NRC-Canada: Building the State-of-the-Art inSentiment Analysis of Tweets
Table 4: Term-level Task: The macro-averaged F-scores on the datasets. The official scores of our submission are shown in bold. SVM-all* shows results after a bug fix.
['[EMPTY]', '[BOLD] Classifier', '[BOLD] Tweets', '[BOLD] SMS']
The table also shows baseline results obtained by a majority classifier that always predicts the most frequent class as output, and an additional baseline result obtained using an SVM and unigram features alone. Our submission obtained a macro-averaged F-score of 88.93 on the tweet set and was ranked first among 29 submissions from 23 participating teams. Even with no tuning specific to SMS data, our SMS submission still obtained second rank with an F-score of 88.00. The score of the first ranking system on the SMS set was 88.39. A post-competition bug-fix in the bigram features resulted in a small improvement: F-score of 89.10 on the tweets set and 88.34 on the SMS set.