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