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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/111176

    Title: 基於語音辨識技術之電子商務輿情推薦系統之研究
    Other Titles: A study of the recommendation systems with sentiment analysis of E-commerce based on speech recognition technique
    Authors: 侯以恆;Hou, Yi-Heng
    Contributors: 淡江大學資訊管理學系碩士班
    蕭瑞祥;Shaw, Ruay-Shiang
    Keywords: 語音辨識;輿情分析;電子商務;推薦系統;Speech recognition;sentimental analysis;e-commerce;Recommendation System
    Date: 2016
    Issue Date: 2017-08-24 23:45:54 (UTC+8)
    Abstract: 根據尼爾森2013年的市調發現,約77%的消費者相信陌生網友在網路上發表的意見評價。從語音辨識技術發展趨勢來看,越來越多應用於家電、智慧型手機,甚至在公共建設上等。例如Apple推出的「Siri」,藉由語音輸入進行人機互動,自動地為使用者處理事情,像是排定會議、撥打電話及傳送訊息,甚至能與使用者對答,至今仍令人津津樂道。透過語音辨識輸入,可增加蒐集及尋找資料的效率和速度。
    According to market investigation report by AC Nielsen in 2013, about 77% of consumers are swayed by opinions and comments posted on the internet by other users.On the perspective of speech recognition development, more and more examples are applied to home appliances, smart devices and public utilities. For example, a system developed by Apple, called “Siri”. It can interact with users through speech recognition inputs, manage user schedules, conference arrangement, phone calling and message sending, or even chat with the user in ways. Now is still widely used. By speech recognition input, it can increase the efficiency and speed of information searching.
    Speech recognition techniques are mostly applied to language learning like English and Japanese pronunciation practice. Some are applied to internet of things. However, we rarely see the examples of combination with E-commerce and sentimental analysis. Thus, this study attempts to combine the speech recognition and sentimental analysis technique to build a prototype of E-commerce recommendation system. This prototype system automatically collects articles and information of products on the web, catches keywords and sentimental analyzing, and identifies the input of user with speech. Then recommends to user for the best product as a result.
    Users were asked to take a survey after experiencing a prototype system built for this study. We took feasible feedback as future reference for developing the system. The purpose of this study was to find the most expected speech recognition e-commerce mode of users. The research found that 68% of users prefer the sentiment-oriented mode to others. The average satisfaction level of system is 4.2 (out of 5). With this data, we hope to contribute more to the development and implementation in this field of study.
    Appears in Collections:[資訊管理學系暨研究所] 學位論文

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