基於機器學習技術之誘導式評論過濾機制：以餐廳評論為例

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姓名

蔡宗祐(Tsung-Yu Tsai) 查詢紙本館藏

畢業系所

資訊管理學系在職專班

論文名稱

基於機器學習技術之誘導式評論過濾機制：以餐廳評論為例
(Inductive Review Filtering Mechanism Based on Machine Learning Techniques: A Case Study of Restaurant Reviews)

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至系統瀏覽論文 (2028-7-20以後開放)

摘要(中)

隨著Google餐廳評論的崛起，消費者不再需要親身嘗試就能避免對飲食體驗的失望，同時，這也為行業競爭助力，進一步促使市場自然淘汰不合格的經營者。然而，隨著時間推移，這種評論機制的本意卻似乎已被扭曲，許多業者透過行銷活動誘導消費者提供非客觀的評論，使得人們對Google評論的信賴度日漸下滑。因此，本研究旨在探討如何利用機器學習技術構建一種能過濾誘導性評論的機制，以提升社群經營策略的效果。
本研究以新竹地區的Google餐廳評論為研究對象，利用四種特徵工程方法，分別是Word2Vec模型、FastText模型、TF-IDF，以及三種綜合方法，從大量的評論中篩選出具有誘導性的特徵詞彙集。繼而，我們使用三種不同的文本向量化方法，包括TF-IDF、BoW和BERT模型，最終以SVM模型作為分類器，建立了一個誘導性評論檢測模型。
研究結果顯示，以TF-IDF辨識相似特徵詞彙，並使用TF-IDF進行文本向量化後，配合SVM模型進行預測的效果最佳。該模型的Precision、Recall、和F1 Score均達到80%以上，且AUC-ROC高達93.31%。根據我們的實驗結果，如果新竹地區的餐廳在Google評論中的評論內容包含這些特徵詞彙，則該評論的可信度值得質疑，相對地，該餐廳在Google評論的星級也可能失去其客觀性。

摘要(英)

With the rise of Google restaurant reviews, consumers can avoid the disappointment of dining experiences without the need for first-hand trials, simultaneously driving industry competition and facilitating the natural elimination of unfit businesses from the market. However, as time goes by, this intended purpose of the review system seems to have been distorted, with many operators inducing consumers to provide non-objective reviews through marketing activities, leading to a gradual decline in people′s trust in Google reviews. Therefore, this study aims to explore how to use machine learning techniques to construct a mechanism that can filter out inducive reviews, thereby enhancing the effectiveness of community management strategies.
This research takes Google restaurant reviews in the Hsinchu area as the subject of study, using four feature engineering methods, namely Word2Vec model, FastText model, TF-IDF, and three composite methods, to screen out a collection of inducive feature words from a large number of reviews. Subsequently, we use three different text vectorization methods, including TF-IDF, BoW, and BERT models, and finally, we construct an inducive review detection model with the SVM model as the classifier.
The research results reveal that the best predictive effect is achieved by using TF-IDF to identify similar feature words, using TF-IDF for text vectorization, and then using the SVM model for prediction. The Precision, Recall, and F1 Score of this model all exceed 80%, and the AUC-ROC reaches as high as 93.31%. According to our experimental results, if the review content of a restaurant in the Hsinchu area contains these feature words in Google reviews, then the credibility of the review deserves to be questioned, and the star rating of the restaurant on Google reviews may also lose its objectivity.

關鍵字(中)

★ 誘導式評論
★ 機器學習
★ 文字探勘
★ 社群經營策略
★ 數位轉型

關鍵字(英)

論文目次

摘要 i
Abstract ii
致謝 iv
目錄 v
圖目錄 viii
表目錄 ix
第一章緒論 1
1-1 研究背景與動機 1
1-2 研究目的 5
1-3 論文架構 6
第二章文獻探討 7
2-1 數位轉型與社群經營 7
2-2 網路評論 8
2-3 機器學習與文字探勘 10
第三章研究方法 14
3-1 研究設計與方法 14
3-2 資料來源 17
3-3 資料前處理 19
3-3-1 資料清洗 19
3-3-2 中文斷詞 21
3-3-3 去除停用詞 22
3-3-4 特徵工程 23
3-3-4-1 Word2Vec模型 26
3-3-4-2 fastText模型 27
3-3-4-3 詞頻逆文檔頻率(TF-IDF) 28
3-3-4-4 綜合特徵方法 29
3-3-5 文本標記 30
3-4 資料分析方法 32
3-4-1 文本向量 32
3-4-1-1 TF-IDF 32
3-4-1-2 詞袋模型(BoW) 32
3-4-1-3 BERT 33
3-4-2 支援向量機(SVM) 35
3-4-3 模型評估指標 36
第四章研究結果與分析 37
4-1 Word2Vec模型所分析的特徵詞彙集 37
4-2 fastText模型所分析的特徵詞彙集 40
4-3 TF-IDF模型所分析的特徵詞彙集 43
4-4 綜合特徵模型所分析的特徵詞彙集 46
4-5 使用不同文本向量方式進行SVM分類之實驗結果 49
第五章結論 53
5-1 結論 53
5-2 研究限制 54
5-3 未來研究與建議 55
參考文獻 57

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指導教授

曾筱珽(Hsiao-Ting,Tseng)

審核日期

2023-7-24

推文