A Dynamic Strategy for Classifying Sentiment From Bengali Text by Utilizing Word2vector Model

Rahman, Mafizur; Talukder, Md. Rifayet Azam; Setu, Lima Akter; Das, Amit Kumar

doi:10.4018/jitr.299919

Cited by 4 publications

(3 citation statements)

References 31 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: 56%

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: 56%

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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“…Here, real IMDB and Amazon data sets were employed to estimate the suggested method performance. Similarly many approaches related to multi class sentiment classification [23][24][25][26][27] has been done in the previous studies but all those methods require better performance in terms of accuracy and the summary of literature review is presented in table 1 as shown below.…”

Section: Cognitive-inspired Domain Adaptationmentioning

confidence: 99%

Novel heuristic-based hybrid ResNeXt with recurrent neural network to handle multi class classification of sentiment analysis

Revathi

Satish²

2023

Mach. Learn.: Sci. Technol.

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Present-day, interdisciplinary research is increasing in social network-related applications, and it is a daily routine activity in every human life. So, sentiment analysis based on opinion mining is the most sophisticated concept in the well-known social network environment. Different machine learning methods were implemented to extract different text label features in sentiment analysis, and all of those methods can detect whether a given text is positive or negative based on the text features. Analysis of sentiment has been suffering from inaccuracies while using machine learning and sentiment-based lexical methods dependent on domain-specific problems. Multi-class sentiment analysis is an expensive task where memory, label samples, and other parameters are insufficient. So, we propose and implement a Novel Hybrid model which is a combination of ResNeXt and Recurrent Neural Framework (NH-ResNeXt-RNF) to explore multi-class sentiment from textual features. This framework investigates the polarity of words connected to a specific domain across the entire dataset and eliminates noisy data in an unsupervised manner using pre-processing .Optimization is required to perform efficient multi-class classification to reduce the effort associated with annotation for multi-class sentiment analysis via unsupervised learning. The proposed model performance is evaluated on two data sets namely: Amazon and Twitter. We increase the accuracy of the sentiment of polarity on each sentence present in the data set. Experimental results of the proposed approach give better and more efficient multi-class (positive, negative, very positive, neutral and highly negative) domain-specific sentiment than traditional approaches related to supervised, semi-supervised, and unsupervised domains. The proposed hybrid model accuracy is 96.5% and 95.37% for Amazon and Twitter datasets respectively.

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“…They employed BERT, a language model, to tackle this issue and achieved a commendable accuracy of 88%, demonstrating its effectiveness. Finally, a study conducted by the authors [29] investigated the application of Skip-gram for multiclass comment classification. The results of this study indicated that an accuracy rate of 75% was achieved.…”

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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A Dynamic Strategy for Classifying Sentiment From Bengali Text by Utilizing Word2vector Model

Cited by 4 publications

References 31 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

Novel heuristic-based hybrid ResNeXt with recurrent neural network to handle multi class classification of sentiment analysis

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

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