Discovering Basic Emotion Sets viaSemantic Clustering on a Twitter Corpus
Table 3.1: Mean average percentage of positive tweets using Twitter Sentiment (TS) and Emotion Keyword Extraction (EK) of five example keywords over a three day period.
['[BOLD] Term', '[BOLD] Ave(TS)Pos%', '[BOLD] Ave(EK)Pos%', '[BOLD] Diff%']
Although sentiment is easier to classify — indeed it is just polarity — using specific emotions rather than a valence ratio of words provides a greater insight into the dimensions, and thus nature, of an expressed feeling. We conduct a brief preliminary comparison of basic emotion extraction using keyword spotting and sentiment extraction using Twitter Sentiment to reveal any similarities and discrepancies as a result of using the increased dimensionality of emotion. As our emotion keywords we use Humaine’s Emotion Annotation and Representation Language (EARL) (2006) Using this set of emotion keywords, we created a database of 1.5 million tweets in three days using the Twitter streaming API.