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    政大機構典藏 > 商學院 > 統計學系 > 學位論文 >  Item 140.119/130954
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/130954

    Title: 情感分析於電影推薦與評論展現系統之應用
    Application of Sentiment Analysis in Movie Recommendation and Comment-Revealing System
    Authors: 黃德潔
    Huang, Te-Chieh
    Contributors: 鄭宇庭
    Huang, Te-Chieh
    Keywords: 文字探勘
    Text Mining
    Sentiment Analysis
    Feature Clustering
    Semantic Orientation
    Machine Learning
    Date: 2020
    Issue Date: 2020-08-03 17:31:00 (UTC+8)
    Abstract: 隨著以文字資訊為主的社交平台興起,例如:微博、推特、部落格…等微型網誌,消費者對於購買商品或服務品質的評價可以在網路世界中迅速傳播,對於其他消費者的購買意願造成很大的影響,同時也加深大眾對於該產品的品牌形象。對於電影產業更是如此,消費者只能透過片商剪輯的預告片,觀賞部分電影片段,就必須決定是否要進電影院觀賞,事後也沒有退換貨的服務,因此民眾在購買電影票之前,會更加注重網路上對於該部電影的相關評論以及心得分享。有鑑於此,如何從巨量的網路資訊當中,正確且有效率地辨別顧客所表達的語意與情緒,成為近年來文字探勘學者致力於探討的議題。
    Following the rise of social media platforms for text information, such as Weibo, Twitter and Blog. Consumers’ rating for purchasable commodity and service quality can be rapidly spread in social media. It causes significant effect to other consumers’ desire to purchase. It also impresses the public about the product’s brand imagine. Furthermore, in movie industry, consumers have to decide whether to go into theater only through watching the segments of movie trailer. They can’t get a refund when they feel regrettable. So consumers will pay more attention on related comments and knowledge-sharing. For this reason, how to identify consumer’s expression of mood and semantization correctly becomes the subject for dedicated scholars.
    This essay produces an efficient movie evaluation system. It collected netizen’s satisfactory list of comments from 2019 Yahoo movie web page. Through Feature Extraction, Attribute Capture, Sentiment Analysis, Semantic Orientation, Feature Clustering, Machine Learning Classification to classify comments in accord with polarity. This experiment proves that the accuracy reaching 83.74% and the F1-Measure reaching 84.29%. It means that this study has achieved its anticipative result in identifying the polarization of comments.
    There are two characters appearing in final comments. First, comments will be listed in sequence according to sentiment intensity that let users browse the most abundant ones at first place. Secondly, by matching opinion keywords and feature keywords to offer users the outcome of multi-faceted analysis which could let them know the evaluation of each film’s attribute. Through it to recommend the suitable movie to consumers.
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    Description: 碩士
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0107354004
    Data Type: thesis
    DOI: 10.6814/NCCU202000667
    Appears in Collections:[統計學系] 學位論文

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