Context-Sensitive Malicious Spelling Error Correction
Table 6: Hate speech detection results with different spelling corrections
['Category', 'Metric', 'Original', 'Our system', 'PyEnchant', 'Ekphrasis', 'Google']
Results. We do a random 80:20 train-test split of the Twitter dataset. A detection system was trained on the normalized train data (without spelling correction) using the state-of-the-art BLSTM model. There were 3218 test tweets, 571 of which had misspellings. We applied the different spelling correction approaches to these 571 tweets, and the corrected tweets were then cleaned as described in the tweet normalization stage. Processed tweets were input to the trained detection system. We compared the hate speech detection results on the 571 test tweets to evaluate the effect of spelling correction on this downstream application. We report precision, recall and F1-score of racist and sexist classification respectively and the macro-averages (to evaluate the overall performance).