1,720,977 research outputs found
A Bi-Directional GRU Architecture for the Self-Attention Mechanism: An Adaptable, Multi-Layered Approach with Blend of Word Embedding
Get PDFSentiment analysis (SA) has become an essential component of natural language processing (NLP) with numerous practical applications to understanding “what other people think”. Various techniques have been developed to tackle SA using deep learning (DL); however, current research lacks comprehensive strategies incorporating multiple-word embeddings. This study proposes a self-attention mechanism that leverages DL and involves the contextual integration of word embedding with a time-dispersed bidirectional gated recurrent unit (Bi-GRU). This work employs word embedding approaches GloVe, word2vec, and fastText to achieve better predictive capabilities. By integrating these techniques, the study aims to improve the classifier’s capability to precisely analyze and categorize sentiments in textual data from the domain of movies. The investigation seeks to enhance the classifier’s performance in NLP tasks by addressing the challenges of underfitting and overfitting in DL. To evaluate the model’s effectiveness, an openly available IMDb dataset was utilized, achieving a remarkable testing accuracy of 99.70%
A Novel Paradigm for Sentiment Analysis on COVID-19 Tweets with Transfer Learning Based Fine-Tuned BERT
Get PDFThe rapid escalation in global COVID-19 cases has engendered profound emotions of fear, agitation, and despondency within society. It is evident from COVID-19-related tweets that spark panic and elevate stress among individuals. Analyzing the sentiment expressed in online comments aids various stakeholders in monitoring the situation. This research aims to improve the performance of pre-trained bidirectional encoder representations from transformers (BERT) by employing transfer learning (TL) and fine hyper-parameter tuning (FT). The model is applied to three distinct COVID-19-related datasets, and each of the datasets belongs to a different class. The evaluation of the model’s performance involves six different machine learning (ML) classification models. This model is trained and evaluated using metrics such as accuracy, precision, recall, and F1-score. Heat maps are generated for each model to visualize the results. The performance of the model demonstrates accuracies of 83%, 97%, and 98% for Class-5, Class-3, and binary classifications, respectively
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