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| Title: | 應用文字探勘於實用推薦文辨別之研究 -以愛評網美食評論為例
A study of identifying useful review comments for food recommendation with text mining approach |
| Authors: | 黃怡蓁
Huang, Yi-Chen |
| Contributors: | 楊建民
洪為璽
Yang, Chien-Min
Hung, Wei-Hsi
黃怡蓁
Huang, Yi-Chen |
| Keywords: | 中文斷詞
資料探勘
網路爬蟲
群集分析
用戶生成內容
Chinese word segmentation
Data mining
Web crawling
Cluster analysis
User-generated content |
| Date: | 2019 |
| Issue Date: | 2019-08-07 16:07:15 (UTC+8) |
| Abstract: | 網路是世界上最有用的資訊查詢工具，隨著電子商務網站大幅度興起，消費者常於進行購買前閱讀網路相關產品與店家推薦文，並於消費後上網進行經驗回饋分享，在這樣的相互作用之下，網路上的相關產品用戶生成推薦文越垂手可得，資訊與雜訊的分辨逐漸重要。
相關產品推薦文有效影響個人的購買行為與企業發展產品決策，本研究提出一監督式學習的迭代模型，探討非結構性之推薦文對於潛在消費者是否實用，以達到辨別評論為實用或非實用文的目的。
本研究採用愛評網(ipeen)之美食評論發表時間於2008年1月至2018年12月內，共1,219篇實用文與478篇非實用文作為檢測實驗資料，透過使用者與評論層級之雙層過濾，以主題性分析建立特徵詞庫，再以Support Vector Machine、Naive Bayes classifier、Random Forests進行分類，藉由分析結果建立預測模型，並定期擴增詞庫以自適應地學習新實用文迭代模式，因應時代用詞變化。
研究結果顯示最佳模型之準確度為80.20%，精確度為0.924，召回率為0.6886，F-score則可達 0.7891，後續研究可進一步拓展跨領域評論辨別。
E-commerce is growing at an unprecedented rate all over the globe and the internet is becoming an increasingly important query tool in the world. Consumers often read the related online review to get more comments about the products before purchasing and share their opinion and experiences on the products they`ve purchased.
Under the interaction, the more user-generated content on the internet, the more important it is to distinguish between information and noise.Reviews of the related product effectively influence the purchase decision of individuals and organizations and predict product trends.
In this study, we present an iterative and supervised framework, exploring the differences between the participial construction of unstructured recommended reviews for potential consumers, in order to achieve the purpose of distinguishing comments as useful or non-useful.
The reviews which we used python to do web crawler to collect from iPeen was published from January 2008 to December 2018. There are 1,219 useful reviews and 478 non- useful reviews were used as our dataset, which were filtered by double layer of user and comment level. We utilized topic model to find the implicit features in the dataset and then it were be used by Support Vector Machine, Naive Bayes classifier, Random Forests for classification
At last, we use the analyzing results of the classification to establish a prediction model. The dataset will periodically update to amplify the keyword thesaurus and adaptively learn the new implicit features. The accuracy of the model is 80.20%, the precision is 0.924, and the recall rate is 0.6886. F-score can reach 0.7891. |
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| Description: | 碩士
國立政治大學
資訊管理學系
106356027 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0106356027 |
| Data Type: | thesis |
| DOI: | 10.6814/NCCU201900451 |
| Appears in Collections: | [資訊管理學系] 學位論文
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