Text normalization of code mix and sentiment analysis

Sharma, Shashank; Pykl, Srinivas; Balabantaray, Rakesh Chandra

doi:10.1109/icacci.2015.7275819

Cited by 49 publications

(36 citation statements)

References 10 publications

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“…The accuracy of the opinion classifier on the er tweets was found to be 65.7%, 7% lower than our system. We also compared our mr sentiment classifier with that of Sharma et al (2015b). As their method performs two class sentiment detection (+ and −), we select such tweets from SAC.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, there has been a couple of studies on sentiment detection of code-switched tweets (Vilares et al, 2015;Sharma et al, 2015b). Sharma et al (2015b) use Hindi SentiWordNet and normalization techniques to detect sentiment in Hi-En CS tweets.…”

Section: Opinion and Sentiment Detectionmentioning

confidence: 99%

“…Apart from being the first large-scale quantitative study of language preference in multilingual societies, this work also has several other contributions: (a) We develop one of the first opinion and sentiment classifiers for Romanized Hindi and CS Hi-En tweets with higher accuracy than the only known previous attempt (Sharma et al, 2015b). (b) We present a novel methodology for automatically detecting pragmatic functions of codeswitching through opinion and sentiment detection.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Understanding Language Preference for Expression of Opinion and Sentiment: What do Hindi-English Speakers do on Twitter?

Rudra

Rijhwani

Begum

et al. 2016

Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Linguistic research on multilingual societies has indicated that there is usually a preferred language for expression of emotion and sentiment (Dewaele, 2010). Paucity of data has limited such studies to participant interviews and speech transcriptions from small groups of speakers. In this paper, we report a study on 430,000 unique tweets from Indian users, specifically Hindi-English bilinguals, to understand the language of preference, if any, for expressing opinion and sentiment. To this end, we develop classifiers for opinion detection in these languages, and further classifying opinionated tweets into positive, negative and neutral sentiments. Our study indicates that Hindi (i.e., the native language) is preferred over English for expression of negative opinion and swearing. As an aside, we explore some common pragmatic functions of codeswitching through sentiment detection.

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Section: Discussionmentioning

confidence: 99%

Section: Opinion and Sentiment Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Understanding Language Preference for Expression of Opinion and Sentiment: What do Hindi-English Speakers do on Twitter?

Rudra

Rijhwani

Begum

et al. 2016

Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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“…Work has also been done to support word level identification of languages in code-mixed text (Chittaranjan et al, 2014). Sharma et al (2015) tried an approach based on lexicon lookup for text normalization and sentiment analysis of code-mixed data. Pravalika et al (2017) used a lexicon lookup approach to perform domain specific sentiment analysis.…”

Section: Related Workmentioning

confidence: 99%

De-Mixing Sentiment from Code-Mixed Text

Lal¹,

Kumar²,

Dhar³

et al. 2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

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Code-mixing is the phenomenon of mixing the vocabulary and syntax of multiple languages in the same sentence. It is an increasingly common occurrence in today's multilingual society and poses a big challenge when encountered in different downstream tasks. In this paper, we present a hybrid architecture for the task of Sentiment Analysis of English-Hindi code-mixed data. Our method consists of three components, each seeking to alleviate different issues. We first generate subword level representations for the sentences using a CNN architecture. The generated representations are used as inputs to a Dual Encoder Network which consists of two different BiLSTMs-the Collective and Specific Encoder. The Collective Encoder captures the overall sentiment of the sentence, while the Specific Encoder utilizes an attention mechanism in order to focus on individual sentiment-bearing sub-words. This, combined with a Feature Network consisting of orthographic features and specially trained word embeddings, achieves state-of-the-art results-83.54% accuracy and 0.827 F1 score-on a benchmark dataset.

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“…Pandey and Sharvari [27] applied HSWN along with negation discourse for sentiment analysis of Hindi language text corpora, with the accuracy of near 80%. Srinivas, Sharma and Balbantray [28] demonstrated that text normalization can be achieved using techniques such phonetics based, slang and spelling correction approaches in another limited work [29] they demonstrated application of sentiment analysis techniques on transliterated text by using bilingual dictionary methods and HSWN for sentiment score calculation with 80% accuracy being achieved. Marathi and Sanskrit word identification was achieved by another work by Kulkarni, Patil and Dhanokar [30] using the genetic algorithm.…”

Section: Balamurali and Joshimentioning

confidence: 99%

Sentiment Analysis of Mixed Code for The Transliterated Hindi and Marathi Texts

Ansari¹,

Govilkar²

2018

IJNLC

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The evolution of information Technology has led to the collection of large amount of data, the volume of which has increased to the extent that in last two years the data produced is greater than all the data ever recorded in human history. This has necessitated use of machines to understand, interpret and apply data, without manual involvement. A lot of these texts are available in transliterated code-mixed form, which due to the complexity are very difficult to analyze. The work already performed in this area is progressing at great pace and this work hopes to be a way to push that work further. The designed system is an effort which classifies Hindi as well as Marathi text transliterated (Romanized) documents automatically using supervised learning methods (KNN), Naïve Bayes and Support Vector Machine (SVM)) and ontology based classification;and results are compared to in order to decide which methodology is better suited in handling of these documents. As we will see, the plain machine learning algorithm applications are just as or in many cases are much better in performance than the more analytical approach.

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Text normalization of code mix and sentiment analysis

Cited by 49 publications

References 10 publications

Understanding Language Preference for Expression of Opinion and Sentiment: What do Hindi-English Speakers do on Twitter?

Understanding Language Preference for Expression of Opinion and Sentiment: What do Hindi-English Speakers do on Twitter?

De-Mixing Sentiment from Code-Mixed Text

Sentiment Analysis of Mixed Code for The Transliterated Hindi and Marathi Texts

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