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Sentiment Analysis of Textual Content in Social Networks

Research Area:  Machine Learning

Abstract:

Sentiment analysis, known as opinion mining, is the computational study of peoples opinions, sentiments, attitudes, feelings, and emotions. It is considered to be one of the most active research fields in natural language processing, data mining, information retrieval, and Web mining. Sentiment analysis is not just the problem of classifying whether a piece of text expresses a positive or negative sentiment or opinion. It is indeed a much more complex problem than that. It is a generic name and involves many facets and multiple related tasks,each of which has its challenges.This thesis proposes several advanced methods to automatically analyse textual content shared on social networks and identify people opinions, emotions and feelings at a different level of analysis and in different languages.We start by proposing a sentiment analysis system, called Senti Rich, based on a set ofrich features, including the information extracted from sentiment lexicons and pre-trained word embedding models. Then, we propose an ensemble system based on Convolutional Neural Networks and XGboost regressors to solve an array of sentiment and emotion analysis tasks on Twitter. These tasks range from the typical sentiment analysis tasks, to automatically determining the intensity of an emotion (such as joy, fear, anger, etc.) and the intensity of sentiment (aka valence) of the authors from their tweets. We also propose a novel Deep Learning-based system to address the multiple emotion classification problem on Twitter.Moreover, we considered the problem of target-dependent sentiment analysis. For this purpose, we propose a Deep Learning-based system that identifies and extracts the target ofthe tweets.

Name of the Researcher:  Mohammed Jabreel

Name of the Supervisor(s):   Prof. Dr. Antonio Moreno

Year of Completion:  2020

University:  Universitat Rovira i Virgili

Thesis Link:   Home Page Url