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

Tamrakar, S.; Bal, Bal Krishna; Thapa, Rajendra Bahadur

doi:10.3126/tj.v2i1.32824

Cited by 8 publications

(5 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, there is a lack of research concerning COVID-19 tweet datasets, especially in the context of the Nepali language. Examination of sentiment in the English and Nepali language using machine and deep learning has been the subject to numerous studies [1], [4], [5], [7], [9], [11], [12] .…”

Section: Related Workmentioning

confidence: 99%

“…This omission is pivotal for effectively distinguishing complex documents or tweets, where factors like higher interclass similarity and intraclass dissimilarity come into play. The author [12] established aspect-based sentiment analysis using SVM and Naïve Bayes classifiers. The algorithm gathers Nepali text data from several sources and uses part-of-speech tagging to locate relevant aspects and sentiment characteristics, which was done using the TF-IDF method.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Sentiment Analysis of Nepali COVID-19 Tweets using BERT-LSTM

Tharu,

Pokhrel,

Lamichhane

2023

J. Eng. Sci.

View full text Add to dashboard Cite

The global impact of COVID-19 has significantly reshaped the day-by-day lives of individuals worldwide. COVID-19 is one of the top deadly diseases and has tragically claimed the lives of millions across the globe. The people are affected not only by the physical infection but also mentally. Among the various social media platforms, Twitter is a widely utilized medium, reflecting a substantial surge in discussions about the coronavirus. These discussions encompass a spectrum of positive, negative, and neutral sentiments. The sentiments acknowledged by individuals, encapsulated in their posts and tweets across this platform, offer valuable insights into their emotional states and perspectives. In this investigation, the people's sentiments using Nepali COVID-19-related Twitter datasets are inspected. For this, the approach involves a two-step process. Initially, the multilingual BERT(m-bert) model will utilize whose output is used for subsequent downstream tasks. Secondly, m-BERT's output is connected to the LSTM layer to categorize the people's sentiments. The model was trained and tested using publically available NepCov19Tweets datasets. These tweets were split into three groups (positive, negative, and neutral). The appraisal outcomes for NepCOV19Tweets demonstrate that the proposed model comes up with outstanding performance when compared to the existing model, achieving an average accuracy of 76.04%, 80.03% recall, a precision of 77.12%, and an F1-score of 76%.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis of Nepali COVID-19 Tweets using BERT-LSTM

Tharu,

Pokhrel,

Lamichhane

2023

J. Eng. Sci.

View full text Add to dashboard Cite

show abstract

“…179 for positive and 205 for negative, and MNB outperformed other methods. Over few years, several research (Piryani et al, 2020;Regmi et al, 2017;Tamrakar et al, 2020) have been conducted using traditional machine learning and deep learning models for Nepali sentiment classification.…”

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%

Comparative Evaluation of Transformer-Based Nepali Language Models

Tamrakar

Silpasuwanchai

2022

Preprint

View full text Add to dashboard Cite

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.

show abstract

“…The nave Bayes algorithm and support vector machine are often compared for accuracy in the application of sentiment analysis which in several studies shows that nave Bayes has a higher level of accuracy. [5][6] [7]. Naïve Bayes has several classic variants, namely multinomial, Bernoulli and Gaussian [8].…”

Section: Introductionmentioning

confidence: 99%

Application of Naïve Bayes Algorithm Variations On Indonesian General Analysis Dataset for Sentiment Analysis

Umar

Nur

2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

View full text Add to dashboard Cite

Indonesian General Analysis Dataset is a dataset sourced from social media twitter by using keywords in the form of conjunctions to get a dataset that does not only focus on a particular topic. The use of Indonesian language datasets with general topics can be used to test the accuracy of the classification model so as to provide additional reference in choosing the right methods and parameters for sentiment analysis. One of the algorithms which in several studies produces the highest level of accuracy is naive Bayes which has several variations. This study aims to obtain the method with the best accuracy from the naive Bayes variation by setting the minimum and maximum document frequency parameters on the Indonesian General Analysis Dataset for sentiment analysis. The naive Bayes classifier variations used include Bernoulli naive Bayes, gaussian naive Bayes, complement naive Bayes and multinomial naive Bayes. The research stage begins with downloading the dataset. Preprocessing becomes the next stage which consists of tokenizing, stemming, converting abbreviations and eliminating conjunctions. In the preprocessed data, feature extraction is carried out by converting the dataset into vectors and applying the TF-IDF method before entering the sentiment analysis classification stage. Tests in this study were carried out by applying the minimum document frequency (min-df) and maximum document frequency (max-df) for each variation of naive Bayes to obtain the appropriate parameters. The test uses k-fold cross validation of the dataset to divide the training data and sentiment analysis test data. The next confusion matrix is made to evaluate the level of accuracy.

show abstract

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

Cited by 8 publications

References 2 publications

Sentiment Analysis of Nepali COVID-19 Tweets using BERT-LSTM

Sentiment Analysis of Nepali COVID-19 Tweets using BERT-LSTM

Comparative Evaluation of Transformer-Based Nepali Language Models

Application of Naïve Bayes Algorithm Variations On Indonesian General Analysis Dataset for Sentiment Analysis

Contact Info

Product

Resources

About