Improving Human Happiness Analysis Based on Transfer Learning: Algorithm Development and Validation

BACKGROUND: Happiness refers to the joyful and pleasant emotions that humans produce subjectively. It is the positive part of emotions, and it affects the quality of human life. Therefore, understanding human happiness is a meaningful task in sentiment analysis. OBJECTIVE: We mainly discuss 2 facets (Agency/Sociality) of happiness in this paper. Through analysis and research on happiness, we can expand on new concepts that define happiness and enrich our understanding of emotions. METHODS: This paper treated each happy moment as a sequence of short sentences, then proposed a short happiness detection model based on transfer learning to analyze the Agency and Sociality aspects of happiness. First, we utilized the unlabeled training set to retrain the pretraining language model Bidirectional Encoder Representations from Transformers (BERT) and got a semantically enhanced language model happyBERT in the target domain. Then, we got several single text classification models by fine-tuning BERT and happyBERT. Finally, an improved voting strategy was proposed to integrate multiple single models, and “pseudo data” were introduced to retrain the combined models. RESULTS: The proposed approach was evaluated on the public dataset happyDB. Experimental results showed that our approach significantly outperforms the baselines. When predicting the Agency aspect of happiness, our approach achieved an accuracy of 0.8653 and an F1 score of 0.9126. When predicting Sociality, our approach achieved an accuracy of 0.9367 and an F1 score of 0.9491. CONCLUSIONS: By evaluating the dataset, the comparison results demonstrated the effectiveness of our approach for happiness analysis. Experimental results confirmed that our method achieved state-of-the-art performance and transfer learning effectively improved happiness analysis.