Bangla hate speech detection on social media using attention-based recurrent neural network

Das, Amit Kumar; Asif, Abdullah Al; Paul, Anik; Hossain, Md. Nur

doi:10.1515/jisys-2020-0060

Cited by 74 publications

(23 citation statements)

References 27 publications

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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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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.

View full text Add to dashboard Cite

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“…Now the visual soft attention mechanism comes to play to classify the images based on the features obtained from the inceptionv3 model. The attention model is a streamlined attempt that focuses on the few relevant things of the selective activities [18]. As a result, this classification system focuses on human faces and determines whether or not the individual is wearing a mask.…”

Section: Facemask Detectionmentioning

confidence: 99%

A Real-time Face Mask Detection and Social Distancing System for COVID-19 using Attention-InceptionV3 Model

Asif

Tisha

2022

J. eng. adv.

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One of the deadliest pandemics is now happening in the current world due to COVID-19. This contagious virus is spreading like wildfire around the whole world. To minimize the spreading of this virus, World Health Organization (WHO) has made protocols mandatory for wearing face masks and maintaining 6 feet physical distance. In this paper, we have developed a system that can detect the proper maintenance of that distance and people are properly using masks or not. We have used the customized attention-inceptionv3 model in this system for the identification of those two components. We have used two different datasets along with 10,800 images including both with and without Face Mask images. The training accuracy has been achieved 98% and validation accuracy 99.5%. The system can conduct a precision value of around 98.2% and the frame rate per second (FPS) was 25.0. So, with this system, we can identify high-risk areas with the highest possibility of the virus spreading zone. This may help authorities to take necessary steps to locate those risky areas and alert the local people to ensure proper precautions in no time.

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“…Where V k represents k-time input, h k represents the hidden form at time k, and W kD represents k-time output [12].…”

Section: Recurrent Neural Network Analysismentioning

confidence: 99%

Design of English Translation Mobile Information System Based on Recurrent Neural Network

Gao¹

2022

Mobile Information Systems

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To solve the problem of translating lines of difference in length into English, this article presents a model of neural network recovery (RNN) English translator-based models of end-to-end encoder-decoder. This method promotes machine autonomous learning of features and transforms corpus data into word vectors by constructing end-to-end. By mapping the source language and target language directly through the recurrent neural network and selecting semantic error to construct objective function during training, the influence of each part in semantic can be well balanced, and the alignment information is fully considered, which provides powerful guidance for deep recurrent neural network training. The results of the neural network test define the standard BLEU score by 1.51–11.86. Our test scores and BLEU scores at all levels show that data in equivalence play an important role in modeling. Summary. the English translation model based on the neural repetitive fusion is efficient and stable.

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Bangla hate speech detection on social media using attention-based recurrent neural network

Cited by 74 publications

References 27 publications

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

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

A Real-time Face Mask Detection and Social Distancing System for COVID-19 using Attention-InceptionV3 Model

Design of English Translation Mobile Information System Based on Recurrent Neural Network

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