It extracts opinion objects and opinion words based on CRF model [50] and judges whether the opinion sentence has sentiment words. If there are no sentiment words, the STAS ends directly. Otherwise, it jumps to the next step.
(2)
It matches each opinion text phrase through sentiment vocabulary ontology library and constructs the relationship between opinion objects and opinion phrases.
(3)
It traverses opinion phrases to match sentiment words in opinion objects by using sentiment vocabulary ontology library. If sentiment word exists, STAS changes it to sentiment type, calculates the sentiment polarity according to inversion words, and is stored as Triple<Opinions Service, Sentiment Type, Sentiment Tendency Value>. If it does not exist, it outputs as Triple<Opinions Service, “neutral,” 0>.
(4)
STAS repeats step 1 to step 3, until it judges all the opinion objects.
The overall sentiment tendency of opinion objects is calculated by weight values of comment words, emotion words, and reverse words. STAS quantifies the sentiment type of opinion words based on artificial tagging and fuzzy set theory [50] and adopts the membership degree to predict its value. Besides, it calculates the similarity between and based on Levenstein edit distance and predicts the sentiment tendency value of sentiment words. Thirdly, it calculates the sentiment tendency value of reverse words by the Triple<opinions object, reverse words, sentiment polarity> and PMI-IR algorithm [51]:
,
where is the Levenstein edit distance between .
,
where word is the target word whose sentiment type is unknown, and and are the positive sentiment vocabulary set and negative sentiment vocabulary set, respectively, in basic sentiment words.
