|Title:||Emotion Recognition of Influential Users|
|Dates:||2013 – present|
|Researchers:||Lionel Martin and Pearl Pu|
|Keywords:||emotion recognition, influence detection, prediction, social media analysis, natural language processing, Geneva Emotion Wheel|
People increasingly rely on other consumers’ opinion to make online purchase decisions. Amazon alone provides access to millions of reviews, risking to cause information overload to an average user. Recent research has thus aimed at understanding and identifying reviews that are considered influential. Most of such works analyzed the structure and connectivity of social networks to identify influential users. We believe that insight about influence can be gained from analyzing the affective content of the text as well as affect intensity. We employ text mining to extract the emotionality of thousands of reviews in different domains to investigate how those influential users behave. We analyze whether texts with words and phrases indicative of a writer’s emotions, moods, and attitudes are more likely to trigger a genuine interest compared to more neutral texts.
|Sponsor:||Swiss National Science Foundation|