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    Multimodal emotion recognition using deep learning techniques : a novel system for real-world emotion recognition
    Καρδάκης, Σπυρίδων; Kardakis, Spyridon
    Τμήμα Μηχανικών Η/Υ και Πληροφορικής (ΜΔΕ)
    In recent years the field of Sentiment Analysis, and by extension Emotion Recognition, has been met with increased interest due to the rise of social media. Making machines capable of automatically recognizing emotions will be a vital task, as well as a milestone, in Human-Computer Interaction in the coming years. Most of the early works focused on data of a single modality, such as a product review or a facial expression. More recent efforts have focused on multimodal fusion since human emotion is expressed through multiple modalities, specifically text, facial expressions and voice. As one can imagine, since recognition of the emotional state of a person can be a very challenging task even for humans, it is even more complex for automated methods, and as a result effective Emotion Recognition is required. In this thesis we study and present the field of Emotion Recognition in-depth. Initially, background topics, related works, methods and approaches are presented for each of the modalities, namely Textual Emotion Recognition and Facial Emotion Recognition. The use of deep learning techniques in the field skyrocketed the performance of classification methods and are the main direction that is currently pursued by researchers, introducing a variety of challenges. In terms of the methodology proposed in this work, a wide variety of architectures and approaches are implemented, leading to different models for the text and the image aspect of the system. Then, the field of Multimodal Emotion Recognition is presented, including its theory and literature. The main goal is to realize an end-to-end deep learning pipeline, in order to address the problem of understanding human emotions and improve the accuracy over the traditional standalone models. An important aspect of the field that is explored is the fusion of modalities which is often performed through a fusion at the feature and/or decision-level. The task at hand is supervised classification. Two additional topics showcased in this work are attention mechanisms and a systematic review of the available datasets in the Emotion Recognition domain. In order to explore the performance of the proposed models in recognizing peoples’ emotions we implement them and evaluate them on a variety of real-world datasets. Thus, we come to conclusions regarding their overall emotion recognition accuracy, when compared to each other, as well as when compared to state-of-the-art approaches. Furthermore, the proposed approach is adapted to a more practical environment by implementing a novel real-world system for Multimodal Emotion Recognition. The user is given the option to enter multiple types of inputs and receives emotion predictions. Overall, we effectively illustrate the different facets of analysis that are performed in the task of Multimodal Emotion Recognition. From the experimental results it is observed that the proposed models consisting of Recurrent and Convolutional Neural Networks achieve very high performance as well as proving that they are potent and suitable tools for practical real-world emotion recognition.
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    Machine learning techniques for sentiment analysis and emotion recognition in natural language
    Καρδάκης, Σπυρίδων; Χατζηλυγερούδης, Ιωάννης; Περίκος, Ισίδωρος; Χατζηλυγερούδης, Ιωάννης; Μακρής, Χρήστος; Kardakis, Spyridon
    Τμήμα Μηχανικών Η/Υ και Πληροφορικής (ΔΕ)
    The field of Textual Sentiment Analysis has been met with increased interest by the research community in recent years due to the rise of social media and the Internet. The vast amount of opinion-heavy user-generated content that is available to us, whether that is a product/service review or an opinion on an event, shows that effective Sentiment Analysis is needed. However, automatic knowledge extraction about the opinion and emotional state of people can be a very challenging task. This thesis studies the fields of Machine Learning and Deep Learning in-depth, in order to perform Sentiment Analysis and by extension Emotion Recognition classification tasks. A novel Hidden Markov Model-based approach is proposed where a single model is trained for each class label with the help of clustering and a lexicon. Overall, the main goal is to present a variety of Machine Learning models, ranging from basic all the way to state-of-the-art approaches, and implement them in real-world datasets. Initially, the theory behind the aforementioned fields and the related literature is introduced. Then, we present the mathematical background of the proposed approaches as well as expand on their usage for text classification and its challenges. The task at hand is supervised text classification. Additionally, a survey of the available datasets in the Sentiment Analysis domain is performed and the field of Ensemble Learning is explored. Finally, we implement and evaluate the proposed models on benchmark datasets using k-fold cross-validation and come to conclusions regarding each algorithm’s ability to recognize peoples' opinions and emotions. From the experimental results it is observed that the proposed Hidden Markov Models and Deep Neural Networks with word embeddings achieve very high performance, proving that they are potent and suitable tools for Sentiment Analysis and classification tasks in general.