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Emotion recognition has become a major problem in computer vision in recentyears that made a lot of effort by researchers to overcome the difficulties inthis task. In the field of affective computing, emotion recognition has a widerange of applications, such as healthcare, robotics, human-computerinteraction. Due to its practical importance for other tasks, many techniquesand approaches have been investigated for different problems and various datasources. Nevertheless, comprehensive fusion of the audio-visual and languagemodalities to get the benefits from them is still a problem to solve. In thispaper, we present and discuss our classification methodology for MuSe-TopicSub-challenge, as well as the data and results. For the topic classification,we ensemble two language models which are ALBERT and RoBERTa to predict 10classes of topics. Moreover, for the classification of valence and arousal, SVMand Random forests are employed in conjunction with feature selection toenhance the performance.

Variants of BERT, Random Forests and SVM approach for Multimodal Emotion-Target Sub-challenge