SentiBench - a benchmark comparison of state-of-the-practice sentiment analysis methods
Tabela 12: Mean Rank Table for Datasets of Comments
['3-Classes  [BOLD] Pos', '3-Classes  [BOLD] Method', '3-Classes  [BOLD] Mean Rank', '2-Classes  [BOLD] Pos', '2-Classes  [BOLD] Method', '2-Classes  [BOLD] Mean Rank', '2-Classes  [BOLD] Coverage (%)']
In the context of Social Networks the best method for 3-class experiments was Umigon, followed by LIWC15 and VADER. In the case of 2-class the winner was SentiStrength with a coverage around 30% and the third and sixth place were Emoticons and Panas-t with about 18% and 6% of coverage, respectively. This highlights the importance to analyze the 2-class results together with the coverage. Overall, when there is an emoticon on the text or a word from the psychometric scale PANAS, these methods are able to tell the polarity of the sentences, but they are not able to identify the polarity of the input text for the large majority of the input text. Sentiment140, LIWC15, Semantria, OpinionLexicon and Umigon showed to be the best alternatives for detecting only positive and negative polarities in social network data due to the high coverage and prediction performance. It is important to highlight that LIWC 2007 appears on the 16th and 21th position for the 3-class and 2-class mean rank results for the social network datasets and it is a very popular method in this community. On the other side, the newest version of LIWC (2015) presented a considerable evolution obtaining the second and the fourth place in the same datasets.