隨著社群媒體的普及，群眾產製的內容（User-generated content, UGC）時常成為新聞記者取材的對象，但現今隨著社群媒體爆發的資料量，記者不易從資料中看到事件的全貌，僅將社群媒體當作一種消息來源，因此報導的內容經常抄襲網友的意見或是落入片面討論的窠臼，無法駕馭社群媒體帶來的豐富資料。考慮改善這樣的現象，本研究透過將新聞取材的過程分為探索事件、收集素材以及回溯情境三個動作來協助記者探索新聞主題。以推特（Twitter）的資料為例，以網路為系統平台，開發一個輔助記者探索社群媒體上的事件、挖掘新聞主題的資訊系統，利用網絡分析以及自然語言處理的技術，結合視覺化的介面將事件資料集用故事元素的方式呈現，四種故事元素模型提供不同的觀察資料集的角度，並利用調整四種故事元素的權重，還原推文文本的語境，找出使用者想看的內容。我們設計了兩階段的任務式實驗以及評估問卷來證明系統的可用性，透過實驗結果驗證了本研究在以社群媒體輔助記者探索新聞主題的系統之價值，能讓對事件不同熟悉程度的傳播記者在此平台上探索新聞主題，並寫下深度報導的編採線索或是一篇新聞報導，透過本系統的輔助，讓使用者在探索及追蹤一起事件時，變得較為快速。 / With the popularity of social media, news reporters usually draw the news materials from mass user-generated content. However, with the outbreak of social media data, the reporter is not easy to see from the data in the whole picture of event. They only use the social media as a news source, so the reported content often copied the views of users, or fall into the stereotype of a one-sided discussion. The reporters can not control the wealth of information brought from social media. Consider improving this phenomenon, our study use Twitter data for example, develop an information system to assist reporters to explore the events on social media, and mine the news topics. We use network analysis and natural language processing as our technique, and show the story elements with the visualization interface. We apply four different story elements model, support the different way to explore data, and let user can adjust the weights from different model to retrospect to the context of tweets, help user find the news topics. We have designed a two-stage task experiment and assessment questionnaire to prove the availability of the system through experimental results. We can allow the reporters who are varying degrees of familiarity of the event to explore news topics from our system. We make the reporter to explore and track some events faster.

近年來科技、網路以及儲存媒介的發達，產生的資料量呈現爆炸性的成長，也宣告了巨量資料時代的來臨。擁有巨量資料代表了不必再依靠傳統抽樣的方式來蒐集資料，分析數據也不再有資料收集不足以致於無法代表母題的限制。突破傳統的限制後，巨量資料的精隨在於如何從中找出有價值的資訊。 以擁有大量輿論和人際互動資訊的社群網站為例，就有相關學者研究其情緒與股價具有正相關性，本研究也試著利用同樣具有巨量資料特性的網路新聞，抓取中央新聞社2013年7月至2014年5月之經濟類新聞共計30,879篇，結合新聞主題偵測與追蹤技術及情感分析，利用新聞事件相似的概念，透過連結匯聚成網絡並且分析新聞的情緒和股價指數的關係。 研究結果顯示，新聞事件間可以連結成一特定新聞主題，且能在龐大的網絡中找出不同的新聞主題，並透過新聞主題之連結產生新聞主題脈絡。對此提供一種新的方式來迅速了解巨量新聞內容，也能有效的回溯新聞主題及新聞事件。 在新聞情緒和股價指數方面，研究發現新聞情緒影響了股價指數之波動，其相關係數達到0.733562；且藉由情緒與心理線及買賣意願指標之比較，顯示新聞的情緒具有一定的程度能夠成為股價判斷之參考依據。 / In recent years, the technology, network, and storage media developed, the amount of generated data with the explosive growth, and also declared the new era of big data. Having big data let us no longer rely on the traditional sample ways to collect data, and no longer have the issue that could not represent the population which caused by the inadequate data collection. Once we break the limitations, the main spirit of big data is how to find out the valuable information in big data. For example, the social network sites (SNS) have a lot of public opinions and interpersonal information, and scholars have founded that the emotions in SNS have a positive correlation with stock prices. Therefore, the thesis tried to focus on the news which have the same characteristic of big data, using the web crawl to catch total of 30,879 economics news articles form the Central News Agency, furthermore, took the “Topic Detection & Tracking” and “Sentiment Analysis” technology on these articles. Finally, based on the concept of the similarity between news articles, through the links converging networks and analyze the relevant between news sentiment and stock prices. The results shows that news events can be linked to specific news topics, identify different news topics in a large network, and form the news topic context by linked news topics together. The thesis provides a new way to quickly understand the huge amount of news, and backtracking news topics and news event with effective. In the aspect of news sentiment and stock prices, the results shows that the news sentiments impact the fluctuations of stock prices, and the correlation coefficient is 0.733562. By comparing the emotion with psychological lines & trading willingness indicators, the emotion is better than the two indicators in the stock prices determination.