SemEval-2015 Task 10: Sentiment Analysis in Twitter
Table 14: Results for Subtask E: Degree of Prior Polarity. The systems are ordered by their Kendall’s τ score, which was the official score.
['[BOLD] Team', '[BOLD] Kendall’s  [ITALIC] τ', '[BOLD] Spearman’s  [ITALIC] ρ']
The last row shows the performance of a lexicon-based baseline. These lexicons have real-valued sentiment scores for most of the terms in the test set. For negated phrases, we use the scores of the corresponding negated entries in the lexicons. For each term, we take its score from the Sentiment140 Lexicon if present; otherwise, we take the term’s score from the Hashtag Sentiment Lexicon. For terms not found in any lexicon, we use the score of 0, which indicates a neutral term in these lexicons. The top three teams were able to improve over the baseline.