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Title: | 互動式主題標籤推薦系統 Interactive hashtag recommendation system |
Authors: | 林俊廷 Lin, Chun-Ting |
Contributors: | 李蔡彥 Li, Tsai-Yen 林俊廷 Lin, Chun-Ting |
Keywords: | 推薦系統 自然語言處理 社群媒體 主題標籤 Recommendation system Natural language processing Social media Hashtag |
Date: | 2022 |
Issue Date: | 2022-07-01 16:21:47 (UTC+8) |
Abstract: | 隨著網絡的不斷發展,越來越多的使用者將自己的所見所聞,透過推文(Tweet)的形式分享在社群媒體(Social Media)之中。這些推文以主題標籤(Hashtag)為聯結,在社群媒體中構成了許許多多的討論主題(Topic)。但由於大多數的使用者都沒有使用主題標籤的習慣,導致大量的推文無法被即時歸類到對應的主題,使得資訊呈現出離散的狀態。為了解決上述問題,本文提出了一種互動式主題標籤推薦系統,預測使用者所發推文的主題,以互動的方式推薦相關的主題標籤。此推薦系統可根據使用者的互動反饋,在編寫推文的不同階段提供適合的主題標籤,幫助社群形成主題共識,促進社群媒體意見的快速收斂。在實驗中,本研究邀請受試者使用此推薦系統,透過受試者的反饋來驗證系統的有用性。實驗結果顯示,本系統提出之互動式推薦流程可以幫助使用者找到適合推文主題的主題標籤。 With the progressive advance of Internet technologies, more and more users share their lives by posting tweets on social media platforms like Twitter. These tweets use hashtags as links to constitute discussion topics on social media. However, since most users are not used to using hashtags, a large number of tweets cannot be classified into corresponding topics immediately, which leads to a discrete state of information. To solve this problem, in this thesis, we propose an interactive hashtag recommendation system, which predicts the topic of an input tweet and interactively recommends rele-vant hashtags. This recommendation system can provide suitable hashtags in different phases of writing a tweet based on the interactive feedback of a user, help the commu-nity to reach a consensus, and increase the convergence speed of opinions on social media. We conducted user experiments to verify the usability of the recommendation system. The experimental results and user feedbacks reveal that the interactive hashtag recommendation can help users find suitable hashtags about the tweet’s topic. |
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Description: | 碩士 國立政治大學 資訊科學系 109753208 |
Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0109753208 |
Data Type: | thesis |
DOI: | 10.6814/NCCU202200469 |
Appears in Collections: | [資訊科學系] 學位論文
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