DeepEmo: Learning and Enriching Pattern-Based Emotion Representations
Table 5: F1 average comparison of our method against other notable published literature.
['[BOLD] Method', '[BOLD] Data Size', '[BOLD] F1-score']
Comparison to state-of-the-art We also compare results with published literature, which employ emotion recognition systems using Ekman’s six basic emotions. For fair comparison, we reduced our dataset from eight emotions to six emotions: anger, disgust, fear, joy, sadness, and surprise. Their emotion recognition system performs better than ours (F1-score of 78%) since they use well-defined linguistic features, such as emoticons and hashtags. Our features are more susceptible to noise because we aim for higher coverage in order to capture more implicit emotional expressions. This consideration is important if we intend to use the emotion lexicons for conducting deep analysis on affective datasets. In addition, their features are domain-specific, which means some important features, such as emoticons and hashtags, may not be applicable to other affective datasets. According to Zhang et al. We plan to continue enlarging our datasets and refining pattern weights, which are feasible methods to improve results.