應用深度學習於智慧型手環口碑情感分析研究

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Title:	應用深度學習於智慧型手環口碑情感分析研究
Other Titles:	Applying deep learning for sentiment analysis on word of mouth of smart bracelet
Authors:	鄧宏洲;Teng, Hung-Chou
Contributors:	淡江大學資訊管理學系碩士在職專班戴敏育;Day, Min-Yuh
Keywords:	文字探勘;情感分析;智慧型手環;網路口碑;深度學習;text mining;Sentiment analysis;Smart Bracelet;eWOM;Deep Learning
Date:	2017
Issue Date:	2018-08-03 14:54:42 (UTC+8)
Abstract:	社群網路的興起，許多的消費者樂於在社群媒體上討論分享，表達自己對產品的意見。企業可透過大量的網路評論分析市場上消費者對產品各項特徵的喜好與優缺點，但在過去的文獻中較少應用深度學習於中文評論的情感分析上。本論文的貢獻為透過文本分析建構出專屬於智慧型手環領域的情感意見詞典，並利用深度學習遞迴神經網路長短期記憶技術於智慧型手環口碑情感分析，與貝氏演算法、支援向量機的結果互相比較。實驗結果顯示，貝氏演算法的正確率為70.67%、支援向量機得到66.01%、深度學習則為89.94%。從而證明深度學習在情感分析上的預測效果最為出色。 The rise of social networking, many consumers are willing to discuss in the community media to share, express their views on the product. Enterprises can analyze the consumers'' preferences and advantages and disadvantages of the various products on the market through a large number of online reviews, but in the past the literature is less applied to the Deep Learning in the Sentiment Analysis of Chinese comments. The contribution of this thesis is to construct a sentiment dictionary which belongs to the field of Smart Bracelet. And applying Deep Learning and Recursive Neural Network Long Short Memory technology in the Smart Bracelet word of mouth Sentiment Analysis. And compared with the results of Naïve Bayes algorithm and Support Vector Machine. The experimental results show that the correct rate of Naïve Bayes algorithm is 70.67%, the Support Vector Machine is 66.01%, and Deep Learning is 89.94%. So as to prove Deep Learning in the Sentiment Analysis of the most effective prediction.
Appears in Collections:	[Graduate Institute & Department of Information Management] Thesis

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