Using Siamese Neural Network with Attention Mechanism to analyze the support degree of news events

Abstract

Today news events affect people’s life style deeply Series of public opinions woud be released on social media such as Facebook PTT etc upon political event news being issued Policy makers can then respond to people’s tendency by emotion analysis on the assembled opinion papers This is how and what the Public Opinion System functions However there is still some limitation; the current public opinion system can provide many emtion analysis by some Natural Language Processing methods such as happiness sadness anger etc of an article but not telling sustain from opposition This research aims for providing an overall sustain level of an article on a certain news event and Text similarity by comparing the article with a simple sustain phrase The contribution of this thesis is to provide a clear sustain level meter by a sustain model for policy makers to make decisions The model introduces an improved Attention Mechanism with LSTM(Long Short-Term Memory) to a Siamese Network to solve the phrase length differential problem and also incorporating several different distance formulae to obtain a comprehensive matching on features of any two selected news articles This research analyzes news event articles collected online The articles in the dataset contain large amount of Manderin proper nouns Selection of word segmentation tools therfore plays an important roll on the sufficiency of this research All the 4 112 articles collected are manually labelled on their positive/negative tendency together with more than 70 thousand news articles being used to train the term library After trained 300 recent news event articles are tested to validate of the model proposed in the research

Date of Award	2020
Original language	English
Supervisor	Tzone-I Wang (Supervisor)