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Multilingual Sentiment Analysis in Text and Video Data


Description

Sentiment analysis is the process of computationally (using deep learning) identifying and categorizing opinions expressed by a writer or speaker, especially in order to determine whether the writer's attitude towards a particular topic, product, etc. is positive or negative. In recent years, a large number of multilingual representation learning in text has emerged, whereby a common representation of the used words from several languages is learned and creates an aligned vector space of word embeddings. This is particularly interesting if you have opinions (e.g. reviews) on a topic (e.g. product) in different languages and can learn more variance across several languages through the aligned vectors.

Task In this work, the student(s) will search and structure the latest research in the field of multilingual sentiment analysis that has utilised deep learning, either for text or video data. The aim is to identify existing literature and methods in the field of multilingual sentiment analysis in text and video. 
Utilises None, literature review only
Requirements Preliminary knowledge in neural networks and representation learning (word embeddings)
Languages German or English
Supervisor Lukas Stappen, M. Sc. (lukas.stappen@informatik.uni-augsburg.de)