Purpose
The development of social media has led to large numbers of internet users now producing massive amounts of user-generated content (UGC). UGC, which shows users’ opinions about events directly, is valuable for monitoring public opinion. Current researches have focused on analysing topic evolutions in UGC. However, few researches pay attention to emotion evolutions of sub-topics about popular events. Important details about users’ opinions might be missed, as users’ emotions are ignored. This paper aims to extract sub-topics about a popular event from UGC and investigate the emotion evolutions of each sub-topic.
Design/methodology/approach
This paper first collects UGC about a popular event as experimental data and conducts subjectivity classification on the data to get subjective corpus. Second, the subjective corpus is classified into different emotion categories using supervised emotion classification. Meanwhile, a topic model is used to extract sub-topics about the event from the subjective corpora. Finally, the authors use the results of emotion classification and sub-topic extraction to analyze emotion evolutions over time.
Findings
Experimental results show that specific primary emotions exist in each sub-topic and undergo evolutions differently. Moreover, the authors find that performance of emotion classifier is optimal with term frequency and relevance frequency as the feature-weighting method.
Originality/value
To the best of the authors’ knowledge, this is the first research to mine emotion evolutions of sub-topics about an event with UGC. It mines users’ opinions about sub-topics of event, which may offer more details that are useful for analysing users’ emotions in preparation for decision-making.
An Efficient Feature Weighting Method for Support Vector Regression
