Mining Storylines within Entrepreneurial Industry Based on Convolutional Neural Networks

• Main Authors: Yu-Hsien Lee, 李昱賢
• Other Authors: 李瑞庭
• Format: Others
• Language: en_US
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/bf4a8d

碩士 === 國立臺灣大學 === 資訊管理學研究所 === 107 === Nowadays, the amount of articles on the Internet has grown very rapidly. Mining storylines from such a large amount of articles may give us a picture what is going on or the whole picture of related events. Therefore, we propose a framework to build storylines from news articles of entrepreneurial industry. The proposed framework contains five steps. First, we preprocess the articles to remove stop words and low-frequency words. Second, based on Convolutional Neural Network (CNN), we build a model, called News-CNN, to extract semantic features from news articles and transform each news article into a feature vector. Third, we convert the name entities in each news article into a feature vector. Fourth, we use the feature vectors derived by News-CNN and the feature vectors derived from name entities to compute the similarity between news articles and then group similar news articles into a document-based storyline. Also, we set some rules to remove redundant storylines. Finally, based on NMF, we propose a method to summarize each document-based storyline into a sentence-based storyline. The experiment results show our proposed method can generate better storylines than SteinerTree. Also, it can provide valuable business insights for users to implement their business strategies in the related industry.