DeepEmotionNet: Emotion mining for corporate performance analysis and prediction

Abstract

Since previous studies in cognitive psychology show that individuals’ affective states can help analyze and predict their future behaviors, researchers have explored emotion mining for predicting online activities, firm profitability, and so on. Existing emotion mining methods are divided into two categories: feature-based approaches that rely on handcrafted annotations and deep learning-based methods that thrive on computational resources and big data. However, neither category can effectively detect emotional expressions captured in text (e.g., social media postings). In addition, the utilization of these methods in downstream explanatory and predictive applications is also rare. To fill the aforementioned research gaps, we develop a novel deep learning-based emotion detector named DeepEmotionNet that can simultaneously leverage contextual, syntactic, semantic, and document-level features and lexicon-based linguistic knowledge to bootstrap the overall emotion detection performance. Based on three emotion detection benchmark corpora, our experimental results confirm that DeepEmotionNet outperforms state-of-the-art baseline methods by 4.9% to 29.8% in macro-averaged F-score. For the downstream application of DeepEmotionNet to a real-world financial application, our econometric analysis highlights that top executives’ emotions of fear and anger embedded in their social media postings are significantly associated with corporate financial performance. Furthermore, these two emotions can significantly improve the predictive power of corporate financial performance when compared to sentiments. To the best of our knowledge, this is the first study to develop a deep learning-based emotion detection method and successfully apply it to enhance corporate performance prediction.