臺灣博碩士論文加值系統

近年來社群網路平台普及，消費者不再是單向被動接受資訊。在社群平台上使用者可以自行創造粉絲圈/追隨者，在平台上與粉絲相互交換訊息資料。Twitter 訊息傳播快速，影響力也很大，推文在沒有設定隱私的功能時，所有的推文是公開可以獲取。另外使用者發文時主要在傳播消息、知識、和有興趣的事，Twitter大量的海內外文本數據集，提供很好的數據集資料。這些大量文字內容具有研究與商業價值，經由文字探勘，可以挖掘出其中潛在內涵，提供另一種市場調查方式，給予企業行銷時多一份參考依據，企業有更多的市場資料參考來源更能幫助其決策方案的制定。

藉由語意分析在推文中找出商品特徵值，再結合文字情感分析，賦予情感的色彩，分析使用者對商品特徵值的好感度，之後再轉為李克氏五點量表，藉此能夠了解使用者對該商品特徵值的滿意度，進而發掘客戶的需求，提供另一個市場問卷調查方式，在進行市場開發時可以多一個決策考量依據，進而調整行銷策略方向。本研究中以巧克力商品做為實驗項目，並探討在不同區域市場中商品特徵值與其喜好程度的差異性，可以做為市場區隔與行銷的參考。

Social networks, such as Facebook, Twitter, and Instagram, are most popular in a decade. Consumers are no longer received product information in single way. For example, users can gather their followers or fans and swap products information on social media. Twitter disseminates information quickly, as well as influence effect. When there is no privacy setting for tweets, all tweets are publicly available. The posted tweets are shared information, knowledge, and interested things. The global information of Twitter datasets provided big data for data mining. Those big data also have commercial value. We can use those data not only to give a new method of market survey, but also to provide company more information to make selling plan and market promotion.

This study adopted NLP (Natural-Language-Processing) and TF-IDF (Term Frequency-Inverse Document Frequency) to find out potential characters of specific product through twitter. Sentiment analysis is used to detect the emotions of sentence. Also, we use Likert Five-Point Scale to detect the satisfaction of product characters. Finally, comparing the relationship between characteristics and emotions, the users’ satisfaction with a product in various characteristics can be found. It is expected that the results of this research can be used as a reference for product market research. In this study, chocolate is used as experimental item, and the differences in product characteristic values and their preferences in different regional markets are discussed, which can be used as a reference for market segmentation and marketing.

摘要 i
ABSTRACT ii
誌謝 iii
目次 iv
表目次 v
圖目次 vi
第一章　緒論 1
第一節　研究背景與動機 1
第二節　研究目的 2
第三節　研究的貢獻 3
第四節　章節概要 3
第二章　文獻探討 4
第一節　情感分析Sentiment Analysis 4
第二節　Twitter 與其價值 5
第三節 TF-IDF 7
第三章　研究方法 9
第一節　研究架構 9
第二節　研究步驟 11
3-2-1資料預處理 11
3-2-2資料分析 13
第三節 結果探討 18
第四章 實驗結果與分析 19
第一節 實驗資料集 19
第二節 研究分析 20
4-2-1 TF-IDF分析 20
第三節 商品特徵值與使用者情緒分析 23
第五章 結論 35
第一節 結論 35
第二節 貢獻 35
第三節 研究限制與未來研究方向 36
參考文獻 37

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