Identifying Purpose Behind Electoral Tweets
Table 15: Accuracy of classification using different lexicons on the 11-class problem. Numbers in bold represent statistically significant difference with the accuracy of the classifier using the NRC Emotion Lexicon (first line) with 95% confidence.
['[BOLD] Lexicon', '[BOLD] Accuracy']
We used the Hashtag Lexicon for classification by creating a separate feature for each emotion-related hashtag, resulting in 585 emotion features. The values of these features were calculated as the sum of the PMI scores between the words in a tweet and the corresponding emotion-related hashtag. The Hashtag Lexicon significantly improved the performance of the classifier on the 11-category task.