臺灣博碩士論文加值系統

本研究目的在透過Instagram遊客自主生成內容，進行安平港國家歷史風景區之觀光吸引力元素探討，而在過去多為使用調查法方式進行，罕有透過影像分析方法進行，因此本研究期望透過影像分析，建立一套觀光吸引力元素探究方法。根據過去文獻指出，遊客偏好之觀光資源，對於該種資源之影像上傳數目有顯著正相關；且近年來也陸續出現運用社群媒體配合影像分析及影像內容描述，探討觀光吸引力之研究。因此本研究將藉由使用者生成內容，從中了解遊客前往旅遊目的地進行遊憩活動時，所吸引他們的觀光資源為何。本研究透過Imstagram API，於Instagram中下載所有被標記”安平”標籤之影像，並且連同其影像時間、hashtag等資訊一併下載，經由篩選掉不符合安平地區內或模糊不清之影像，共獲得31,130份可以進行分析之照片，並且透過研究者判斷分析影像（Researcher Judgment）及Google Cloud Vision API人工智能（以下用Cloud Vision稱之）影像分析兩種不同途徑之方法來辨識照片中之主要元素。最終確立了在125種子集合中共有12項子集屬於具吸引力之元素，分別為：人像攝影、建築與其附屬設施、海洋／海岸、甜點、天空、樹木、古蹟、小吃、太陽、船、餐廳、飲料。由研究結果可知遊客在進行拍攝時，無論該吸引力元素是否有人與其合影，皆不影響遊客對該吸引力元素之偏好；在Researcher Judgment、Cloud Vision之比對分析中，可以得知若要使用Cloud Vision進行吸引力元素分析，雖然分析速度快速且能夠節省大量人力成本，但會受限於其辨識力相對低，因此較適合用於觀光旅遊地之集合類別判斷；然而若是使用Researcher Judgment則會耗費時間長、人力成本高，然而判斷力會遠勝於Cloud Vision，因此本研究建議若要提升Cloud Vision之判斷能力，可以先透過一次人為判斷建立該地區之分析模型，藉此提升Cloud Vision判斷力。

The purpose of this study is to explore the attraction elements of the National Anping Harbor Historic Park through tourist generated content by Instagram users. In the past, the analysis of attraction elements was often carried out by survey rather than the image, and therefore this study will establish a set of exploration attraction elements through image analysis. According to the past literature, the tourist resources preferred by tourists had a significant positive correlation with the quantities of images uploaded by tourists. In recent years, social media has been used in conjunction with image analysis and video content description to explore the attraction of tourism. Therefore, this study used the user-generated content to learn about the tourism resources that attracted visitors when they travel to destinations for recreation. Through the Instagram API, the study made use of downloaded all the images with the labeled "Anping" tags on Instagram and with their image time, theme tags etc. at the same time by filtering out the images irrelevant to the Anping area or ambiguous ones. A total of 31,130 photos that could be analyzed were obtained, and the main elements in them were identified by both the researcher judgment and the image analysis by Google Cloud Vision API artificial intelligence (hereinafter referred to as Cloud Vision). In the end, the 12 subsets of 125 seed collections were identified as attractive elements: portrait photography, architecture and its ancillary facilities, ocean/coast, dessert, sky, trees, monuments, snacks, sun, boat, restaurant, and drink. From the research results, a tourist took a picture of whether the attraction element was photographed with people or not, not affecting the preference of the attraction element for him. At the discretion of the researchers, Cloud Vision comparison analysis shows that if you use Cloud Vision for attractive element analysis, though it is fast and can save a lot of labor costs, it will be limited by its relatively low recognition. It is more suitable for the collection category judgment of sightseeing spots. However, if you use the researcher judgment, it will take a long time and the labor cost is high, but the accuracy will be far better than Cloud Vision’s. Therefore, this study suggests that if you want to improve the judgment ability of Cloud Vision, you can first establish an analysis model of the region through a human judgment, thereby improving the judgment of Cloud Vision.

摘要……i
Abstract. ……ii
誌謝……iv
目錄……vi
表目錄……viii
圖目錄……ix
第一章、緒論……1
1.1 研究背景與動機……1
1.2 研究目的……2
1.3 研究範圍……2
1.4 名詞解釋.…….2
1.5 研究限制……2
1.6 研究流程……3
第二章、文獻探討……5
2.1 觀光吸引力……5
2.1.1 觀光吸引力之定義……5
2.1.2 觀光吸引力之分類……5
2.1.3 觀光吸引力之研究方法……8
2.1.4 小結……10
2.2 遊客自主攝影法……11
2.2.1 遊客自主攝影法之定義……11
2.2.2 遊客自主攝影法之特性……11
2.2.3 遊客自主攝影法之相關研究……12
2.2.4 遊客自主攝影法與吸引力之關係……13
2.2.5 小結……14
2.3 影像分析……15
2.3.1 影像分析之特性……15
2.3.2 影像分析與遊客自主攝影法之關係……15
2.3.3 影像分析之相關研究……16
2.3.4 Google Cloud Vision……18
2.3 小結……18 第三章、研究方法……19
3.1 研究架構……19
3.2 研究設計……19
3.2.1 研究步驟……19
3.2.2 研究對象……20
3.2.3 研究資料蒐集方法……20
3.2.4 研究資料分析方法……22
第四章、研究結果分析與討論……25
4.1 描述性樣本分析……25
4.1.1 Researcher Judgment 所有集群與子集描述性統計……26
4.1.2 Researcher Judgment 最切合之集群與子集描述性統計……38
4.1.3 Cloud Vision 所有集群與子集描述性統計……48
4.1.4 Cloud Vision 最切合之集群與子集描述性統計……59
4.2 Cloud Vision 與 Researcher Judgment 比對分析……69
4.2.1 比較 Researcher Judgment、Cloud Vision 所有子集與集群……69
4.2.2 比較 Cloud Vision 所判斷最切合的一項與 Researcher Judgment 的三項子集和 集群……73
4.2.3 比較 Cloud Vision 所判斷最切合的一項與 Researcher Judgment 所判斷最切合 的一項子集和集群……76
第五章 結論與建議……79
5.1 研究結論……79
5.2 實務意涵與建議……80
5.3 研究限制與未來研究建議……81
參考文獻……82
Extended Abstract…… 87

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