Sentiment analysis of mixed language employing Hindi-English code switching

Sitaram, Dinkar; Murthy, Savitha; Ray, Debraj; Sharma, Devansh; Dhar, Kashyap

doi:10.1109/icmlc.2015.7340934

Cited by 28 publications

(9 citation statements)

References 8 publications

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“…As shown in Figure 2, there is a large volume of published studies concentrating on a mix of English language with Hindi (D. Sitaram, et al.,2015 . It is observed that only a few studies have been carried out on other language pairs such as German, Portuguese and Malay.…”

Section: Language Pairsmentioning

confidence: 99%

Sentiment Analysis of Code-Mixed Text: A Review

et.al¹

2021

TURCOMAT

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In recent times, sentiment analysis has become one of the most active research and progressively popular areas in information retrieval and text mining. To date, sentiment analysis has been applied in various domains such as product, movie, sport and political reviews. Most of the previous work in this field has focused on analyzing only a single language, especially English. However, with the need of globalization and the increasing number of the Internet used worldwide; it is common to see the post written in multiple languages. Moreover, in an unstructured content like Twitter posts, people tend to mix languages in one sentence, which make sentiment analysis process even harder and more challenging. This paper reviews the state-of-the-art of sentiment analysis for code-mixed, which includes the detail discussions of each focus area, qualitative comparison and limitations of current approaches. This paper also highlights challenges along this line of research and suggests several recommendations for future works that should be explored.

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Section: Language Pairsmentioning

confidence: 99%

Sentiment Analysis of Code-Mixed Text: A Review

et.al¹

2021

TURCOMAT

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“…Hidayat (2012) reported that 45 percent of codeswitching was instigated by real lexical needs, 40 percent was used for talking about a particular topic and 5 percent for content clarification based on Facebook data set. More recently the computational linguistics research community started to utilize social media content to create code-mixed corpus (Chakma and Das, 2016), build automatic language identification system (Barman et al, 2014;Chanda et al, 2016), or perform sentiment analysis (Sitaram et al, 2015).…”

Section: Code-switching In Online Environmentsmentioning

confidence: 99%

Mixed language queries in online searches

2019

AJIM

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Purpose With the increasing number of online multilingual resources, cross-language information retrieval (CLIR) has drawn much attention from the information retrieval (IR) research community. However, few studies have examined how and why multilingual searchers seek information in two or more languages, specifically how they switch and mix language in queries to get satisfying results. The purpose of this paper is to focus on Chinese–English bilinguals’ intra-sentential code-switching behaviors in online searches. The scenarios and reasons of code-switching, factors that may affect code-switching, the patterns of mixed language query formulation and reformulation and how current IR systems and other search tools can facilitate such information needs were examined. Design/methodology/approach In-depth semi-structured interviews were used as the research method. In total, 30 participants were recruited based on their English proficiency, location and profession, using a purposive sampling method. Findings Four scenarios and four reasons for using Chinese–English mixed language queries to cover information needs were identified, and results suggest that linguistic and cultural/social factors are of equivalent importance in code-switching behaviors. English terms and Chinese terms in queries play different roles in searches, and mixed language queries are irreplaceable by either single language queries or other search facilitating features. Findings also suggest current search engines and tools need greater emphasis in the user interface and more user education is required. Originality/value This study presents a qualitative analysis of bilinguals’ code-switching behaviors in online searches. Findings are expected to advance the theoretical understanding of bilingual users’ search strategies and interactions with IR systems, and provide insights for designing more effective IR systems and tools to discover multilingual online resources, including cross-language controlled vocabularies, personalized CLIR tools and mixed language query assistants.

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“…According to the research, the involvement of Indian users has increased greatly these days and so is the presence of Indian languages (16) . Now, more users express their feelings in Hindi, Bengali, Punjabi, Telugu, Tamil and Malayalam etc.…”

Section: Introductionmentioning

confidence: 99%

“…The increment of 4-5% in accuracy was shown when Hindi-English code mix was used for misspelling, morpheme and application of Sub-word LSTM architecture instead of traditional approaches (24) . The linguistic code-switching is considered by taking into account the grammatical transition among languages and the recursive neural tensor network (RNTN) (16) . Further research has been done for Hindi-English code mixed data to identify the polarity of a speech as normal or hate (25) .…”

Section: Introductionmentioning

confidence: 99%

Annotated corpus creation for sentiment analysis in code-mixed Hindi-English (Hinglish) social network data

Garg¹,

Sharma²

2020

IJST

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Background: Evaluating the sentiments of tweets, blogs, comments and posts have become a crucial part of many applications. Sentiment analysis of social network data is very helpful for decision-making in application areas like movie reviews, product feedback and impact of the speech of a politician etc. The users often comment in their native languages or in slang languages or more often they use abbreviations and do not even stick to grammatical rules of the language. The bilingual and multilingual community often mixes two or more than two languages in their comments. Unavailability of annotated code-mixed data for native language adds to the difficulty in performing sentiment analysis. Objectives The motive of this article is to present the process of creating annotated corpus for code mixed social media text in Hindi, English and Hinglish which is collected from twitter. Ambiguous meaning words and inconsistent spellings of both the languages have also been included in the study to provide wide spread canvas. Method: This study will provide significant elements that should be considered while developing the annotated corpus of Hindi, Hinglish & English dataset. The annotation is calculated on the basis of polarity of words in three categories as positive, negative and neutral. There are words which have mixed feeling i.e. these words have positive as well as negative sentiments. To consider these words, inner agreement among the polarities has been considered. The words used for sarcasm or slangs have also been taken into account. The study has included ambiguous meaning and inconsistent spelling words of both languages as well. Findings: The proposed work provides a standard annotated corpus for code switched social media text in Hindi-English (Hinglish). The process of developing the corpus and calculating the polarity has been shown. It is found that if one considers the code-mixed text, the accuracy can be enhanced. Application: The proposed corpus can be utilized in the area of market analysis, customer behavior, polling analysis, brand monitoring, etc. The corpus serves as dataset which can further be extended according to the problem definition.

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Sentiment analysis of mixed language employing Hindi-English code switching

Cited by 28 publications

References 8 publications

Sentiment Analysis of Code-Mixed Text: A Review

Sentiment Analysis of Code-Mixed Text: A Review

Mixed language queries in online searches

Annotated corpus creation for sentiment analysis in code-mixed Hindi-English (Hinglish) social network data

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