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研究生: 王皓平
Wang, Hao-Ping
論文名稱: 運用類神經網路方法分析基於面向的情感極性分類
Aspect-based Sentiment Polarity Classification using Neural Network Methods
指導教授: 侯文娟
Hou, Wen-Juan
口試委員: 郭俊桔
Kuo, June-Jei
方瓊瑤
Fang, Chiung-Yao
侯文娟
Hou, Wen-Juan
口試日期: 2022/06/20
學位類別: 碩士
Master
系所名稱: 資訊工程學系
Department of Computer Science and Information Engineering
論文出版年: 2022
畢業學年度: 110
語文別: 中文
論文頁數: 48
中文關鍵詞: 自然語言處理雙向長短期記憶自注意力機制情感分析編碼器深度學習
英文關鍵詞: Natural language processing, Bi-directional Long Short-Term Memory, Self-attention mechanism, sentiment analysis, encoder, deep learning
研究方法: 主題分析比較研究文件分析法
DOI URL: http://doi.org/10.6345/NTNU202200793
論文種類: 學術論文
相關次數: 點閱:115下載:8
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  • 隨著時代以及科技技術的成長,人們不像過去一樣,需要查看報紙、購買雜誌、詢問左右鄰居的情報才能知道自己想要得知的資訊。在科技技術的成長下,不管是餐廳的評價、筆記型電腦的實用程度,大部分的人們都可以使用網際網路來查看是否有所想要的資訊。
    本論文使用的資料集由SemEval-2014 Task 4官方所提供,並且含有四項子任務:(一) Aspect term extraction、(二) Aspect term polarity、(三) Aspect category detection、(四) Aspect category polarity,本論文進行第二項子任務研究,判斷出句子中的面向詞是正面、負面或中立,評估方式採用Accuracy,並且與當年競賽結果相比較。
    本論文實驗方法將資料先進行前處理並且轉成詞向量作為輸入的來源,以及將極性做情感標籤,並且使用Bi-LSTM (Bi-directional Long Short-Term Memory)、Self-attention(自注意力機制)及使用Two-level encoding對資料進行訓練。
    最後去比對每種不同模型的準確率,結果顯示Two-level encoding預測準確率餐廳達82%,筆記型電腦則達78%。

    With the development of the times and technology, people are not the same as in the past, that they need to check newspapers, buy magazines, and ask their neighbors for information to know what they want to know. With the development of technology, most people can use the Internet to check whether they have the information they want, no matter it is restaurant reviews or the practicality of laptops.

    The dataset used in this study is officially provided by SemEval-2014 Task 4, which contains four subtasks: (1) Aspect term extraction, (2) Aspect term polarity, (3) Aspect category detection, (4) Aspect category polarity, This paper conducts the second sub-task study to judge whether the term words in the sentence are positive, negative or neutral.

    The experimental method of this study pre-processes the data and converts it into the word vector as the source of input, and uses polarity as emotional label. Then Bi-LSTM (Bi-directional Long Short-Term Memory), Self-attention and Two-level encoding models are used to train the data.

    Finally, to compare the accuracy of each different model, the results show that using the two-level encoding method, the prediction accuracy rate is 82% for restaurants, and 78% for laptops.

    第一章 緒論 1 第一節 研究動機 1 第二節 研究目的 2 第三節 論文架構 3 第二章 文獻探討 4 第一節 自然語言處理工具包(NLTK) 4 第二節 情感分析 5 第三節 長短期記憶模型 7 第四節 自注意力機制 8 第五節 條件隨機場 11 第三章 實驗方法與步驟 14 第一節 實驗資料 14 第二節 評估指標 15 第三節 實驗流程 16 第四節 資料整理 18 第五節 去除標點符號及停用詞 20 第六節 情感標籤 23 第七節 轉換為向量 24 第八節 雙向長短期記憶 28 第九節 Encoder-Decoder 29 第四章 實驗結果 32 第一節 開發環境 32 第二節 實驗結果 33 第三節 分析與討論 40 第五章 結論與未來展望 43 第一節 結論 43 第二節 未來研究方向 44 參考文獻 45

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