This project constitutes the Diploma Thesis I submited for my B.Sc. in Electrical & Computer Engineering, at Aristotle University of Thessaloniki, Greece.
Abstract
Sentiment Analysis is a very popular task of Natural Language processing with many applications. It actually involves the automated classification of text in sentiment categories like "positive", "negative". In this thesis I start by going through the broad field of Natural Language Processing in first chapter. Second chapter explains thoroughly the task of Sentiment Analysis with emphasis on Neural Networks approach. Having established a decent theoretical basis on the first two chapters, two different experiments are presented next. The first one is about monitoring public opinion in Twitter by analyzing the sentiment of tweets. A novel mechanism that makes use of state-of-the-art neural architectures is developed to perform this task. Data from US presidential elections 2016/2020 are used. The results were enlightening. Second experiment aimed to improve the accuracy of sentiment analysis in figurative texts. To that end, we transfer neural knowledge from teacher to student model in a multi-task training setting. We managed to get a well trained student model that produced state-of-the-art results on a relevant dataset.
File description
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SA_Diploma_Thesis_HMMY__English.pdf : The final english version of my thesis.
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SA_Diploma_Thesis_HMMY__Greek.pdf : The final greek version of my thesis.
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Thesis_Final_Presentation.pptx : The final ppt file of my thesis.
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Sentiment_Analysis.pptx : A ppt file for the 2nd chapter regarding the field of Sentiment Analysis.
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NLP.pptx : A ppt file for the 1st chapter regarding the broad field of Natural Language Processing.
Links to the experiments repositories