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    政大機構典藏 > 資訊學院 > 資訊科學系 > 學位論文 >  Item 140.119/150169
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/150169


    Title: 基於Transformers的社群媒體輿論風向變化視覺化分析系統
    Visualization of Social Media Opinion Detection Using Transformers
    Authors: 陳岳紘
    Chen, Yue-Hung
    Contributors: 紀明德
    Chi, Ming-Te
    陳岳紘
    Chen, Yue-Hung
    Keywords: 資訊視覺化
    大型語言模型
    社群媒體
    Visualization
    Large language models
    Social media
    Date: 2024
    Issue Date: 2024-03-01 13:41:54 (UTC+8)
    Abstract: 近年來,社群媒體逐漸成為人們生活中不可或缺的一部分,而大型語言模型的出現提升了文本分析的可行性與發展性,在這樣的背景下,本研究探討了使用基於 transformers 的語言模型實現基於文本的視覺化系統的可能性,利用主題建模的技術擷取社群媒體中的風向變化,並且提出兩階段分群的作法提升風向變化分析的效率。為了結合對話式語言模型與視覺化系統,本研究也探討了如何使用 GPT 輸出特定模式的結果,透過提示工程的實驗,我們改良了留言的立場分析的提示詞,使輸出的結果能夠直接為後續程式所用。本研究也提出基於物理碰撞的視覺化方式,能夠讓使用者快速了解社群媒體中的風向變化,並且對感興趣的主題進行進一步的瞭解。我們利用時間軸表示立場分析的結果,並結合各種資訊,讓使用者能夠從各種不同面向對資料進行觀察。最後,我們也使用一連串量化分析的指標來測試這些結果,並提出一些使用案例。
    In recent years, social media has gradually become an indispensable part of people's lives. With the advancement of internet technology, the volume of data within social media has been steadily increasing, making the efficient extraction of information from social media a crucial challenge. On the other hand, the emergence of large language models has enhanced the feasibility and expansiveness of text analysis. Therefore, this study explores the possibility of implementing a text-based visualization system using transformer-based language models. The research focuses on utilizing topic modeling techniques to extract opinion changes within social media. Additionally, a visualization approach based on physical collision is proposed, allowing users to rapidly comprehend changes in the opinion of social media posts and gain further insights into topics of interest. The study also investigates how to use GPT models to output specific patterns. Through prompting engineering, the model is able to do stance analysis in comments, and the results can be directly utilized by subsequent programs. The stance analysis results are represented on a timeline, incorporating various information to enable users to observe data from different perspectives. Finally, a series of quantitative experiment are employed to evaluate these results, and several use cases are presented.
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    Description: 碩士
    國立政治大學
    資訊科學系
    110753121
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0110753121
    Data Type: thesis
    Appears in Collections:[資訊科學系] 學位論文

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