I act , therefore I judge : Network sentiment dynamics based on user activity change Supplemental Material
نویسندگان
چکیده
We annotate individual posts following the approach in [1], [2]. Tweets are grouped by topic based on included topic hashtags. For example, tweets relating to the topic of president Barack Obama contain the hashtag #obama within them. The topic-related tweets often contain other hashtags which we assign a preliminary sentiment probability (positive and negative) using the Multinomial Naive Bayes classifier method introduced in [1]. This classifier is trained on a document set consisting of positive / negative tweets discovered using manually collated sets of strongly positive and negative topicbased hashtags (for example “#voteForObama” or “#impeachObama”). The results from the classifier give the sentiment probabilities on a per-tweet basis (in other words Pr(t = s), the probability that tweet t has sentiment s), but may be extended to the contained hashtags in order to obtain the probability of any hashtag h, having sentiment s using the following equation:
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