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All Journal International Journal of Electrical and Computer Engineering
Gurusamy, Bharathi Mohan
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Computer Science & IT Electrical & Electronics Engineering

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A hybrid approach for text summarization using semantic latent Dirichlet allocation and sentence concept mapping with transformer Gurusamy, Bharathi Mohan; Rengarajan, Prasanna Kumar; Srinivasan, Parthasarathy
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6663-6672

Abstract

Automatic text summarization generates a summary that contains sentences reflecting the essential and relevant information of the original documents. Extractive summarization requires semantic understanding, while abstractive summarization requires a better intermediate text representation. This paper proposes a hybrid approach for generating text summaries that combine extractive and abstractive methods. To improve the semantic understanding of the model, we propose two novel extractive methods: semantic latent Dirichlet allocation (semantic LDA) and sentence concept mapping. We then generate an intermediate summary by applying our proposed sentence ranking algorithm over the sentence concept mapping. This intermediate summary is input to a transformer-based abstractive model fine-tuned with a multi-head attention mechanism. Our experimental results demonstrate that the proposed hybrid model generates coherent summaries using the intermediate extractive summary covering semantics. As we increase the concepts and number of words in the summary the rouge scores are improved for precision and F1 scores in our proposed model.
The social media sentiment analysis framework: deep learning for sentiment analysis on social media Rangarjan, Prasanna Kumar; Gurusamy, Bharathi mohan; Muthurasu, Gayathri; Mohan, Rithani; Pallavi, Gundala; Vijayakumar, Sulochana; Altalbe, Ali
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3394-3405

Abstract

Researching public opinion can help us learn important facts. People may quickly and easily express their thoughts and feelings on any subject using social media, which creates a deluge of unorganized data. Sentiment analysis on social media platforms like Twitter and Facebook has developed into a potent tool for gathering insights into users' perspectives. However, difficulties in interpreting natural language limit the effectiveness and precision of sentiment analysis. This research focuses on developing a social media sentiment analysis (SMSA) framework, incorporating a custom-built emotion thesaurus to enhance the precision of sentiment analysis. It delves into the efficacy of various deep learning algorithms, under different parameter calibrations, for sentiment extraction from social media. The study distinguishes itself by its unique approach towards sentiment dictionary creation and its application to deep learning models. It contributes new insights into sentiment analysis, particularly in social media contexts, showcasing notable advancements over previous methodologies. The results demonstrate improved accuracy and deeper understanding of social media sentiment, opening avenues for future research and applications in diverse fields.
Co-Authors Altalbe, Ali Mohan, Rithani Muthurasu, Gayathri Pallavi, Gundala Rangarjan, Prasanna Kumar Rengarajan, Prasanna Kumar Srinivasan, Parthasarathy Vijayakumar, Sulochana