A Code-Diverse Kannada-English Dataset For NLP Based Sentiment Analysis Applications

Kannadaguli, Prashanth

doi:10.1109/iciip53038.2021.9702548

Cited by 7 publications

(3 citation statements)

References 38 publications

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“…This is due to the influence of English, which is widely recognized as the language of education and higher learning. The practice of code-mixing is not limited to those with less education or training but is also observed among individuals with higher levels of education and training [9]. The use of Roman script for writing Tamil-English combinations, or "Tanglish," is also common on the internet, reflecting the widespread use of English online [10].…”

Section: Amentioning

confidence: 99%

Sentiment Analysis on Code-Mixed Tamil-English Corpus: A Comprehensive Study of Transformer-Based Models

Sangeetha,

Nimala

2023

Preprint

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NLP, or natural language processing, is a subfield of AI that aims to equip computers with the ability to understand and analyze human language. Sentiment analysis is a widely used application of NLP, particularly for examining attitudes expressed in online conversations. Nevertheless, many social media comments are written in languages that are not native to the authors, making sentiment analysis more difficult, especially for languages with limited resources, such as Tamil. To tackle this issue, a code-mixed and sentiment-annotated corpus in Tamil and English was created. This article will explain how the corpus was established, including the process of data collection and the assignment of polarities. The article will also explore the agreement between annotators and the results of sentiment analysis performed on the corpus. This work signifies various performance metrics such as precision, recall, support, and F1-score for the transformer-based model such as BERT, RoBerta, and XLM-RoBerta. Among the various models, XLM-Robert shows slightly significant positive results on the code-mixed corpus when compared to the state of art models.

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Section: Amentioning

confidence: 99%

Sentiment Analysis on Code-Mixed Tamil-English Corpus: A Comprehensive Study of Transformer-Based Models

Sangeetha,

Nimala

2023

Preprint

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“…During this stage, unnecessary characters, punctuation, and numbers are eliminated, and specific challenges such as spelling variations and abbreviations unique to the regional language are addressed. Additionally, the text is divided into individual words and sub-words for further analysis and processing [2]. In certain regional languages, words are not always separated by spaces or punctuation marks.…”

Section: Introductionmentioning

confidence: 99%

“…In other words, it is the percentage of times that the model predicts a positive class label when the actual class label is positive. Sensitivity = TP / (TP + FN)(2)…”

mentioning

confidence: 99%

Unravelling Emotional Tones: A Hybrid Optimized Model for Sentiment Analysis in Tamil Regional Languages

2024

JMC

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Review comments from digital platform such as Facebook, Twitter and YouTube used for identification of emotional tones from text. Nowadays, reviews are posted in different languages such as English, French, Chinese, and Indian regional languages such as Tamil, Telegu, and Hindi. Identification of emotional tones from text written in Indian regional language is challenging. During the translation of the regional language to the English language for sentiment analysis, lexical and pragmatic ambiguity are the major problem. The above problem arises due to dialects in language such as regional, standard, and social dialects. In this paper, dialect-based ambiguity problems solve through proposed Hybrid optimized deep learning transformer Models like M-BERT, M-Roberta, and M-XLM-Roberta for Tamil language dialects recognise and classified. The proposed algorithms provide better sentimental analysis after Hybrid optimization due to adaptation mechanisms, dynamic changes in the parameters and strategies in fine-tuning the search. The proposed Hybrid optimized algorithms perform better than existing algorithms such as SVM, Naïve Bayes, and LSTM with an accuracy of 95%.

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Comparative study of machine learning algorithms for Kannada twitter sentimental analysis

Patil,

Ijeri,

Kulkarni

et al. 2023

Multimed Tools Appl

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A Code-Diverse Kannada-English Dataset For NLP Based Sentiment Analysis Applications

Cited by 7 publications

References 38 publications

Sentiment Analysis on Code-Mixed Tamil-English Corpus: A Comprehensive Study of Transformer-Based Models

Sentiment Analysis on Code-Mixed Tamil-English Corpus: A Comprehensive Study of Transformer-Based Models

Unravelling Emotional Tones: A Hybrid Optimized Model for Sentiment Analysis in Tamil Regional Languages

Comparative study of machine learning algorithms for Kannada twitter sentimental analysis

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