  Detecting emotions in limited text datasets from under-resourced languages
presents a formidable obstacle, demanding specialized frameworks and
computational strategies. This study conducts a thorough examination of deep
learning techniques for discerning emotions in short English texts. Deep
learning approaches employ transfer learning and word embedding, notably BERT,
to attain superior accuracy. To evaluate these methods, we introduce the
"SmallEnglishEmotions" dataset, comprising 6372 varied short English texts
annotated with five primary emotion categories. Our experiments reveal that
transfer learning and BERT-based text embedding outperform alternative methods
in accurately categorizing the text in the dataset.
