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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 盧信銘(Hsin-Min Lu) | |
dc.contributor.author | Yu-Ting Tu | en |
dc.contributor.author | 凃育婷 | zh_TW |
dc.date.accessioned | 2021-06-15T16:21:32Z | - |
dc.date.available | 2021-08-31 | |
dc.date.copyright | 2020-08-25 | |
dc.date.issued | 2020 | |
dc.date.submitted | 2020-08-06 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/52642 | - |
dc.description.abstract | 近年隨著論壇與社群平台的興起,許多人習慣在網路上分享自己對產品服務的看法,這些非結構化的資料中包含對個人或組織來說有價值的訊息,例如消費者能輔助做出購物決定、公司能從中找到改進產品的方向。為了要更快速準確地捕獲其中所蘊含的資訊,關鍵技術正是情感分析。在眾多文獻研究中,大多數著重於改善情感分析技術,較少看到專門研發情感分析工具的研究。我們認為有一套可直接執行情感分析的工具能帶來實質且具體的效益,因此將研究重點聚焦於開發情感分析開源工具。 本研究開發的工具希望能符合實用性與效能兩大目標。本研究透過探索過往情感分析文獻、訂立情感分析架構和調查現有情感分析工具,確立所要開發的工具特性,包含提供句子情感分類、屬性術語提取與屬性情感分類功能,處理繁體中文的分析,並主要基於順序遷移學習中的預訓練搭配微調模式,設計適合本研究的預訓練學習策略和微調模型架構,同時建立消費者評論資料集作為訓練測試數據。 藉由本研究制定的四類型實驗,分別驗證了預訓練策略的有效性、微調配置的合適性、所研發工具的可靠性,以及開發繁體中文工具的有用性,實驗結果證實我們設計的訓練策略與相關配置能勝過開源預訓練模型,並有助於提高模型能力;另外,與其它工具和經典論文方法進行比較,本研究所開發之工具senti_c在兩個資料集上的各項指標表現都優於比較對象,顯示senti_c對於處理情感分析問題能達到一定效能、提供更良好的分析結果;除此之外,透過測試各工具對於處理繁體與簡體中文文本的性能差異,可驗證本研究提供的繁體中文工具確實具有實用價值;最後,我們將經過完善測試的senti_c套件發佈至PyPI (pypi.org),一般大眾皆能自由下載運用。 | zh_TW |
dc.description.abstract | Large amounts of user comments and reviews on products, services, and events are readily accessible on social media and e-commerce platforms. These text data contain valuable information for individuals or organizations. Sentiment analysis facilitates the analysis of large amounts of unstructured review data, and may benefit consumers and business alike. Previous studies have accumulated large amounts of technical approaches for sentiment analysis. However, to the best of our knowledge, few high-quality open-source sentiment analysis tools are available for Traditional Chinese. To fill this gap, this thesis aims at developing an open-source toolkit for analyzing sentiment in Traditional Chinese text. We conducted an extensive review on the sentiment analysis literature and developed a sentiment analysis framework. A review of existing tools using this framework allows us to establish the main functionality of senti_c, a high-quality open-source sentiment analysis toolkit. The senti_c toolkit is a Python-based library that provides three main functions: sentence-level sentiment classification, aspect terms extraction, and aspect-level sentiment classification. We developed our own training data and adopted the sequential transfer learning approach to develop the machine learning-based prediction module based on the transformer-based deep learning natural language models. We conducted extensive experiments based on different variations of pre-training and fine-tuning strategies. Our experimental results showed that the training strategies we designed delivered models that outperformed current state-of-the-art open-source pre-training models. Moreover, senti_c consistently performed better than other baseline methods and toolkits currently available. While the main training data is in traditional Chinese, senti_c also has good performance for simplified Chinese. The senti_c toolkit is available from PyPI (pypi.org). | en |
dc.description.provenance | Made available in DSpace on 2021-06-15T16:21:32Z (GMT). No. of bitstreams: 1 U0001-0608202014285700.pdf: 6099258 bytes, checksum: e6dc3c326795652722027f585f617608 (MD5) Previous issue date: 2020 | en |
