AVEC 2019 workshop and challenge: state-of-mind, detecting depression with AI, and cross-cultural affect recognition

The Audio/Visual Emotion Challenge and Workshop (AVEC 2019) "State-of-Mind, Detecting Depression with AI, and Cross-cultural Affect Recognition" is the ninth competition event aimed at the comparison of multimedia processing and machine learning methods for automatic audiovisual health and emotion analysis, with all participants competing strictly under the same conditions. The goal of the Challenge is to provide a common benchmark test set for multimodal information processing and to bring together the health and emotion recognition communities, as well as the audiovisual processing communities, to compare the relative merits of various approaches to health and emotion recognition from real-life data. This paper presents the major novelties introduced this year, the challenge guidelines, the data used, and the performance of the baseline systems on the three proposed tasks: state-of-mind recognition, depression assessment with AI, and cross-cultural affect sensing, respectively

Evaluation of Political Sentiment on Twitter

With the increasing level of access to online political discourses, made possibleby the social media networks, a systematic analysis of political speech becomesmore critical. The available data could be useful in analyzing important controversies and estimating the general public's opinion in important political events. A deeper insight enables us to track the public's opinions to detect the shift ofsentiment and discuss on potential reasons why the shift has happened.Twitter has thus far, shown to be the most effective social network for politicalanalysis. Tweets are limited to 140 characters which lead the users to make succinct statements. The simplicity of text along with the relatively high level ofaccessibility has made Twitter, a popular target for social analysts.In this study, we have provided a mechanism for online and automatic collectionof Twitter data surrounding different people and events. The data is maintainedin an organized time based fashion which makes it easy for future references. Wehave applied sentiment analysis to estimate political popularity and track the sentiment over time. Sentiment scores have been defined for multiple politicians inorder to allow for a comparison of popularity