Analyzing Social Network Data Using Deep Neural Networks: A Case Study Using Twitter Posts

• Main Authors: Yang, Tzu-Hsuan, 楊子萲
• Other Authors: Liao, Wen-Hung
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/wtqyh3

碩士 === 國立政治大學 === 資訊科學系 === 107 === Interaction on various social networking platforms has become an important part of our daily life. Apart from text messages, image is also a popular media format utilized for online communication. Text or image alone, however, cannot fully convey the ideas that users wish to express. In the thesis, we employ computer vision and word embedding techniques to analyze the relationship between image content and text messages and explore the rich information entangled. The limitation on the total number of characters compels Twitter users to compose their messages more succinctly, suggesting a stronger association between text and image. In this study, we collected all tweets which include keywords related to Taiwan during 2017. After data cleaning, we apply machine learning techniques to classify tweets into to ‘travel’ and ‘non-travel’ types. This is achieved by employing deep neural networks to process and integrate text and image information. Within each class, we use hierarchical clustering to further partition the data into different clusters and investigate their characteristics. Through this research, we expect to identify the relationship between text and images in a tweet and gain more understanding of the properties of tweets on social networking platforms. The proposed framework and corresponding analytical results should also prove useful for qualitative research.