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        <title>A Comparative Assessment of Machine Learning Techniques in Kannada Multi-Emotion Sentiment Analysis</title>
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        <namePart type="given">Dakshayani</namePart>
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    <name type="personal">
        <namePart type="given">Pushpa</namePart>
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            <title>Proceedings of the 21st International Conference on Natural Language Processing (ICON)</title>
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        <name type="personal">
            <namePart type="given">Sobha</namePart>
            <namePart type="family">Lalitha Devi</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Karunesh</namePart>
            <namePart type="family">Arora</namePart>
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            <publisher>NLP Association of India (NLPAI)</publisher>
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    <abstract>In order to advance a firm, it is crucial to understand user opinions on social media. India has diversity, with Kannada being one of the widely spoken languages. Sentiment analysis in Kannada offers a tool to assess opinion, gather customer feedback, and identify social media trends among the Kannada-speaking community. This kind of analysis assists businesses in comprehending the sentiments expressed in Kannada language customer reviews, social media posts, and online conversations. It empowers them to make choices based on data and customize their offerings to better suit the needs of their customers. This work proposes a model to perform sentiment analysis in Kannada language with four emotions, namely anger, fear, joy, and sadness, using machine learning algorithms like linear support vector classification, logistic regression, stochastic gradient descent, K-nearest neighbors, multinomial naive bayes, and random forest classification. The model achieved an accuracy of 87.25% with a linear support vector classifier.</abstract>
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%0 Conference Proceedings
%T A Comparative Assessment of Machine Learning Techniques in Kannada Multi-Emotion Sentiment Analysis
%A Ijeri, Dakshayani
%A B. Patil, Pushpa
%Y Lalitha Devi, Sobha
%Y Arora, Karunesh
%S Proceedings of the 21st International Conference on Natural Language Processing (ICON)
%D 2024
%8 December
%I NLP Association of India (NLPAI)
%C AU-KBC Research Centre, Chennai, India
%F ijeri-b-patil-2024-comparative
%X In order to advance a firm, it is crucial to understand user opinions on social media. India has diversity, with Kannada being one of the widely spoken languages. Sentiment analysis in Kannada offers a tool to assess opinion, gather customer feedback, and identify social media trends among the Kannada-speaking community. This kind of analysis assists businesses in comprehending the sentiments expressed in Kannada language customer reviews, social media posts, and online conversations. It empowers them to make choices based on data and customize their offerings to better suit the needs of their customers. This work proposes a model to perform sentiment analysis in Kannada language with four emotions, namely anger, fear, joy, and sadness, using machine learning algorithms like linear support vector classification, logistic regression, stochastic gradient descent, K-nearest neighbors, multinomial naive bayes, and random forest classification. The model achieved an accuracy of 87.25% with a linear support vector classifier.
%U https://aclanthology.org/2024.icon-1.49/
%P 422-431

Markdown (Informal)

[A Comparative Assessment of Machine Learning Techniques in Kannada Multi-Emotion Sentiment Analysis](https://aclanthology.org/2024.icon-1.49/) (Ijeri & B. Patil, ICON 2024)

• A Comparative Assessment of Machine Learning Techniques in Kannada Multi-Emotion Sentiment Analysis (Ijeri & B. Patil, ICON 2024)

ACL

• Dakshayani Ijeri and Pushpa B. Patil. 2024. A Comparative Assessment of Machine Learning Techniques in Kannada Multi-Emotion Sentiment Analysis. In Proceedings of the 21st International Conference on Natural Language Processing (ICON), pages 422–431, AU-KBC Research Centre, Chennai, India. NLP Association of India (NLPAI).