Neural Network Based Bengali News Headline Multi Classification System: Selection of Features describes Comparative Performance

Khushbu, Sharun Akter; Masum, Abu Kaisar Mohammad; Abujar, Sheikh; Hossain, Syed Akhter

doi:10.1109/icccnt49239.2020.9225611

Cited by 11 publications

(3 citation statements)

References 15 publications

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“…They used unigrams, part-of-speech tags, sentiment-topic, and semantic features on STS, HCR, and OMD Twitter datasets. Khushbu et al [11] proposed a neural network-based method to classify Bengali news headlines.…”

Section: Related Work and Datasetsmentioning

confidence: 99%

“…We have looked at a few recent works that are related to our research to determine how well our suggested model predicts the polarity of newspaper headlines. The observation of the literature reveals that only two research works have been performed in Bengali by Khushbu et al[11] and Das and Bandyopadhyay[12] on the classification of Bengali news headlines and phrase-level polarity on Bengali news texts, using some classical machine learning techniques. There has been no previous research on predicting the polarity (positive or negative sentiment) of Bengali news headlines using both deep learning and classical machine learning techniques.…”

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confidence: 99%

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Prediction of Bengali Newspaper Headline Polarity Using Machine Learning Techniques

Barua

Raza

Islam

et al. 2023

Preprint

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Automated detection of the sentiment of newspaper headlines can save time and effort in reading and help to create a positive environment by reducing the prevalence of negative news and promoting positive news. Negative news can have a variety of effects on society, including Increased fear and anxiety, Desensitization to violence and tragedy, Decreased trust in institutions, Polarization, and division, and Decreased well-being. In this study, we propose a system for detecting the positivity or negativity of newspaper headlines in Bangla. While there has been a significant amount of research on detecting the sentiment of English newspaper headlines, there has been none in Bangla. To build our dataset, we used a large number of Bangla newspaper headlines. To categorize these Bangla newspaper headlines, three classic machine learning models, e.g. support vector machine (SVM), logistic regression, and random forest, and one deep learning model, e.g. sequential model are utilized. We then applied these three classical machine learning models as well as a deep learning model to the dataset following a machine learning approach. The obtained results are quantified in terms of four inquisitive performance metrics – accuracy, F1-score, precision, and recall. The results showed that the sequential deep learning model surpassed all other classical machine learning models achieving an accuracy of 80% F1-Score of 79% Precision of 87% and Recall of 76%.

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Section: Related Work and Datasetsmentioning

confidence: 99%

mentioning

confidence: 99%

Prediction of Bengali Newspaper Headline Polarity Using Machine Learning Techniques

Barua

Raza

Islam

et al. 2023

Preprint

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show abstract

“…According to the study by Khushbu and Masum et al, specifying the news genre in Bengali news headlines can enhance their specificity to a greater extent [7]. Consequently, the machine can intelligently analyze the sequence of sentences in the output to discern the news genre.…”

Section: Researchers Of Articlementioning

confidence: 99%

Classifying Bengali Newspaper Headlines with Advanced Deep Learning Models: LSTM, Bi-LSTM, and Bi-GRU Approaches

Hasan,

Islam,

Ejaz

et al. 2023

AJRCoS

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Reading newspapers is beneficial for people of all ages and the global community. The enjoyment of gathering diverse data from various sources adds to the overall experience. To enhance specificity in Bengali news headlines, recognizing the news genre becomes crucial. Recognizing the genre of the news, it is a very challenging task in Bengali Text Classification with the help of AI. A very few research works is done on Bengali News headline classification and we have done a model to provide a solution to the addressed issue. Due to the continuous change of the structure of the news headlines, we have employed a neural network adoption connection to our methodology experiment on a mixture of primary and secondary dataset. Achieving significant results, we implemented a Bengali dataset in Multi Classification using Long-Short Term Memory (LSTM), Bi- Long-Short Term Memory (Bi-LSTM), and Bi-Gated Recurrent Unit (Bi-GRU). The dataset is established by aggregating news headlines from various Bengali news portals and websites, showcasing robust categorization performance in the end product. Six categories were employed for the classification of Bengali newspaper headlines. The Bi-LSTM Model emerged with the highest training accuracy at 97.96% and the lowest validation accuracy at 77.91%. Furthermore, it demonstrated enhanced sensitivity and specificity.

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Multi-class Short Text Classification Using Ensemble of Deep Learning Classifier

Jannat

Hossain²,

Hoque³

et al. 2022

Intelligent Computing &Amp; Optimization

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Neural Network Based Bengali News Headline Multi Classification System: Selection of Features describes Comparative Performance

Cited by 11 publications

References 15 publications

Prediction of Bengali Newspaper Headline Polarity Using Machine Learning Techniques

Prediction of Bengali Newspaper Headline Polarity Using Machine Learning Techniques

Classifying Bengali Newspaper Headlines with Advanced Deep Learning Models: LSTM, Bi-LSTM, and Bi-GRU Approaches

Multi-class Short Text Classification Using Ensemble of Deep Learning Classifier

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