基於深度學習模型應用於中文之目標情感分析

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姓名

江佳儒(Chia-Ju Chiang) 查詢紙本館藏

畢業系所

資訊管理學系在職專班

論文名稱

基於深度學習模型應用於中文之目標情感分析

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至系統瀏覽論文 (2027-7-18以後開放)

摘要(中)

隨著網路的發展，人們透過網際網路扮演資訊分享者角色，對商品及服務分享評論，對於消費者而言，可依據評論了解不同商品及服務之間的優缺點，因此我們想利用情緒分析找出評論中隱藏的價值。

過往基於目標情感分析的研究大多用於英文資料集上，而本研究主要想利用目標情感分析中最新及討論度較高的深度學習模型來探討用於中文資料集的適應性，以此提供給業界行銷相關部門，可以針對線上評論來瞭解顧客的真實意見，進而幫助市場行銷。

本研究將統整目標情感分析最新的做法，基於各深度學習模型方法的不同來探討應用於中文資料集上的適應性，並進行比較與分析，提供目標情感分析使用於中文資料集上的準則。實驗結果顯示，基於BERT模型的LCF-ATEPC、R-GAT和AEN-BERT在中文文本的效果表現皆優於未使用BERT的模型，其中又以AEN-BERT為最佳模型方法。

摘要(英)

With the development of the Internet, people play the role of information sharers by sharing reviews on goods and services through the Internet. For consumers, reviews can be used to understand the strengths and weaknesses of different goods and services. Therefore, we can use sentiment analysis to find the hidden value from reviews.

Majority of previous studies based on aspect sentiment analysis were conducted on English datasets. In this study, we focus on the latest and highly discussed deep learning models of aspect sentiment analysis to explore adaptability on Chinese language. So as to provide the marketing department in the industry and it will help marketing department with online reviews to understand actual opinions from customers.

This study will integrate the latest methods of aspect sentiment analysis. Based on each deep learning model approach to explore adaptability of Chinese text, compare and analyze them. Provide the guidelines for use of aspect sentiment analysis on Chinese text. Our experimental results show that the BERT-based models, LCF-ATEPC, R-GAT and AEN-BERT perform better in Chinese text than the non-BERT based models. Moreover, the AEN-BERT is the best overall performing model in our experiments.

關鍵字(中)

★ 深度學習
★ 自然語言處理
★ 目標情感分析

關鍵字(英)

★ deep learning
★ natural language process
★ aspect sentiment analysis

論文目次

摘要 I
Abstract II
誌謝 III
目錄 IV
表目錄 VI
圖目錄 VII
第一章緒論 1
1.1 研究背景 1
1.2 研究動機 3
1.3 研究目的 3
1.4 研究流程與論文架構 4
第二章文獻探討 6
2.1 目標情感分析Aspect-Level Sentiment Analysis 6
2.2 基於深度學習於目標情感分析 8
2.2.1 CNN Based 8
2.2.2 RNN Based 9
2.2.3 Memory Network (MN) 13
2.2.4 Transformer 15
2.2.5 GNN Based 17
2.3 基於深度學習於目標情感分析之中文文本 19
2.4 詞向量生成 20
2.4.1 Word2Vec 20
2.4.2 fastText 21
2.4.3 ELMo(Embeddings from Language Models) 21
2.4.4 BERT(Bidirectional Encoder Representations from Transformers) 22
2.5 成效評估 23
2.6 綜合討論 24
第三章研究方法 26
3.1 資料集介紹 26
3.2 資料前處理 28
3.2.1 將文本內容簡體轉繁體 29
3.2.2 斷詞 29
3.2.3 刪除停用詞(Stopword Removal) 29
3.2.4 依存句法分析(Dependency Parsing) 29
3.3 模型方法比較 30
3.4 實驗：模型方法超參數設置 31
第四章結果與分析 34
4.1 整體分析 34
4.2 中英文差異分析 35
4.3 高頻目標詞個別分析 37
第五章總結 42
5.1 結論 42
5.2 實驗貢獻 42
5.3 研究限制 43
5.4 未來研究方向 43
參考文獻 44

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指導教授

柯士文(Shi-Wen Ke)

審核日期

2022-8-24

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