Domain based sentiment analysis in regional Language-Kannada using machine learning algorithm

Rohini, Venmathi Asirvatham; Thomas, Merin; Latha, C. A.

doi:10.1109/rteict.2016.7807872

Cited by 27 publications

(7 citation statements)

References 3 publications

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“…These studies have addressed challenges such as informal language, slang, and emoticon usage typical of social media text, with BERT models demonstrating superior performance due to their ability to capture contextual information effectively. The exploration of these models across various languages and contexts emphasizes the dynamic nature of sentiment analysis research and its potential for future advancements [ 1,3,4,5,7,9,10,11,13,14,15,20,22,23,24,30].For languages with limited computational resources, such as Marathi and Urdu, lexicon-based approaches have been proposed as effective methods for sentiment analysis. Researchers have developed lexicons that include lists of positive and negative words, assigning polarity values to facilitate the classification of sentences into sentiments.…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring Sentiment Analysis in Indian Regional Languages: Methods, Challenges, and Future Directions

Nemade

2024

IJSREM

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Sentiment Analysis, pivotal in natural language processing, extends its reach beyond English to Indian regional languages like Hindi, Marathi, Kannada, Konkani, Bengali, Khandeshi, and Urdu. This paper presents a comprehensive survey of 32 research papers in this domain, examining methodologies, datasets, and techniques while emphasizing the significance of sentiment analysis in diverse linguistic contexts for enhancing customer relationship management functionalities. It underscores the necessity for future research and highlights the efficacy of machine learning techniques. By elucidating on computational challenges and outlining various sentiment analysis methods, this paper serves as a critical resource for researchers and practitioners, fostering advancements in sentiment analysis tailored to regional linguistic nuances. KEYWORDS Bag Of Words, Hindi, Kannada, RNN, Konkani, Malayalam, Marathi, Maximum Entropy, Naive Bayes, Sentiment Analysis, SVM, TF-IDF, Urdu.

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Section: Literature Reviewmentioning

confidence: 99%

Exploring Sentiment Analysis in Indian Regional Languages: Methods, Challenges, and Future Directions

Nemade

2024

IJSREM

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“…Language Size Mathews and Abraham [5] Malayalam 136 Kumar et al [6] Malayalam 13000 Kumar et al [7] Malayalam 12922 Soumya and Pramod [8] Malayalam 3184 Rohini et al [10] Kannada 100 Joshi and Vekariya [11] Gujarati 40 Shrivastava and Kumar [12] Hindi 8352 Bayhaqy et al [13] Hindi 230 Soumya and Pramod [14] Malayalam 5468 Thavareesan and Mahesan [15] Tamil 2691 Prasad et al [16] Bengali, Tamil 999, 1103 Naidu et al [17] Telugu 1400 Sharif et al [18] Bengali 1427…”

Section: Datasetmentioning

confidence: 99%

Sentiment analysis of Malayalam tweets using bidirectional encoder representations from transformers: a study

Ramamirtham²

2023

IJEECS

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Sentiment analysis on views and opinions expressed in Indian regional languages has become the current focus of research. But, compared to a globally accepted language like English, research on sentiment analysis in Indian regional languages like Malayalam are very low. One of the major hindrances is the lack of publicly available Malayalam datasets. This work focuses on building a Malayalam dataset for facilitating sentiment analysis on Malayalam texts and studying the efficiency of a pre-trained deep learning model in analyzing the sentiments latent in Malayalam texts. In this work, a Malayalam dataset has been created by extracting 2,000 tweets from Twitter. The bidirectional encoder representations from transformers (BERT) is a pretrained model that has been used for various natural language processing tasks. This work employs a transformer-based BERT model for Malayalam sentiment analysis. The efficacy of BERT in analyzing the sentiments latent in Malayalam texts has been studied by comparing the performance of BERT with various machine learning models as well as deep learning models. By analyzing the results, it is found that a substantial increase in accuracy of 5% for BERT when compared with that of Bi-GRU, which is the next bestperforming model.

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“…Resources in Telugu include the ACTSA (Annotated Corpus for Telugu Sentiment Analysis) dataset, 13 Sentiraama corpus 14 . Corpus created and work done in regional languages like Kannada, 15 Tamil, 16 Malayalam, 17 Bangla (Nafis et al, Das et al), 9 Urdu 18 have also encouraged researchers in the NLP domain to create and share more datasets for the regional languages.…”

Section: Related Workmentioning

confidence: 99%

Sentiment analysis on Hindi tweets during COVID‐19 pandemic

Saroj,

Thakur,

Pal

2023

Computational Intelligence

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A gap among the people has been created due to a lack of social interactions. The physical void has led to an increase in online interaction among users on social media platforms. Sentiment analysis of such interactions can help us analyze the general public psychology during the pandemic. However, the lack of data in non‐English and low‐resource languages like ‘Hindi’ makes it difficult to study it among native and non‐English speaking masses. Here, we create a small collection of ‘Hindi’ tweets on COVID‐19 during the pandemic containing 10,011 tweets for sentiment analysis, which is named as sentiment analysis for Hindi (SAFH). In this article, we describe the process of collecting, creating, annotating the corpus, and sentiment classification. The claims have been verified using different word embedding with a deep learning classifier through the proposed model. The achieved accuracy of the proposed model yields up to a permissible rate of 90.9%.

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Domain based sentiment analysis in regional Language-Kannada using machine learning algorithm

Cited by 27 publications

References 3 publications

Exploring Sentiment Analysis in Indian Regional Languages: Methods, Challenges, and Future Directions

Exploring Sentiment Analysis in Indian Regional Languages: Methods, Challenges, and Future Directions

Sentiment analysis of Malayalam tweets using bidirectional encoder representations from transformers: a study

Sentiment analysis on Hindi tweets during COVID‐19 pandemic

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