dc.description.tableofcontents | 誌謝 i 摘要 ii Abstract iii 主目錄 v 圖目錄 x 表目錄 xiii 第一章 緒論 1 1.1 研究背景與動機 1 1.2 研究架構 3 第二章 文獻回顧 5 2.1 情感分析概述 5 2.2 粒度 (Granularity) 8 2.3 應用領域 (Application Domain) 9 2.4 支援語言 (Supported Language) 11 2.5 任務 (Tasks) 11 2.5.1 情感偵測 (Sentiment Detection) 13 2.5.2 屬性偵測與分類 (Aspect Detection and Categorization) 16 2.5.3 觀點詞提取 (Opinion Expression Extraction) 18 2.5.4 命名實體識別 (Named Entity Recognition) 18 2.5.5 觀點摘要 (Opinion Summarization) 19 2.5.6 諷刺偵測 (Sarcasm Detection) 20 2.5.7 意見垃圾識別 (Opinion Spam Detection) 21 2.5.8 多模態情感分析 (Multimodal Sentiment Analysis) 21 2.5.9 創建情感詞典 (Lexicon Creation) 22 2.5.10 處理特殊案例 (Dealing with Special Cases) 22 2.6 文本表示 (Text Representation) 22 2.6.1 向量空間模型 (Vector Space Model) 23 2.6.2 分散式表示 (Distributed Representation) 24 2.7 技術方法 (Technical Approach) 28 2.7.1 基於知識方法 (Knowledge-based Approach) 29 2.7.2 機器學習方法 (Machine Learning Approach) 32 2.7.3 綜合方法 (Hybrid Approach) 79 2.8 小結 80 第三章 情感分析工具之調查 82 3.1 概述 82 3.2 各工具介紹 84 3.2.1 中文購物評論情緒分析 91 3.2.2 Opcluster-PT 91 3.2.3 Pattern 92 3.2.4 TextBlob 93 3.2.5 SnowNLP 93 3.2.6 新聞評論觀點挖掘系統 94 3.2.7 VADER 95 3.2.8 Twitter Sentiment Analysis using ConvNet 95 3.2.9 Twinword Inc:Sentiment Analysis API 96 3.2.10 Microsoft Azure:Text Analytics API 96 3.2.11 NLTK 97 3.2.12 spaCy 98 3.2.13 Stanford CoreNlp 99 3.2.14 Stanza 99 3.2.15 CKIP 100 3.2.16 CSentipackage 100 3.2.17 BosonNLP 101 3.2.18 Jiagu深度學習自然語言處理工具 102 3.2.19 HanLP 103 3.2.20 Google Cloud Natural Language API 103 3.3 小結 104 第四章 研究方法 108 4.1 研究問題 108 4.2 研究流程與模型設計 109 4.2.1 預訓練階段 111 4.2.2 微調階段 119 4.3 評估指標 129 4.3.1 句子情感分析指標 129 4.3.2 屬性情感分析指標 131 4.4 小結 133 第五章 實驗結果與討論 134 5.1 資料介紹 134 5.1.1 預訓練資料集 134 5.1.2 微調資料集 139 5.2 實驗過程相關設置 142 5.3 預訓練過程實驗與結果 146 5.3.1 各階段設定 146 5.3.2 各階段實驗結果 156 5.3.3 結果分析 157 5.4 模型設置比較實驗與結果 162 5.4.1 模型與參數設計驗證 162 5.4.2 模型與參數實驗組合 166 5.4.3 實驗結果 167 5.4.4 結果分析 168 5.5 其他工具/論文模型比較實驗與結果 169 5.5.1 相關設置與比較對象 169 5.5.2 實驗結果 176 5.5.3 結果分析 181 5.6 繁簡體中文差異實驗與結果 184 5.6.1 相關設置與比較對象 184 5.6.2 實驗結果 186 5.6.3 結果分析 191 5.7 小結 193 第六章 套件介紹 194 6.1 套件簡介 194 6.2 功能說明 196 6.2.1 句子模型重新微調 196 6.2.2 句子情感分類預測 196 6.2.3 屬性模型重新微調 197 6.2.4 屬性情感分析預測 198 6.3 使用範例 198 6.3.1 句子情感分類預測範例 198 6.3.2 屬性情感分析預測範例 199 第七章 結論 203 7.1 總結 203 7.2 研究貢獻 204 7.3 未來方向 206 參考文獻 208 | |
dc.language.iso | zh-TW | |
dc.title | 基於順序遷移學習開發繁體中文情感分析工具 | zh_TW |
dc.title | Developing Sentiment Analysis Toolkit for Traditional Chinese Using Sequential Transfer Learning | en |
dc.type | Thesis | |
dc.date.schoolyear | 108-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 林怡伶(Yi-Ling Lin),簡宇泰(Yu-Tai Chien) | |
dc.subject.keyword | 情感分析,句子情感分類,屬性術語提取,屬性情感分類,深度學習,順序遷移學習,預訓練,微調,繁體中文,工具開發, | zh_TW |
dc.subject.keyword | Sentiment Analysis for Traiditional Chinese,Sentence-level Sentiment Classification,Aspect Term Extraction,Aspect-level Sentiment Classification,Sequential Transfer Learning,Pre-training,Fine-tuning,Traditional Chinese,Tool Development, | en |
dc.relation.page | 219 | |
dc.identifier.doi | 10.6342/NTU202002535 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2020-08-07 | |
dc.contributor.author-college | 管理學院 | zh_TW |
dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
顯示於系所單位: | 資訊管理學系 |
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