A swarm-based multi-objective approach for sentiment-oriented generic summarization: application to tweets

Abstract Nowadays, automatic text summarization task is a matter that has acquired special relevance in numerous contexts. Particularly, sentiment analysis and opinion mining need summarization methods to quickly analyze public opinion about any event. In this way, the aim of the sentiment-oriented summarization approach is to produce a summary reflecting the sentiment of the authors’ opinions, covering the main content, and reducing the redundancy. In this work, a Sentiment-Oriented Dominance-based Bee Algorithm (SODBA) has been designed, developed, and applied for solving this problem. Experimentation has been conducted with datasets provided by Document Understanding Conferences. The evaluation of the results has been carried out by using the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics and the Pearson correlation coefficient. The reported results have outperformed those obtained in the scientific literature in terms of ROUGE metrics. Moreover, SODBA has been applied to the tweets concerning the COVID-19 pandemic to obtain the summaries of the days with the most positive and the most negative sentiment.