Abstract:

The social media, as a major platform for communication and information exchange, provides a rich repository of people's opinions and sentiments about a vast spectrum of topics. Such knowledge is embedded in multiple facets, such as comments, tags, as well as shared image and video content. The analysis of such information either in the area of opinion mining, affective computing or sentiment analysis is playing an important role in computational social sciences, which aims to understand and predict human decision making and enables applications such as brand monitoring, market prediction, and even voting forecasts. However, the massive growth of photo and video sharing is increasingly eclipsing text on the leading visual social platforms. So visual communication is complementing and even supplanting the written word, and this visual language is a powerful way for people to express themselves. This talk will exploit the most recent image understanding models based on neural networks to process the vast amount of data generated by social users. These improvements will enable to know more accurately the social user's demands and cultural-driven interests, eventually reaching some degree of personality trait description, depression detection and early suicidal tendencies estimation.