Sentiment Analysis in Tweets: Exploring Instance-based Transfer Learning for Dataset Enrichment - Guimarães, Eliseu; Vianna, Daniela; Paes, Aline; Plastino, Alexandre (Journal of Information and Data Management, 2023)
Due to the popularity of user-generated content driven by social networks, sentiment analysis has become a very rich and influential research field. A challenging problem in this classification task is curating sufficient labeled data to train a classifier with a good performance. In order to address that issue, a promising strategy is to enrich the dataset of interest with labeled data from other datasets of different domains. However, another issue emerges: how to properly select data from a broad set of datasets to improve the classifier’s performance. This manuscript presents instance-based transfer learning strategies to enrich the training set that is initially composed of the labeled target-dataset. Notably, we investigate the benefits of selecting similar and dissimilar instances from a set of source-datasets to transfer them to the target-dataset. Our results show that one of the strategies produces statistically significant performance improvement and that diversity plays an essential role in enhancing performance.