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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/84071

    Title: 情感觀點分析之評論應用;Review Application of Aspect Based Sentiment Analysis
    Authors: 邱政;Chiu, Cheng
    Contributors: 資訊管理學系
    Keywords: 自然語言處理;情感分析;情感觀點分析;NLP;sentiment analysis;aspect based sentiment analysis
    Date: 2020-07-29
    Issue Date: 2020-09-02 18:00:43 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 情感分析屬於自然語言處理領域中的分支,主要目的為判斷評論者對於產品或服務的回饋是屬於正向還是負向情感。自從社群網路的崛起,越來越多人願意在平台上分享產品使用心得或是服務經驗,成為決策者新的參考依據。餐廳業者也設計線上問卷來收集顧客的用餐滿意度,藉此改善各缺點以提升顧客回流率。隨著評論的增加,很難以人工的方法瀏覽評論,需透過電腦取代人力來取得寶貴的評論資訊。目前情感分析的應用大多屬於文檔級,依據全部的評論預測出一個情感極性,忽略了評論可能包含對多個觀點的意見及情感。
    ;Sentiment analysis belongs to a branch in the field of natural language processing. The main purpose is to determine whether the feedback of the reviewer to the product or service is positive or negative emotion. Since the rise of social networking, more and more people are willing to share product experience or service experience on the platform, becoming a new reference for decision makers. Restaurants have also designed online questionnaires to collect customers′ dining satisfaction, thereby improving various shortcomings and increasing customer return rates. With the increase of reviews, it is difficult to browse the entire review by manual methods. Therefore, it is necessary to use computers to replace manpower to obtain valuable comment information. At present, most of the applications of sentiment analysis are document level which the sentiment polarity is predicted based on all the comments, ignoring that the comments may contain opinions and sentiments on multiple aspects.
    In order to allow users to view the sentiment polarity of each aspect of view in the review and understand the advantages and disadvantages of the four aspects of the restaurant′s dining experience, this study uses aspect based sentiment analysis for restaurant reviews. First, use word embedding to convert the word of sentences and aspects into word vectors as the input source of computer calculation. Second, divide the aspect word into five categories: food, price, service, atmosphere and anecdotes. Third, use Attention-based LSTM with Aspect Embedding model judges the comment sentence as positive, negative or neutral emotion according to the given five categories label. The results show that the prediction accuracy of the model is as high as 84%. A restaurant review classification system can be built based on the results of aspect based sentiment analysis. The application data set can be divided into food, price, service, atmosphere and other four aspects and anecdotes. Reviews, based on the classification results of emotional polarity, found the restaurant’s customer satisfaction from different viewpoints. Emotional opinion analysis is used in restaurant reviews to classify the ever-increasing reviews, so that restaurant owners and customers can quickly find useful information.
    Appears in Collections:[資訊管理研究所] 博碩士論文

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