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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 黃恆獎 | zh_TW |
dc.contributor.advisor | Heng-Chiang Huang | en |
dc.contributor.author | 黃昕 | zh_TW |
dc.contributor.author | Hsin Huang | en |
dc.date.accessioned | 2023-08-16T16:26:18Z | - |
dc.date.available | 2023-11-09 | - |
dc.date.copyright | 2023-08-16 | - |
dc.date.issued | 2023 | - |
dc.date.submitted | 2023-08-07 | - |
dc.identifier.citation | Selenium 4.10.0. https://pypi.org/project/selenium/, 2023. Accessed: 2023-06-01.
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Springer, 2009. H. Huang. Thesis repository. https://github.com/xinhuang0716/Thesis, 2023. Accessed: 2023-06-01. R. Jeffrey, P. Bian, F. Ji, P. Sweetser Kyburz, et al. The wisdom of the gaming crowd. In Annual Symposium on Computer-Human Interaction in Play. Association for Computing Machinary, 2020. Z. H. Kilimci, H. Yörük, and S. Akyokus. Sentiment analysis based churn prediction in mobile games using word embedding models and deep learning algorithms. In 2020 International Conference on Innovations in Intelligent Systems and Applications(INISTA), pages 1–7. IEEE, 2020. J. Lee, D.-H. Park, and I. Han. The effect of negative online consumer reviews on product attitude: An information processing view. Electronic commerce research and applications, 7(3):341–352, 2008. D. Lin, C.-P. Bezemer, Y. Zou, and A. E. Hassan. An empirical study of game reviews on the steam platform. Empirical Software Engineering, 24:170–207, 2019. Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. 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In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 351–365, 2021. X. Pei, Y. Li, and C. Xu. Gpt self-supervision for a better data annotator. arXiv preprint arXiv:2306.04349, 2023. X. Qiu, T. Sun, Y. Xu, Y. Shao, N. Dai, and X. Huang. Pre-trained models for natural language processing: A survey. Science China Technological Sciences, 63(10):1872–1897, 2020. J. Quinn, J. McEachen, M. Fullan, M. Gardner, and M. Drummy. Dive into deep learning: Tools for engagement. Corwin Press, 2019. Retrieved from https://d2l.ai/. A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever, et al. Language models are unsupervised multitask learners. OpenAI blog, 1(8):9, 2019. H. Schutze, C. D. Manning, and P. Raghavan. Introduction to information retrieval. Cambridge University Press, 2008. C. Shen, L. Cheng, R. Zhou, L. Bing, Y. You, and L. Si. 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Rao, and C. Kulkarni. A survey on sentiment analysis methods, applications, and challenges. Artificial Intelligence Review, 55(7):5731–5780, 2022. J. P. Zagal, A. Ladd, and T. Johnson. Characterizing and understanding game reviews. In Proceedings of the 4th international Conference on Foundations of Digital Games, pages 215–222, 2009. Y. Zhu, P. Zhang, E.-U. Haq, P. Hui, and G. Tyson. Can chatgpt reproduce human-generated labels? a study of social computing tasks. arXiv preprint arXiv:2304.10145, 2023. Z. Zuo. Sentiment analysis of steam review datasets using naive bayes and decision tree classifier. https://www.ideals.illinois.edu/items/106100, 2018. Library of University of Illinois as Urbana-Champaign. | - |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88937 | - |
dc.description.abstract | 遊戲評論中隱藏了許多有用的資訊,像是玩家對遊戲平衡機制的建議、遊戲運行時所遇到的錯誤情形,又或是被玩家讚譽有加的遊戲特色,對於遊戲開發商來說,這些評論資訊在遊戲開發、營運、維護上是極具價值的決策參考依據。然而,對部分獨立(Indie)遊戲廠商而言,並無多餘的人力、財力來對遊戲評論進行資訊檢索、分析,因此,本研究希望能利用深度學習與預訓練模型來幫助遊戲廠商進行自動化評論分類,快速地從繁多的遊戲評論中挑出包含特定類別資訊的評論,進而拓展遊戲評論應用的可能性。本研究以數位遊戲平台Steam上的英文遊戲評論作為模型訓練資料,並以多標籤標註的方式將評論標註上Suggestion、Pro、Con、Bug等標籤;在分類策略方面,則根據資料集特性、分類方式與分類模型等三個方向,規劃出多種分類策略。最終發現,使用RoBERTa預訓練模型與簡單的神經網路對多標籤資料集進行訓練,所建構的單一分類模型能得到達到最好的成效,Pro、Con與Bug等類別F1-Score皆高於0.8,而Suggestion類別也接近0.75,與過往研究比較也來得優異。因此,透過本研究的自動化評論分類,遊戲開發商可對遊戲評論進行快速且精準地分類,進而加速評論分析流程,來更好地發展商業應用。 | zh_TW |
