4.step three.4. Identity regarding public interactions
From Wordnet, we collected all the verbs under the lexical domains of verb.contact and verbmunication. We manually filtered all these sets to produce three resulting verb sets of: aggression verbs VViolence (361 words), friendliness verbs VAmicable (70 words) and sexual interaction verbs VIntimate (70 words).
Having these three sets, the tool is able to determine which nodes in the tree are interaction verbs. Whenever the tool detects an interaction verb, it marks the noun immediately preceding it and following it as potential interaction actors, matches the pair of nouns to the previous sets of characters, and counts each matched pair as being part of an interaction. 2 Because an interaction is of three types, the tool gathers the previously extracted interactions in the set I = IAggression?IFriendly?ISexual, where IAggression is the set of aggressive interactions, IFriendly is the set of friendly interactions and ISexual is the set of sexual interactions. With those sets of interaction types, the tools computes five additional metrics from table 1:
4.step 3.5. Character of attitude
To identify positive and negative emotions in the dream report, the tool incorporates the Emolex emotion dictionary , a widely used lexicon of English words associated with the eight basic emotions of Plutchik’s model : anger, fear, anticipation, trust, surprise, sadness, joy and disgust. The Hall–Van de Castle guidelines define that expressions of joy is indicated by the presence of positive emotions, whereas expressions of anger, sadness and apprehension are indicated by markers of negative emotions. The tool therefore compiles a list of emotion words WIdeas that is composed by positive words WSelf-confident (containing all the words in the dream report that belong to the set of the Emolex category of joy) and by negative words WNegative (containing all the dream report’s words belonging to the Emolex categories of anger, sadness or fear). Continue reading “To determine public relationships, we first-built a database off verbs stating violence, friendliness and intimate associations”