An Attention on Sentiment Analysis of Child Abusive Public Comments Towards Bangla Text and ML

Lucky, Effat Ara Easmin; Sany, Mahadi Hasan; Keya, Mumenunnessa; Khushbu, Sharun Akter; Noori, Sheak Rashed Haider

doi:10.1109/icccnt51525.2021.9580154

Cited by 14 publications

(3 citation statements)

References 10 publications

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“…Compared to the few studies conducted on multiclass sentiment analysis in Bangla using deep learning, the efficacy of our proposed approach with Bangla-BERT is significantly improved. In our dataset, Bangla-BERT classified multiclass sentiments with 88.78% accuracy, which is higher than previously proposed deep learning methods [15,35,36].…”

Section: A Insights and Implicationsmentioning

confidence: 57%

Advancement in Bangla Sentiment Analysis: A Comparative Study of Transformer-Based and Transfer Learning Models for E-commerce Sentiment Classification

Ahmed,

Shanto,

Jony

2023

J. Inf. Syst. Eng. Bus. Intell.

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Background: As a direct result of the Internet's expansion, the quantity of information shared by Internet users across its numerous platforms has increased. Sentiment analysis functions at a higher level when there are more available perspectives and opinions. However, the lack of labeled data significantly complicates sentiment analysis utilizing Bangla natural language processing (NLP). In recent years, nevertheless, due to the development of more effective deep learning models, Bangla sentiment analysis has improved significantly. Objective: This article presents a curated dataset for Bangla e-commerce sentiment analysis obtained solely from the "Daraz" platform. We aim to conduct sentiment analysis in Bangla for binary and understudied multiclass classification tasks. Methods: Transfer learning (LSTM, GRU) and Transformers (Bangla-BERT) approaches are compared for their effectiveness on our dataset. To enhance the overall performance of the models, we fine-tuned them. Results: The accuracy of Bangla-BERT was highest for both binary and multiclass sentiment classification tasks, with 94.5% accuracy for binary classification and 88.78% accuracy for multiclass sentiment classification. Conclusion: Our proposed method performs noticeably better classifying multiclass sentiments in Bangla than previous deep learning techniques. Keywords: Bangla-BERT, Deep Learning, E-commerce, NLP, Sentiment Analysis

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Section: A Insights and Implicationsmentioning

confidence: 57%

Advancement in Bangla Sentiment Analysis: A Comparative Study of Transformer-Based and Transfer Learning Models for E-commerce Sentiment Classification

Ahmed,

Shanto,

Jony

2023

J. Inf. Syst. Eng. Bus. Intell.

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“…The results of this investigation demonstrated a significant accuracy rate of 90.86%. In the context of multiclass settings, the problem of abusive remarks was examined by researchers [28]. They employed BERT, a language model, to tackle this issue and achieved a commendable accuracy of 88%, demonstrating its effectiveness.…”

Section: Literature Reviewmentioning

confidence: 99%

Mining User Opinions: A Balanced Bangla Sentiment Analysis Dataset for E-Commerce

Shanto,

Ahmed,

Jony

2023

Malaysian J. Sci. Adv. Tech.

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The utilization of sentiment analysis has gained significant importance as a valuable method for obtaining meaningful insights from textual data. The research progress in languages such as English and Chinese has been notable. However, there is a noticeable dearth of attention towards creating tools for sentiment analysis in the Bangla language. Currently, datasets are limited for Bangla sentiment analysis, especially balanced datasets capturing both binary and multiclass sentiment for e-commerce applications. This paper introduces a new sentiment analysis dataset from the popular Bangladeshi e-commerce site “Daraz”. The dataset contains 1000 reviews across 5 product categories, with both binary (positive/negative) and multiclass (very positive, positive, negative, very negative) sentiment labels manually annotated by native Bangla speakers. Reviews were collected using an organized process, and labels were assigned based on standardized criteria to ensure accuracy. In addition, a benchmark evaluation of the performance achieved by Machine Learning and Deep Learning algorithms on this dataset is also provided. The new dataset can aid research on multiclass and binary Bangla sentiment analysis utilizing both machine learning, deep learning, and Large Language Models. It can aid e-commerce platforms in analysing nuanced user opinions and emotions from online reviews. The utilization of categorized product reviews also facilitates research in the field of text categorization.

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“…Deep neural network models based on transformers have been used to detect abusive remarks on Bangla social media Aurpa et al (2021), Lucky et al (2021). Pre-training language architectures such as BERT (Bidirectional Encoder Representations from Transformers) and ELECTRA (Efficiency Learning an Encoder that Classifies Token Replacements Accurately) are used in conjunction.…”

Section: Related Workmentioning

confidence: 99%

Hope speech detection in YouTube comments

Chakravarthi

2022

Soc. Netw. Anal. Min.

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Recent work on language technology has tried to recognize abusive language such as those containing hate speech and cyberbullying and enhance offensive language identification to moderate social media platforms. Most of these systems depend on machine learning models using a tagged dataset. Such models have been successful in detecting and eradicating negativity. However, an additional study has lately been conducted on the enhancement of free expression through social media. Instead of eliminating ostensibly unpleasant words, we created a multilingual dataset to recognize and encourage positivity in the comments, and we propose a novel custom deep network architecture, which uses a concatenation of embedding from T5-Sentence. We have experimented with multiple machine learning models, including SVM, logistic regression, K-nearest neighbour, decision tree, logistic neighbours, and we propose new CNN based model. Our proposed model outperformed all others with a macro F1-score of 0.75 for English, 0.62 for Tamil, and 0.67 for Malayalam.

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An Attention on Sentiment Analysis of Child Abusive Public Comments Towards Bangla Text and ML

Cited by 14 publications

References 10 publications

Advancement in Bangla Sentiment Analysis: A Comparative Study of Transformer-Based and Transfer Learning Models for E-commerce Sentiment Classification

Advancement in Bangla Sentiment Analysis: A Comparative Study of Transformer-Based and Transfer Learning Models for E-commerce Sentiment Classification

Mining User Opinions: A Balanced Bangla Sentiment Analysis Dataset for E-Commerce

Hope speech detection in YouTube comments

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