KEAHT: A Knowledge-Enriched Attention-Based Hybrid Transformer Model for Social Sentiment Analysis View Full Text


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Article Info

DATE

2022-07-11

AUTHORS

Dimple Tiwari, Bharti Nagpal

ABSTRACT

Social media materialized as an influential platform that allows people to share their views on global and local issues. Sentiment analysis can handle these massive amounts of unstructured reviews and convert them into meaningful opinions. Undoubtedly, COVID-19 originated as the enormous challenge across the world that physically and financially bruted humankind. Meanwhile, farmers' protests shook up the world against three pieces of legislation passed by the Indian government. Hence, an artificial intelligence-based sentiment model is needed for suggesting the right direction toward outbreaks. Although Deep Neural Network (DNN) gained popularity in sentiment analysis applications, these still have a limitation of sequential training, high-dimension feature space, and equal feature importance distribution. In addition, inaccurate polarity scoring and utility-based topic modeling are other challenging aspects of sentiment analysis. It motivates us to propose a Knowledge-Enriched Attention-based Hybrid Transformer (KEAHT) model by enriching the explicit knowledge of Latent Dirichlet Allocation (LDA) topic modeling and lexicalized domain ontology. A pre-trained Bidirectional Encoder Representation from Transformer (BERT) is employed to train within a minimum training corpus. It provides the facility of attention mechanism and can solve complex text problems accurately. A comparative study with existing baselines and recent hybrid models affirms the credibility of the proposed KEAHT in the field of Natural Language Processing (NLP). This model emphasizes artificial intelligence's role in handling the situation of the global pandemic and democratic dispute in a country. Furthermore, two benchmark datasets, namely “COVID-19-Vaccine-Labelled-Tweets" and "Indian-Farmer-Protest-Labelled-Tweets”, are also constructed to accommodate future researchers for outlining the essential facts associated with the outbreaks. More... »

PAGES

1-38

References to SciGraph publications

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  • 2018-12-18. Incorporating word embeddings into topic modeling of short text in KNOWLEDGE AND INFORMATION SYSTEMS
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  • 2015-12-09. Improved lexicon-based sentiment analysis for social media analytics in SECURITY INFORMATICS
  • 2018-08-10. Deep Learning Based Sentiment Analysis Using Convolution Neural Network in ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
  • 2018-03-14. Sentic LSTM: a Hybrid Network for Targeted Aspect-Based Sentiment Analysis in COGNITIVE COMPUTATION
  • 2021-11-27. Aspect-Based Sentiment Analysis Using Attribute Extraction of Hospital Reviews in NEW GENERATION COMPUTING
  • 2019-01. Enhanced news sentiment analysis using deep learning methods in JOURNAL OF COMPUTATIONAL SOCIAL SCIENCE
  • 2016-09-20. A semi-supervised approach to sentiment analysis using revised sentiment strength based on SentiWordNet in KNOWLEDGE AND INFORMATION SYSTEMS
  • 2018-10-31. Boosting Holistic Ontology Matching: Generating Graph Clique-Based Relaxed Reference Alignments for Holistic Evaluation in KNOWLEDGE ENGINEERING AND KNOWLEDGE MANAGEMENT
  • 2017-12-13. Hybrid sentiment classification on twitter aspect-based sentiment analysis in APPLIED INTELLIGENCE
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