在這社會網路蓬勃發展之中、網際網路頻寬與速度相繼提昇的資訊年代，結合網路科技所衍生的Flickr網路相簿因應而生。Flickr提供許多API程式讓使用者或有興趣研究的專家學者能透過Flickr所收集及其所探討的議題，來觀察社會網路的變化情形。 社會網路主要是由節點以及節點間彼此相連結所形成，常見的網路模型大致可分為One-mode與Two-mode兩種網路結構，而本文則採用內部同時有兩種類節點、由兩個城市與Tags共同組合而成的Two-mode網路為基礎架構，期望藉此來闡述一個Tags系統分析法，利用Flickr使用者收集、標註之Flickr標記來與世界城市的概念輪廓相連結，透過提取城市語義分配給Flickr上照片的Tags，以及解決Part-Of-Speech （POS）、詞幹還原及雜訊處理…等問題，來達成依據排名結果分析出城市概念輪廓的最終目的。 除此之外，本文還運用了Flickr tag資料來彙整出41個城市的前100名tag，再篩選出前10名的tag，將其與相關的城市歸類一起比較。本文亦使用字詞共現指標（Tag co-occurrence）來計算與該城市的關聯性，再利用此法則來歸納出這兩個城市字詞共同出現的機會，以便於了解城市與城市之間的關連字詞組合。最後，本研究亦透過Flickr網站本身Popular Tags經由分析及匯出標籤雲的結果來與本文之實驗結果相對照，本實驗85%的吻合度驗證了可靠性。 / The Flickr Web Albums was born in the information age of social network growth, internet bandwidth and speed improvement. Users and researchers can observe the changing of social network from topics collected and studied by Flickr using API programs provided by Flickr. The main structure of social network can be distinguished one-mode and two-mode network which is composed by nodes, generally. An approach for world cities concept profiling analysis is developed in this study by conbineing two types of nodes and two cities with tag which is the two-mode network using extracting city semantics for tags assigned to photos on Flickr, solving Part-of-Speech（POS）, Stemming reduction and noise handing by collecting Flickr's tags from Flickr users. The top 100 tags were slected for 41 cities and then top 10 tags for each city were also extracted. The Tag co-occurrence was also applied to analysis the relationship of cities. Then the connection between the cities can be understood by the result of tag co-occurrence opportunities. The 85% accurancy was demonstrated by comparing the result of analysised and exported Popular Tags from Flickr Website service and the result of experiments in this study.

本研究試圖從心理學出發，探究描述情緒狀態的分類方法為何， 為了進行情緒與語意的連結，我們試圖將影像當作情緒狀態的刺激 來源，針對Flickr網路社群所共建共享的內容進行抽樣與觀察，使 用心理學研究中基礎的情緒字詞與詞性變化，提取12,000張帶有字 詞標籤的照片，進行標籤字詞與情緒分類字詞共現的計算、關聯規則 計算。同時，透過語意差異量表，提出了新的偏向與強度的座標分類 方法。 透過頻率門檻的過濾、詞性加註與詞幹合併字詞的方法，從 65983個不重複的文字標籤中，最後得到272個帶有情緒偏向的事物 概念字詞，以及正負偏向的情緒關聯規則。為了透過影像驗證這些字 詞是否與影像內容帶給人們的情緒狀態有關聯，我們透過三種查詢 管道：Flickr單詞查詢、google image單詞查詢、以及我們透過照片 標籤綜合指標：情緒字詞比例、社群過濾參數來選定最後要比較的 42張照片。透過語意差異量表，測量三組照片在136位使用者的答案 中，是否能吻合先前提出的強度-偏向模型。 實驗結果發現，我們的方法和google image回傳的結果類似， 使用者問卷調查結果支持我們的方法對於正負偏向的判定，且比 google有更佳的強弱分離程度。 / This study attempts to proceed from psychology to explore the emotional state of the classification method described why, in order to be emotional and semantic links, images as we try to stimulate the emotional state of the source, the Internet community for sharing Flickr content sampling and observation, using basic psychological research in terms of mood changes with the parts of speech, with word labels extracted 12,000 photos, label and classification of words and word co-occurrence of emotional computing, computing association rules. At the same time, through the semantic differential scale, tend to put forward a new classification of the coordinates and intensity. Through the frequency threshold filter, filling part of speech combined with the terms of the method stems from the 65,983 non-duplicate text labels, the last 272 to get things with the concept of emotional bias term, and positive and negative emotions tend to association rules. In order to verify these words through images is to bring people's emotional state associated with our pipeline through the three sources: Flickr , google image , and photos through our index labels: the proportion of emotional words, the community filtering parameters to select the final 42 photos to compare. Through the semantic differential scale, measuring three photos in 136 users of answers, whether the agreement made earlier strength - bias model. Experimental results showed that our methods and google image similar to the results returned, the user survey results support our approach to determine the positive and negative bias, and the strength of better than google degree of separation.