Comparing and Combining Sentiment Analysis Methods
Table 2: Summary information of the six major topics events studied
['Topic', 'Period', 'Keywords']
To extract tweets relevant to these events, we first identified the sets of keywords describing the topics by consulting news websites, blogs, Wikipedia, and informed individuals. Given our selected list of keywords, we identified the topics by searching for keywords in the tweet dataset. This process is very similar to the way in which mining and monitoring tools to crawl data about specific topics. We limited the duration of each event because popular keywords are typically hijacked by spammers after a certain amount of time. The first column contains a short name for the event, which we use to refer to them in the rest of the paper. While the table does not show the ground truth sentiment of the six events, we can utilize these events to compare the predicted sentiments across different methods. As shown in the figure, SentiWordNet and SenticNet have the highest coverage with 90% and 91%, respectively, followed by SentiStrength with 69%. Emoticons and PANAS-t can interpret less than 10% of all relevant tweets. In the other events SentiStrength, LIWC, and SASA had ranked in third and fourth positions.