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

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.

Exploring Customer Review of Local Agriculture Product Acceptance in Malaysia: A Concept Paper on Sentiment Mining

“…Most of the sentiment analysis in agriculture studies investigating the opinion of the price hike [74]- [76] and related to smart farming acceptance [77]- [81]. It is of vital importance to take into consideration the after-effect of the protests and riots by farmers, due to the economic problems, lack of subsidy and the spike in food and commodity prices [82]- [84]. These sentiments are often expressed by them on social media.…”

Effective Twitter Sentiment Analysis Using Deep Belief Network with Enhanced Dragonfly Optimization Algorithm

“…However, the random forest classifier was ineffective and slow in real time prediction like sentiment analysis of farmers' protest. On the other hand, Tiwari and Nagpal [15] presented knowledge-enriched attention-based hybrid transformer (KEAHT) with bidirectional encoder representation from transformer (BERT) model for social sentiment evaluation. The experimental investigation confirmed that the developed model has achieved high classification results by means of accuracy, precision, F1-score and recall, but it was computationally complex.…”

Performing Stance Classification and Bot Detection on the Indian Farmers’ Protest – A Study to Unveil Hidden Perspectives.

“…al. use word embeddings that are fed into Bidirectional Encoder Representation from Transformer (BERT) to perform sentiment analysis of farmers protest tweets [9]. The work reports an overall positive and neutral sentiment of tweets (81%) towards the farmers' protest.…”