Comparing and Combining Sentiment Analysis Methods
Table 5: Average prediction performance for all labeled dataset.
['[BOLD] Metric', '[BOLD] PANAS-t', '[BOLD] Emoticons', '[BOLD] SASA', '[BOLD] Sentic-  [BOLD] Net', '[BOLD] Senti-  [BOLD] WordNet', '[BOLD] Happiness  [BOLD] Index', '[BOLD] Senti-  [BOLD] Strength', '[BOLD] LIWC']
For the F-measure, a score of 1 is ideal and 0 is the worst possible. The method with the best F-measure was Emoticons (0.846), which had the lowest coverage. The second best method in terms of F-measure is SentiStrength, which obtained a much higher coverage than Emoticons. It is important to note that the SentiStrength version we are using is already trained, probably with this entire dataset. Thus, running experiments with SentiStrength using this dataset would be potentially biased, as it would be training and testing with the same dataset. Instead, we compute the prediction performance metrics for SentiStrengh based on the numbers they reported in their experiments