旅館評論自動分析與歸納系統

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姓名

賴威廷(Wei-Ting Lai) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

旅館評論自動分析與歸納系統
(Automatic Analysis and Summarization System for Hotel Reviews)

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至系統瀏覽論文 (2025-7-14以後開放)

摘要(中)

隨著現在網路的快速發展，任何人都可以輕而易舉的在網路上留下評論，表達自己的觀點或感受。這些評論都是很重要的數據，但如果單純利用人工進行查詢、統計這些龐大的數據，顯然是困難且缺乏效率的。而透過自然語言處理分析可以快速的知道人們對產品、服務等具體意見。因此本篇論文實作一個旅館評論自動分析與歸納系統，能夠有效的取代以往人工查看評論尋找資訊的過程，以節省時間。
在旅館評論自動分析與歸納系統中，我們訓練了句子邊界檢測模型，改善在面對網路評論中常見的文法或標點符號錯誤時，基於統計模型或文法規則的方式難以處理的問題，提升了句子分割的效果。並透過訓練情緒分析、關鍵字提取模型，找出旅館中最多人提到的優、缺點，將使用者評論按照關鍵字分類，接著透過分群演算法統整相關的關鍵字。最後將成果以網頁呈現，讓使用者能便利的進行查詢。

摘要(英)

With the rapid development of the Internet nowadays, it′s easy for anyone to leave a comment on the web, expressing their own opinions or feelings. These comments are important data, but it would be difficult and inefficient to conduct manual inquiries and statistics on these large data set. Using Natural Language Processing technology, it is possible to quickly know people′s specific opinions on products, services, etc. Therefore, this paper implements an automatic analysis and summarization system for hotel reviews. It can effectively save the time of manually checking comments and looking for information.
In the automatic analysis and summarization system, we have trained a sentence boundary detection model. Improve the problem of grammatical or punctuation errors commonly found in online comments, which difficult to handle by statistical models or grammatical rules. Through training on sentiment analysis and keyword extraction models, we identify the most frequently mentioned strengths and weaknesses of hotels. The comments are categorized by keywords, and the clustering algorithm is used to organize the relevant keywords. Finally, the results are presented on a web page for users to conveniently make inquiries.

關鍵字(中)

★ 深度學習
★ 句子邊界檢測
★ 文字分析

關鍵字(英)

★ Deep learning
★ Sentence Boundary Detection
★ Text Analysis

論文目次

摘要 I
Abstract II
目錄 III
圖目錄 V
表目錄 VI
第一章緒論 1
1.1 研究動機 1
1.2 研究背景 1
1.3 研究目的 2
1.4 論文架構 3
第二章相關背景與研究 4
2.1 詞嵌入(Word Embedding) 4
2.2 降維(Dimensionality Reduction) 5
2.2.1 主成分分析(Principal Component Analysis) 5
2.2.2 後處理演算法(Post-Processing Algorithm) 6
2.2.3 詞嵌入降維 7
2.3 句子邊界檢測(Sentence Boundary Detection)相關研究 8
2.4 情緒分析(Sentiment Analysis)相關研究 9
2.5 關鍵字提取(Keyword Extraction)相關研究 10
2.6 語言模型(Language Model)相關研究 12
2.6.1 ELMo 13
2.6.2 BERT 13
第三章系統說明與分析方法 17
3.1 系統架構介紹 17
3.2 句子邊界檢測 18
3.3 情緒分析 21
3.4 關鍵字提取 23
3.5 關鍵字合併 25
3.6 關鍵字分群 27
3.7 使用者介面 29
第四章實驗結果與分析 32
4.1 測試資料 32
4.2 評估指標 32
4.3 實驗結果與分析 34
4.3.1 實驗一: 不同模型對情緒分析之結果 34
4.3.2 實驗二: 不同模型對句子邊界檢測之結果 35
4.3.3 實驗三: 不同模型對關鍵字提取之結果 36
4.3.4 實驗四: 不同維度對關鍵字分群之影響 36
4.3.5 實驗五: 時間評估 39
第五章結論與未來研究方向 40
參考文獻 41

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指導教授

鄭旭詠(Hsu-Yung Cheng)

審核日期

2020-7-21

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