dc.description.abstract | Game reviews often contain valuable information, such as players' suggestions on game balance, bugs during gameplay, or game features that are highly popular. These reviews serve as invaluable references for game developers during game development, operation, or maintenance. Nevertheless, some Indie game developers may lack the personnel and resources to retrieve review information and conduct further analysis. Therefore, this study aims to use deep learning and pretrained models to assist game developers in automatic game review classification, to swiftly identify the reviews that contain specific categories of information from a large number of game reviews, and thereafter broaden the potential applications of game reviews. In this study, English game reviews on the digital game platform Steam were used as training data. The reviews were labeled with categories such as Suggestion, Pro, Con, and Bug via a multi-label annotation approach. And the classification strategies are designed based on dataset characteristics, classification methods, and classification models. Our findings indicate that using the RoBERTa pretrained model and a simple neural network to train the multi-labeled dataset resulted in the best performance after all. The F1-Scores for categories such as Pro, Con, and Bug were all above 0.8, and the Suggestion category was close to 0.75, which compares favorably to previous research.Therefore, through the automatic game review classification in this study, game developers can rapidly and precisely classify game reviews, accelerating the review analysis process to better develop commercial applications. | en |
dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-08-16T16:26:18Z No. of bitstreams: 0 | en |
dc.description.provenance | Made available in DSpace on 2023-08-16T16:26:18Z (GMT). No. of bitstreams: 0 | en |
dc.description.tableofcontents | 口試委員審定書 i
致謝 ii 摘要 iii Abstract iv 目錄 vi 圖目錄 ix 表目錄 x 第一章 緒論 1 1.1 研究背景 1 1.2 研究動機 2 1.3 研究目的 3 1.4 研究流程 3 1.5 預期成果及貢獻 4 第二章 文獻探討 5 2.1 應用場景 7 2.2 分類模型 10 2.3 獨立遊戲 11 第三章 資料集 13 3.1 遊戲種類選擇 13 3.2 遊戲選擇與遊戲評論數據 14 3.3 資料標註 17 3.3.1 標註方式與標註流程 19 3.3.2 遊戲評論分類定義 20 3.3.2.1 評論判斷標準: Pro / Con 20 3.3.2.2 評論判斷標準: Bug 21 3.3.2.3 評論判斷標準: Suggestion 21 3.4 資料分佈 22 第四章 研究方法 25 4.1 模型評估 25 4.2 機器學習 26 4.2.1 Decision Tree 26 4.2.2 Support-Vector Machine 27 4.3 深度學習 29 4.3.1 深度學習 29 4.3.2 預訓練模型 30 4.4 分類策略 32 4.4.1 單標註資料集 / 多標註資料集 32 4.4.2 單一模型 / 多模型 34 4.4.3 機器學習 / 深度學習 35 4.5 其他 36 4.5.1 實驗環境 / 版本 36 4.5.2 實驗過程 37 4.5.2.1 資料前處理 37 4.5.2.2 模型訓練 38 4.5.2.3 分類結果衡量 38 第五章 研究結果 39 5.1 各分類策略表現 39 5.2 參數優化 40 5.3 成果應用 43 第六章 結論 45 6.1 研究貢獻 45 6.2 研究應用 47 6.3 研究限制 47 6.3.1 擴大測試至跨遊戲評論 47 6.3.2 應用至非獨立類型遊戲 48 6.3.3 強化資料標註 48 6.4 未來展望 49 6.4.1 評論資訊檢索架構 49 6.4.2 強度分數 50 6.4.3 生成式語言模型應用 51 參考文獻 53 | - |
dc.language.iso | zh_TW | - |
dc.title | 深度學習框架應用於Steam 遊戲評論分類 | zh_TW |
dc.title | Application of Deep Learning for Steam Game Review Classification | en |
dc.type | Thesis | - |
dc.date.schoolyear | 111-2 | - |
dc.description.degree | 碩士 | - |
dc.contributor.coadvisor | 楊立偉 | zh_TW |
dc.contributor.coadvisor | Li-Wei Yang | en |
dc.contributor.oralexamcommittee | 潘令妍;查士朝 | zh_TW |
dc.contributor.oralexamcommittee | Ling-Yen Pan;Shi-Cho Cha | en |
dc.subject.keyword | Steam,遊戲評論,獨立遊戲,深度學習,文本分類, | zh_TW |
dc.subject.keyword | Steam,Game Reviews,Indie Games,Deep Learning,Text Classification, | en |
dc.relation.page | 57 | - |
dc.identifier.doi | 10.6342/NTU202300916 | - |
dc.rights.note | 同意授權(全球公開) | - |
dc.date.accepted | 2023-08-09 | - |
dc.contributor.author-college | 管理學院 | - |
dc.contributor.author-dept | 國際企業學系 | - |
顯示於系所單位: | 國際企業學系 |
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