Toward Integrated CNN-based Sentiment Analysis of Tweets for Scarce-resource Language—Hindi

Gupta, Vishal; Jain, Nikita; Shubham, Shubham; Madan, Agam; Chaudhary, Ankit; Xin, Qin

doi:10.1145/3450447

Cited by 30 publications

(13 citation statements)

References 20 publications

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“…Gupta et al ( 2021 ) also handle the problem of low resource languages, and they consider the issue of the Hindi Language as prime. They discussed the issues with the Hindi language, such as spelling variations due to many dialectics, co-reference resolution, and many more.…”

Section: Related Workmentioning

confidence: 99%

RETRACTED ARTICLE: An ensemble deep learning classifier for sentiment analysis on code-mix Hindi–English data

Pradhan¹,

Sharma²

2022

Soft Comput

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Section: Related Workmentioning

confidence: 99%

RETRACTED ARTICLE: An ensemble deep learning classifier for sentiment analysis on code-mix Hindi–English data

Pradhan¹,

Sharma²

2022

Soft Comput

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“…Supervised learning is a method used to prepare a set of decision-making rules that can help predict a known outcome (Gupta et al , 2021). These rules can be cited as examples.…”

Section: Methodsmentioning

confidence: 99%

A deep neural network-based approach for fake news detection in regional language

Katariya

Gupta

Arora

et al. 2022

IJWIS

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Purpose The current natural language processing algorithms are still lacking in judgment criteria, and these approaches often require deep knowledge of political or social contexts. Seeing the damage done by the spreading of fake news in various sectors have attracted the attention of several low-level regional communities. However, such methods are widely developed for English language and low-resource languages remain unfocused. This study aims to provide analysis of Hindi fake news and develop a referral system with advanced techniques to identify fake news in Hindi. Design/methodology/approach The technique deployed in this model uses bidirectional long short-term memory (B-LSTM) as compared with other models like naïve bayes, logistic regression, random forest, support vector machine, decision tree classifier, kth nearest neighbor, gated recurrent unit and long short-term models. Findings The deep learning model such as B-LSTM yields an accuracy of 95.01%. Originality/value This study anticipates that this model will be a beneficial resource for building technologies to prevent the spreading of fake news and contribute to research with low resource languages.

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“…A convolutional neural network (CNN) is a deep learning network structure that is more suitable for the information stored in the array's data structure. Like other neural network structures, CNN comprises an input layer, the memory stack of pooling and convolutional layers for extracting feature sets, and then a fully connected layer with a softmax classifier in the classification layer [64][65][66][67][68].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Part of speech tagging: a systematic review of deep learning and machine learning approaches

Chiche

Yitagesu

2022

J Big Data

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Natural language processing (NLP) tools have sparked a great deal of interest due to rapid improvements in information and communications technologies. As a result, many different NLP tools are being produced. However, there are many challenges for developing efficient and effective NLP tools that accurately process natural languages. One such tool is part of speech (POS) tagging, which tags a particular sentence or words in a paragraph by looking at the context of the sentence/words inside the paragraph. Despite enormous efforts by researchers, POS tagging still faces challenges in improving accuracy while reducing false-positive rates and in tagging unknown words. Furthermore, the presence of ambiguity when tagging terms with different contextual meanings inside a sentence cannot be overlooked. Recently, Deep learning (DL) and Machine learning (ML)-based POS taggers are being implemented as potential solutions to efficiently identify words in a given sentence across a paragraph. This article first clarifies the concept of part of speech POS tagging. It then provides the broad categorization based on the famous ML and DL techniques employed in designing and implementing part of speech taggers. A comprehensive review of the latest POS tagging articles is provided by discussing the weakness and strengths of the proposed approaches. Then, recent trends and advancements of DL and ML-based part-of-speech-taggers are presented in terms of the proposed approaches deployed and their performance evaluation metrics. Using the limitations of the proposed approaches, we emphasized various research gaps and presented future recommendations for the research in advancing DL and ML-based POS tagging.

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Toward Integrated CNN-based Sentiment Analysis of Tweets for Scarce-resource Language—Hindi

Cited by 30 publications

References 20 publications

RETRACTED ARTICLE: An ensemble deep learning classifier for sentiment analysis on code-mix Hindi–English data

RETRACTED ARTICLE: An ensemble deep learning classifier for sentiment analysis on code-mix Hindi–English data

A deep neural network-based approach for fake news detection in regional language

Part of speech tagging: a systematic review of deep learning and machine learning approaches

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