Classifying sentiments in Nepali subjective texts

Thapa, Lal Bahadur Reshmi; Bal, Bal Krishna

doi:10.1109/iisa.2016.7785374

Cited by 12 publications

(4 citation statements)

References 6 publications

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“…Apart from news classification task in Nepali language, sentiment classification is another well-explored field that have used machine learning and deep learning models. Thapa and Bal (2016) performed a document level sentiment analysis for movie reviews using SVM, MNB and Logistic Regression which used TF-IDF and BoW as feature extractors. Moreover, their data consisted very low number of samples, i.e.…”

Section: Text Classificationmentioning

confidence: 99%

“…Shahi & Pant, 2018;Singh, 2018;Subba, Paudel, & Shahi, 2019;Thakur & Singh, 2014;Wagle & Thapa, 2021) and sentiment analysis (Piryani, Piryani, Singh, & Pinto, 2020;Regmi, Bal, & Kultsova, 2017;T. Shahi, Sitaula, & Paudel, 2022;Sitaula, Basnet, Mainali, & Shahi, 2021;Tamrakar, Bal, & Thapa, 2020;Thapa & Bal, 2016). Several studies (Aggarwal, Chauhan, Kumar, Mittal, & Verma, 2020;Al-Yahya, Al-Khalifa, Al-Baity, AlSaeed, & Essam, 2021;Terechshenko et al, 2020) show that transformer models give significantly better performances than the former approaches due to their ability to attend to longer sequences of text using attention mechanisms.…”

Section: Introductionmentioning

confidence: 99%

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Comparative Evaluation of Transformer-Based Nepali Language Models

Tamrakar

Silpasuwanchai

2022

Preprint

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Large pre-trained transformer models using self-supervised learning have achieved state-of-the-art performances in various NLP tasks. However, for low-resource language like Nepali, pre-training of monolingual models remains a problem due to lack of training data and well-designed and balanced benchmark datasets. Furthermore, several multilingual pre-trained models such as mBERT and XLM-RoBERTa have been released, but their performance remains unknown for Nepali language. We compared Nepali monolingual pre-trained transformer models with multilingual models to determine their performance using a Nepali text classification dataset as a downstream task based on different number of classes and data sizes, taking machine learning (ML) and deep learning (DL) algorithms as baselines. Under-representation of Nepali language in mBERT resulted in overall poor performance, but, XLM-RoBERTa, which has a larger vocabulary size, produced state-of-the-art performance which is relatively similar to that of Nepali DistilBERT and DeBERTa, which outperformed all of the baseline algorithms. Bi-LSTM and SVM from the baselines also performed very well in variety of settings. Moreover, to assess the cross-language knowledge transfer for the cases when mono-lingual models are not available, we also evaluated HindiRoBERTa, a monolingual Indian language model on Nepali text dataset. This research mainly contributes to the Nepali NLP community by creation of news classification dataset with 20 classes, with over 200,000 articles and performance evaluation of various pre-trained monolingual Nepali transformers with multilingual transformers, DL and ML algorithms.

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Section: Text Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparative Evaluation of Transformer-Based Nepali Language Models

Tamrakar

Silpasuwanchai

2022

Preprint

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“…Very scarce amount of contributions exists in analyzing the sentiments of such under resourced languages. Going through a research study on Nepali language by Thapa and Bal [14], implementation of supervised ML classifiers on a size of 384 book and movie reviews has been observed. Extracting features by TF-IDF and Bag-of Words (BOW) technique, results unmasked that MNB performed well in comparison with SVM and LR.…”

Section: A Sentiment Analysis In English and Other Languagesmentioning

confidence: 99%

Supervised Machine Learning Algorithms for Sentiment Analysis of Bangla Newspaper

2021

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We can state undoubtedly that Bangla language is rich enough to work with and implement various Natural Language Processing (NLP) tasks. Though it needs proper attention, hardly NLP field has been explored with it. In this age of digitalization, large amount of Bangla news contents are generated in online platforms. Some of the contents are inappropriate for the children or aged people. With the motivation to filter out news contents easily, the aim of this work is to perform document level sentiment analysis (SA) on Bangla online news. In this respect, the dataset is created by collecting news from online Bangla newspaper archive. Further, the documents are manually annotated into positive and negative classes. Composite process technique of “Pipeline” class including Count Vectorizer, transformer (TF-IDF) and machine learning (ML) classifiers are employed to extract features and to train the dataset. Six supervised ML classifiers (i.e. Multinomial Naive Bayes (MNB), K-Nearest Neighbor (K-NN), Random Forest (RF), (C4.5) Decision Tree (DT), Logistic Regression (LR) and Linear Support Vector Machine (LSVM)) are used to analyze the best classifier for the proposed model. There has been very few works on SA of Bangla news. So, this work is a small attempt to contribute in this field. This model showed remarkable efficiency through better results in both the validation process of percentage split method and 10-fold cross validation. Among all six classifiers, RF has outperformed others by 99% accuracy. Even though LSVM has shown lowest accuracy of 80%, it is also considered as good output. However, this work has also exhibited surpassing outcome for recent and critical Bangla news indicating proper feature extraction to build up the model.

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“…Researches have been performed on detecting sentiments (Gupta & Bal, 2015) and classifying sentiments (Thapa & Bal, 2016)…”

Section: Introductionmentioning

confidence: 99%

Aspect Based Sentiment Analysis of Nepali Text Using Support Vector Machine and Naive Bayes

Tamrakar

Bal

Thapa

2020

Tech J

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Aspect-based Sentiment Analysis assists in understanding the opinion of the associated entities helping for a better quality of a service or a product. A model is developed to detect the aspect-based sentiment in Nepali text using Machine Learning (ML) classifier algorithms namely Support Vector Machine (SVM) and Naïve Bayes (NB). The system collects Nepali text data from various websites and Part of Speech (POS) tagging is applied to extract the desired features of aspect and sentiment. Manual labeling is done for each sentence to identify the sentiment of the sentence. Term Frequency – Inverse Document Frequency (TF-IDF) is applied to compute the importance of the words. The feature vectors thus produced are then applied to the Classifier algorithms to predict and classify the sentence. The accuracy obtained by the SVM classifier is 76.8% whereas Bernoulli NB is 77.5%.

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Classifying sentiments in Nepali subjective texts

Cited by 12 publications

References 6 publications

Comparative Evaluation of Transformer-Based Nepali Language Models

Comparative Evaluation of Transformer-Based Nepali Language Models

Supervised Machine Learning Algorithms for Sentiment Analysis of Bangla Newspaper

Aspect Based Sentiment Analysis of Nepali Text Using Support Vector Machine and Naive Bayes

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