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研究生(外文):Yu-Hong Zhang
論文名稱(外文):Combining BERT with Sentiment Lexicon for Sentiment Analysis
口試委員(外文):Ya-Xuan HuangShin-Feng Lin
外文關鍵詞:Attention MechanismSentiment LexiconSentiment AnalysisNeural Network
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文本情緒分析(Textual Sentiment Analysis)是一件擬讓機器從文句中判別發文者當下情緒與情感反應的有趣工作,技術上而言,此問題可被當作一種將不同的字詞歸類為不同情緒種類的問題。但一個文句中常參雜多種不同詞類,甚至會包含不少與情緒無關的冗贅文字,要精準判別文句中的情緒並不容易,不少研究人員已嘗試利用機器學習技術來解決這個問題。在前人的研究中指出,應用專注力機制(Attention Mechanism)結合情緒辭典(Sentiment Lexicon Dictionary)的長短時記憶(Long Short-Term Memory,LSTM)神經網路模型可有不錯的效果,鑒於LSTM並非目前處理文句資料的主流神經網路模型,而且該方法僅能將情緒辭典直接套用,並未能依據文句語料作動態調適,所以本論文提出使用BERT模型來改進LSTM的專注力機制,並針對情緒辭典設計更好的調適機制,讓它可根據文句重新編碼以達到動態適能的目的,實驗結果顯示本論文提出之方法準確度皆超越先前最佳的模型。
Text sentiment analysis is an interesting topic that allows a machine to recognize the current sentiment and emotional reaction of the writer from the sentences. Technically, this problem can be regarded as a problem of classifying different sentences into different emotion types. However, a sentence is often mixed with many different parts of speech, and even contains too many words that are not related to emotions. It is not easy to accurately recognize emotions in a sentence, which often contains many different words or even redundant words that have nothing to do with emotions. Many researchers have attempted to solve this problem by using machine learning techniques. According to previous studies, using the Long Short-Term Memory (LSTM) combined with the Attention mechanism and the Sentiment Lexicon can be quite effective. Nonetheless, LSTM is currently not the most effective neural network model for processing textual data. It can only be applied to an emotional lexicon dictionary in a limited and specific domain. To incorporate the power of both the sentiment lexicon and the wide-domain textual corpus, this thesis proposes a BERT model to improve the simple mechanism of LSTM and to design an adaptive mechanism for the sentiment lexicon to better adapt the encoding of the lexicon entries to our textual corpus. Experimental results show that our method outperform the state-of-the-art models.
誌謝 I
摘要 II
Abstract III
圖目錄 V
表目錄 VI
第1章 緒論 1
1.1 研究背景 1
1.2 研究動機 2
第2章 文獻探討 3
2.1 情緒分析 3
2.2專注力機制文本分析模型 5
2.2.1 專注力機制 5
2.2.2 Transformer 7
2.3 BERT 8
2.4 使用情緒辭典之類神經網路 9
2.5 本研究之方法設計思考背景 10
第3章 情緒引導之 BERT(Sentiment Guided BERT) 11
3.1 整體架構 11
3.1.1 硬助推 (Hard Boost) 14
3.1.2 軟助推 (Soft Boost) 15
3.1.3 混合助推 (Merge Boost) 16 本研究提出之方法 (Proposed Method) 18
3.2.1 可學習式情緒字典嵌入 (Learnable Sentiment Lexicon Embedding) 19
3.2.2 混合因子自動調適 (Automatic Blending Factor Adaptation) 19
第4章 實驗設置 23
4.1情緒字典與資料集 23
4.1.1 情緒字典 23
4.1.2 資料集- Stanford Sentiment Treebank 24
4.2 類神經網路架構 25
4.3 訓練方式 26
第5章 實驗結果與分析 27 比較模型 27
實驗結果 28
模型分析 29
第6章 結論與未來方向 33
參考文獻 35
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