[],"msr_project":[248216],"publication":[],"video":[],"download":[],"msr_publication_type":"inproceedings","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/731419"}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-research-item"}],"version-history":[{"count":1,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/731419\/revisions"}],"predecessor-version":[{"id":731422,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/731419\/revisions\/731422"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=731419"}],"wp:term":[{"taxonomy":"msr-content-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-content-type?post=731419"},{"taxonomy":"msr-research-highlight","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-highlight?post=731419"},{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=731419"},{"taxonomy":"msr-publication-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-publication-type?post=731419"},{"taxonomy":"msr-product-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-product-type?post=731419"},{"taxonomy":"msr-focus-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-focus-area?post=731419"},{"taxonomy":"msr-platform","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-platform?post=731419"},{"taxonomy":"msr-download-source","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-download-source?post=731419"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=731419"},{"taxonomy":"msr-field-of-study","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-field-of-study?post=731419"},{"taxonomy":"msr-conference","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-conference?post=731419"},{"taxonomy":"msr-journal","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-journal?post=731419"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=731419"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=731419"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}