Transfer Learning for Sentiment Analysis Using BERT Based Supervised Fine-Tuning

Prottasha, Nusrat Jahan; Sami, Abdullah As; Kowsher, Md.; Murad, Saydul Akbar; Bairagi, Anupam Kumar; Masud, Mehedi; Baz, Mohammed

doi:10.3390/s22114157

Cited by 107 publications

(52 citation statements)

References 44 publications

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“…In recent years, a few Bangla language researchers have demonstrated a keen interest in Bangla corpus development and Bangla text analysis. Some notable progress has been documented in corpus creation in [15], [16], [17], [18], [19], [20] and knowledge engineering on Bangla language in [21], [22], [23], [24], etc. The first electronic Bangla corpus was constructed by the Central Institute of Indian Languages (CIIL) from 1991 to 1995 [25].…”

Section: Bangla Language Corpusmentioning

confidence: 99%

Compilation, Analysis and Application of a Comprehensive Bangla Corpus KUMono

et al. 2022

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Research in Natural Language Processing (NLP) and computational linguistics highly depends on a good quality representative corpus of any specific language. Bangla is one of the most spoken languages in the world but Bangla NLP research is in its early stage of development due to the lack of quality public corpus. This article describes the detailed compilation methodology of a comprehensive monolingual Bangla corpus, KUMono. The newly developed corpus consists of more than 350 million word tokens and more than one million unique tokens from 18 major text categories of online Bangla websites. We have conducted several word-level and character-level linguistic phenomenon analyses based on empirical studies of the developed corpus. The corpus follows Zipf's curve and hapax legomena rule. The quality of the corpus is also assessed by analyzing and comparing the inherent sparseness of the corpus with existing Bangla corpora, by analyzing the distribution of function words of the corpus and vocabulary growth rate. We have developed a Bangla article categorization application based on the KUMono corpus and received compelling results by comparing to the state-of-the-art models.

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Section: Bangla Language Corpusmentioning

confidence: 99%

Compilation, Analysis and Application of a Comprehensive Bangla Corpus KUMono

et al. 2022

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“…As a result, BERT was fine-tuned on the ASAP dataset in our study in order to improve its ability to score the essays. Fine tuning is an essential step when using language models because it provides language context which can enhance the performance of the model (Prottasha, et al, 2022). The baseline BERT model is first used for the task of classifying texts and scoring essays using the ASAP dataset.…”

Section: Transformer-based Modeling and Active Learning As Applied To...mentioning

confidence: 99%

Using Active Learning Methods to Strategically Select Essays for Automated Scoring

Firoozi

Mohammadi²,

Gierl

2022

Educational Measurement

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Research on automated essay scoring has become increasing important because it serves as a method for evaluating students' written-responses at scale. Scalable methods for scoring written responses are needed as students migrate to online learning environments resulting in the need to evaluate large numbers of written-response assessments. The purpose of this study is to describe and evaluate three active learning methods than can be used to minimize the number of essays that must be scored by human raters while still providing the data needed to train a modern automated essay scoring system. The three active learning methods are the uncertainty-based, the topological-based, and the hybrid method. These three methods were used to select essays included as part of the Automated Student Assessment Prize competition that were then classified using a scoring model that was training with the bidirectional encoder representations from transformer language model. All three active learning methods produced strong results, with the topological-based method producing the most efficient classification. Growth rate accuracy was also evaluated. The active learning methods produced different levels of efficiency under different sample size allocations but, overall, all three methods were highly efficient and produced classifications that were similar to one another.

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“…There are a number of tools which are used to carry out sentiment analysis and text analytics. These include lexicon based, machine learning based and deep learning-based methods [8,9].…”

Section: Related Workmentioning

confidence: 99%

Deciphering Voice of the Customer using Text Analytics and Sentiment Analysis: An Interpretable Review Rating Prediction using RoBERTa

U.C.

N.M.

2022

IJRITCC

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In this era of cut-throat competition among traditional and newer digital organizations, capturing, listening, and understanding customer voices are critical for success in the marketplace. The challenge to decipher the voice of the customer (VOC) has multiplied many times today, as now the number of customer reviews are present in multiple platforms and the data to be analyzed is huge. Sentiment analysis, and text analytics using machine learning, deep learning tools and transformer-based tools can be applied to gather meaningful insights from these data. This paper applies the traditional machine learning tools of the Naive Bayes classifier, Random Forest and AdaBoost to predict the customer review ratings. These results are compared with deep learning methods of CNN, RNN and Bi-LSTM and transformer-based approaches of BERT, DistilBERT and RoBERTa. The results show that RoBERTa has the highest accuracy among these methods. Paper also uses the explainable AI tool of LIME to understand the customer sentiments deeper in terms of why customers are giving a particular rating to the product. Business organizations will continue to use more and more AI tools to understand the customer feedback and the attempt in this paper is to learn how we can make predictions faster and more accurately.

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Transfer Learning for Sentiment Analysis Using BERT Based Supervised Fine-Tuning

Cited by 107 publications

References 44 publications

Compilation, Analysis and Application of a Comprehensive Bangla Corpus KUMono

Compilation, Analysis and Application of a Comprehensive Bangla Corpus KUMono

Using Active Learning Methods to Strategically Select Essays for Automated Scoring

Deciphering Voice of the Customer using Text Analytics and Sentiment Analysis: An Interpretable Review Rating Prediction using RoBERTa

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