Sentiment Analysis for Code-Mixed Indian Social Media Text With Distributed Representation

Kumari, Shalini; Ganesh, H B Barathi; Kumar, M. Anand

doi:10.1109/icacci.2018.8554835

Cited by 20 publications

(9 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Also, they employed a stance detection system to detect stance in Kannada-English code-mixed text (on social media) using sentence embeddings. Shalini et al (2018) have used distributed representations for sentiment analysis of Kannada-English code-mixed texts through neural networks, which had three tags: Positive, Negative and Neutral. However, the dataset for Kannada was not readily available for research purposes.…”

Section: Related Workmentioning

confidence: 99%

DravidianCodeMix: sentiment analysis and offensive language identification dataset for Dravidian languages in code-mixed text

Chakravarthi

Priyadharshini²,

Muralidaran

et al. 2022

Lang Resources & Evaluation

View full text Add to dashboard Cite

This paper describes the development of a multilingual, manually annotated dataset for three under-resourced Dravidian languages generated from social media comments. The dataset was annotated for sentiment analysis and offensive language identification for a total of more than 60,000 YouTube comments. The dataset consists of around 44,000 comments in Tamil-English, around 7000 comments in Kannada-English, and around 20,000 comments in Malayalam-English. The data was manually annotated by volunteer annotators and has a high inter-annotator agreement in Krippendorff’s alpha. The dataset contains all types of code-mixing phenomena since it comprises user-generated content from a multilingual country. We also present baseline experiments to establish benchmarks on the dataset using machine learning and deep learning methods. The dataset is available on Github and Zenodo.

show abstract

Section: Related Workmentioning

confidence: 99%

DravidianCodeMix: sentiment analysis and offensive language identification dataset for Dravidian languages in code-mixed text

Chakravarthi

Priyadharshini²,

Muralidaran

et al. 2022

Lang Resources & Evaluation

View full text Add to dashboard Cite

show abstract

“…For the identification of emotions in Hindi-English Twitter and Facebook data, authors in [48] proposed a Deep Learningbased system. Several Deep Learning techniques such as 1D-CNN, LSTM, Bi-LSTM, CNN-LSTM and CNN-BilSTM were used to predict the polarity of the sentence.…”

Section: F Convolutional Neural Networkmentioning

confidence: 99%

Machine Learning Techniques for Sentiment Analysis of Code-Mixed and Switched Indian Social Media Text Corpus - A Comprehensive Review

Ahmad¹,

Singla²,

Anis³

et al. 2022

IJACSA

View full text Add to dashboard Cite

A comprehensive review of sentiment analysis for code-mixed and switched text corpus of Indian social media using machine learning (ML) approaches, based on recent research studies has been presented in this paper. Code-mixing and switching are linguistic behavior shown by the bilingual/multilingual population, primarily in spoken but also in written communication, especially on social media. Code-mixing involves combining lower linguistic units like words and phrases of a language into the sentences of other language (the base language) and code-switching involves switching to another language, for the length of one sentence or more. In code-mixing and switching, a bilingual person takes one or more words or phrases from one language and introduces them into another language while communicating in that language in spoken or written mode. People nowadays express their views and opinions on several issues on social media. In multilingual countries, people express their views using English as well as their native languages. Several reasons can be attributed to code-mixing. Lack of knowledge in one language on a particular subject, being empathetic, interjection and clarification are some to name. Sentiment analysis of monolingual social media content has been carried out for the last two decades. However, during recent years, Natural Language Processing (NLP) research focus has also shifted towards the exploration of code-mixed data, thereby, making code mixed sentiment analysis an evolving field of research. Systems have been developed using ML techniques to predict the polarity of code-mixed text corpus and to fine tune the existing models to improve their performance.

show abstract

“…Several studies also have conducted for sentiment analysis and emotion detection using code-mixing data. Shalini et al [12] studied sentiment analysis for Facebook 7 comments with Kannada-English languages. The experiment was done by applying Facebook's fast text, Doc2Vec with SVM, Bidirectional LSTM, and CNN.…”

Section: Related Workmentioning

confidence: 99%

Aspect-Based Sentiment Analysis and Emotion Detection for Code-Mixed Review

Suciati

Budi²

2020

IJACSA

View full text Add to dashboard Cite

Review can affect customer decision making because by reading it, people manage to know whether the review is positive, or negative. However, positive, negative, and neutral, without considering the emotion will be not enough because emotion can strengthen the sentiment result. This study explains about the comparison of machine learning and deep learning in sentiment as well as emotion classification with multilabel classification. In machine learning comparison, the problem transformation that we used are Binary Relevance (BR), Classifier Chain (CC), and Label Powerset (LP), with Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Extra Tree Classifier (ET) as algorithms of machine learning. The features we compared are n-gram language model (unigram, bigram, unigram-bigram). For deep learning, algorithms that we applied are Gated Recurrent Unit (GRU) and Bidirectional Long Short-Term Memory (BiLSTM), using selfdeveloped word embedding. The comparison results show RF dominates with 88.4% and 89.54% F1 scores with CC method for food aspect, and LP for price, respectively. For service and ambience aspects, ET leads with 92.65% and 87.1% with LP and CC methods, respectively. On the other hand, in deep learning comparison, GRU and BiLSTM obtained similar F1-score for food aspect, 88.16%. On price aspect, GRU leads with 83.01%. However, for service and ambience, BiLSTM achieved higher F1score, 89.03% and 84.78%.

show abstract

Sentiment Analysis for Code-Mixed Indian Social Media Text With Distributed Representation

Cited by 20 publications

References 13 publications

DravidianCodeMix: sentiment analysis and offensive language identification dataset for Dravidian languages in code-mixed text

DravidianCodeMix: sentiment analysis and offensive language identification dataset for Dravidian languages in code-mixed text

Machine Learning Techniques for Sentiment Analysis of Code-Mixed and Switched Indian Social Media Text Corpus - A Comprehensive Review

Aspect-Based Sentiment Analysis and Emotion Detection for Code-Mixed Review

Contact Info

Product

Resources

About