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系統識別號 U0026-2406201522460600
論文名稱(中文) 多情感辭典特徵層級情感分析之評論自動摘要
論文名稱(英文) Using Multi-Lexicons in Feature-Level Sentiment Analysis for Reviews Summarization
校院名稱 成功大學
系所名稱(中) 資訊管理研究所
系所名稱(英) Institute of Information Management
學年度 103
學期 2
出版年 104
研究生(中文) 孫義峰
研究生(英文) Yi-Feng Sun
學號 R76024132
學位類別 碩士
語文別 中文
論文頁數 54頁
口試委員 指導教授-王惠嘉
口試委員-盧文祥
口試委員-高宏宇
口試委員-劉任修
中文關鍵字 文字探勘  產品特徵擷取  情感分析 
英文關鍵字 text mining  product feature extraction  sentiment analysis 
學科別分類
中文摘要 現今社會中旅遊已經成為人們生活中的一部分,出外旅行時難免需要一個住所,因此住宿服務產業也就隨之蓬勃。由於網路資訊發達,消費者在規劃旅遊行程時已不必親自前往旅行社詢問旅遊資訊,自行在家中使用個人電腦瀏覽景點及旅館的資訊即可。
許多消費者在決定要入住哪間旅館前,需要取得旅館相關資訊以協助他們做決策,其中最重要的資訊來源就是之前的消費者所撰寫的評論。但Web 2.0盛行使網路上評論數量快速成長,消費者已不易以人工閱讀方式將所有評論看完,因此設計一套自動分析評論的系統就可以協助使用者更有效率的了解評論內容。
在自動分析消費者撰寫之評論時,最主要的目的在於了解該消費者在評論中所表達之意見為正向(讚賞)或負向(批評),利用情感分析技術即可辨別出消費者在評論中想表達之意見。在情感分析方法中,需要知道意見字詞之描述對象,才能釐清其真正含意,在本研究中意見字詞之描述對象稱為特徵,本研究提出非監督式之學習方法,藉由詞性排列模式來幫助系統自動擷取出旅館之特徵,並且加上多源辭典情感分析技術,取出評論中重要的部分(即消費者之意見),透過摘要的方式將評論整合,提供消費者了解旅館的途徑,以利消費者能夠縮短選擇旅館之決策時間。
最終透過實驗來驗證本研究所提出之方法,而實驗結果顯示本研究提出之方法能夠有效地擷取出旅館之特徵,評估指標F-measure達到.628,在特徵擷取表現上優於過去之研究。
英文摘要 Nowadays, tourism has become a part of life. Before reserving hotels, customers need some information, which the most important source is online reviews, about hotels to help them make decisions. Due to the dramatic growing of online reviews, it is impossible for customers to read all reviews manually. Therefore, designing an automatic review analysis system, which summarizes reviews, is necessary. The main purpose of the system is to understand the opinion of reviews, which may be positive or negative. In other words, the system would analyze whether the customers who visited the hotel like it or not. Using sentiment analysis methods will help the system achieve the purpose. In sentiment analysis methods, the target of opinion (we call it “feature”) should be recognized to clarify the polarity of the opinion because polarity of the opinion may be ambiguous. Hence, we propose an unsupervised method using Part-Of-Speech pattern and multi-lexicons sentiment analysis to summarize all reviews and help customers know hotels as well as make decisions efficiently. Experimental results show that our method outperforms the state of the art methods with F-measure .628.
論文目次 第1章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 4
1.3 研究範圍與限制 5
1.4 研究流程 6
1.5 論文大綱 7
第2章 文獻探討 9
2.1 自然語言處理 9
2.1.1 詞性標記 9
2.1.2 向量空間模型 10
2.1.3 中文斷詞處理 11
2.1.3.1 基於統計資訊的機器學習方法 11
2.1.3.2 基於字典方法 13
2.2 機器學習 13
2.3 情感分析 14
2.3.1 廣義知網知識本體架構 15
2.4 產品特徵擷取 16
2.5 小結 18
第3章 研究方法 19
3.1 研究架構 19
3.2 資料前處理模組 21
3.3 特徵擷取模組 25
3.4 情感分析模組 27
3.5 小結 30
第4章 系統建置與驗證 32
4.1 系統環境建置 32
4.2 實驗方法 33
4.2.1 資料來源 34
4.2.2 評估指標 38
4.3 參數設定 39
4.4 實驗結果 40
4.4.1 實驗一 40
4.4.2 實驗二 41
4.4.3 實驗三 42
4.4.4 實驗四 42
4.4.5 實驗五 45
第5章 結論以及未來方向 47
5.1 研究成果 47
5.2 未來研究方向 49
參考文獻 51
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