DeepEmo: Learning and Enriching Pattern-Based Emotion Representations
Table 2: Comparison of our model against conventional feature extractors using F1-score. LIWC uses a bag of words approach. word2vec model adopts pre-trained embeddings from Mikolov et al. (2013). char refers to character-level features. n-gram employ unigrams, bigrams, and trigrams as features. CNN-patt uses the proposed CNN architecture with basic patterns.
['[BOLD] Models', '[BOLD] Features', '[BOLD] anger', '[BOLD] anticipation', '[BOLD] disgust', '[BOLD] fear', '[BOLD] joy', '[BOLD] sadness', '[BOLD] surprise', '[BOLD] trust', '[BOLD] F1 Avg.']
Traditional Feature Extractors As the table shows, TF-IDF models usually produce better results than basic count-based features for both character-level and word-level feature extractors. These findings are consistent with the work of Zhang et al.