Master Tesia
Title:
Automatic Stance Detection on Political Discourse in Twitter
Author:
Elena Zotova
Abstract:
The majority of opinion mining tasks in natural language processing (NLP) have been
focused on sentiment analysis of texts about products and services while there is
comparatively less research on automatic detection of political opinion. Almost all
previous research work has been done for English, while this thesis is focused on the
automatic detection of stance (whether he or she is favorable or not towards important
political topic) from Twitter posts in Catalan, Spanish and English. The main objective
of this work is to build and compare automatic stance detection systems using supervised
both classic machine and deep learning techniques. We also study the influence of text
normalization and perform experiments with different methods for word representations
such as TF-IDF measures for unigrams, word embeddings, tweet embeddings, and
contextual character-based embeddings. We obtain state-of-the-art results in the stance
detection task on the IberEval 2018 dataset. Our research shows that text normalization
and feature selection is important for the systems with unigram features, and does not
affect the performance when working with word vector representations. Classic methods
such as unigrams and SVM classifer still outperform deep learning techniques, but seem
to be prone to overfitting. The classifiers trained using word vector representations and
the neural network models encoded with contextual character-based vectors show greater
robustness.
File:
Tutor:
Rodrigo Agerri and German Rigau
Urtea:
2019
hitz_gakoak:
Text Categorization, Stance Detection, Opinion Mining, Supervised Machine Learning
Assigned: