社群媒體上的生成式人工智慧輿情探勘

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陳敬元(Ching-Yuan Chen) 查詢紙本館藏

畢業系所

資訊管理學系

論文名稱

社群媒體上的生成式人工智慧輿情探勘
(Public Opinion Mining of Generative AI)

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至系統瀏覽論文 (2029-7-8以後開放)

摘要(中)

生成式人工智慧 (GAI) 是當前發展最快且廣受討論的技術之一，對社會產生了重大影響。生成式AI能夠產生大量豐富且有趣的內容，但也因為一些問題而受到批評，例如侵犯版權或智慧財產權。本研究旨在利用自然語言處理技術探討社群媒體用戶對生成式AI內容的看法和情緒反應。研究聚焦於社群媒體平台Reddit，深入收集和分析用戶的討論和意見。數據範圍涵蓋2023年7月至2024年4月，來自多個子板塊和關鍵字，涵蓋文字、圖片和影片生成等各種生成式AI技術。本研究使用RoBERTa預訓練模型進行兩階段情感分類，分析貼文和評論中表達的情緒，並將其分類為主動和被動情緒。研究還使用潛在狄利克雷分佈來識別主要的討論主題，探討用戶對不同類型生成式AI技術的主要關注點。研究結果表明，只有相對較低比例的用戶對生成式AI技術表達了負面情緒，這表明即使GAI引發了許多爭議，大多數公眾仍對其保持積極的態度。雖然使用者對各種類型生成式AI技術的主要關注點略有不同，也存在許多相似的討論話題。本研究為技術開發者、政策制定者和社群媒體平台管理者提供了了解公眾情緒和解決生成式AI相關問題的看法。

摘要(英)

Generative artificial intelligence (GAI) is one of the fastest developing and widely discussed technologies, having a significant impact on society. However, while generative AI can produce a lot of rich and interesting content, it has also been criticized for some issues such as violating copyright or intellectual property rights. This study aims to explore social media users′ views and emotional responses to generative AI content using natural language processing techniques. The research focuses on Reddit, a social media platform, by deeply collecting and analyzing user discussions and opinions. The data spans multiple subreddits and keywords from July 2023 to April 2024, covering various generative AI technologies such as text, image, and video generation. This study employs the RoBERTa pre-trained model for a two-stage sentiment classification to analyze the emotions expressed in posts and comments, categorizing them into active and passive sentiments. Additionally, the study uses Latent Dirichlet Allocation to identify the main discussion topics, exploring users′ primary concerns regarding different types of generative AI technologies. The study′s findings indicate that a relatively low proportion of users expressed negative sentiments toward generative AI technology. This suggests that despite the numerous controversies surrounding GAI, the majority of the public maintains a positive outlook on it, and although there are slight differences in the main concerns regarding various types of generative AI technologies, there are also many similar discussion topics. This research provides insights for technology developers, policymakers, and social media platform managers to understand public sentiments and address concerns related to generative AI.

關鍵字(中)

★ 生成式人工智慧
★ 社群媒體
★ 輿情分析
★ 情感分析
★ 隱含狄利克雷分布
★ 情感分類

關鍵字(英)

★ Generative AI
★ Social media
★ Opinion mining
★ Sentiment analysis
★ LDA
★ Emotion Classification

論文目次

摘要 i
Abstract ii
Table of Contents iii
List of Figures v
List of Tables vi
I. Introduction 1
1-1 Research Background 1
1-2 Research Motivation 4
1-3 Research Objectives 8
II. Related Work 9
2-1 Public perceptions of emerging technology 9
2-2 Generative AI 11
2-3 Opinion mining and Sentiment analysis 14
III. Experiment Design 19
3-1 Research Framework 19
3-2 Data collection 19
3-3 Dataset 25
3-3-1 Data Characteristics 25
3-3-2 Data Preprocessing 27
3-3-3 Data Cleaning 28
3-4 Pretrained Model 30
3-5 Training Dataset 31
3-6 Sentiment Analysis 32
3-7 Topic Modeling 33
3-8 Emotion Classification 36
IV. Result and Discussion 42
4-1 Descriptive Statistical Analysis Result 42
4-2 Trend Analysis 47
4-3 Topic Modeling Result 49
4-3-1 Text Generative AI 49
4-3-2 Image Generative AI 50
4-3-3 Video Generative AI 51
4-3-4 Audio Generative AI 52
4-3-5 Comprehensive discussion of Generative AI 53
4-4 Emotion classification 54
V. Conclusion and Contribution 61
5-1 Conclusion 61
5-2 Contribution 63
5-3 Limitations and Future Research 66
References 67

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指導教授

曾筱珽蔡邦維(Hsiao-Ting,Tseng Pang-Wei Tsai)

審核日期

2024-7-10

推文