臺灣博碩士論文加值系統

在COVID-19疫情的影響下，讓電子商務網站的銷售量大幅增加。許多買家會透過文字評價和星級評價來分享對商品的看法，這些分享對潛在消費者的購買決定帶來了很大的影響，因此賣家對於買家評價更加的重視，並引起許多賣家利用獎勵的方式吸引買家去評價。過去有研究指出，星級可能無法完全捕捉評價中的極性訊息，因此本研究應用情感分析於蝦皮網站的買家評價，並利用評價內容將評價資料進行情感指數分析，希望能找出星級評價和評價內容不對等的買家評價，並且探討不對等買家評價對情感極性分類是否有影響。
本研究首先爬取蝦皮網站的買家評價作為實驗資料，並且將爬取的資料做前處理，接著進行星級情感極性分類評估及情感指數極性分類評估，最後再進行情感指數極性分類結果比較。本研究的情感極性分類評估有Word2Vec與Doc2Vec兩種向量轉換方法，並使用SVM、MLP、CNN、LSTM及Bi-LSTM，總共5種分類方法來進行情感極性分類。實驗結果顯示，在星級情感極性分類為分三類和分兩類下，向量轉換不管為w2v或d2v的分類結果差距皆不大；在向量轉換為w2v和d2v的情況下，星級情感極性分類為分兩類皆比分三類好；接著在情感指數極性分析中確實找出了星級評價與情感指數評價分類不對等的評價資料，而在情感指數極性分類評估下，向量轉換w2v與d2v的結果也差距不大；最後星級情感與情感指數極性分類結果比較，不管在向量轉換為w2v或d2v的情況下，情感指數極性分類(不含不對等評價資料)皆比星級情感極性分類(含不對等評價資料)好，因此不對等買家評價對情感極性分類是有影響的。

The impact of the COVID-19 outbreak has led to a significant increase in sales on e-commerce websites. Many purchasers share their evaluations about products through text and starred reviews, and these shares can have a significant impact on a potential consumer's purchasing decision. As a result, sellers are paying more attention to purchaser’s reviews, and many sellers are using incentives to attract purchasers to review. In the past research, the star rating may not fully capture the polarity of the evaluations, so this study applied sentiment analysis to the purchaser’s evaluation of the Shopee website, and used the evaluation content to analyze the data for sentiment index, hoping to identify the purchaser’s evaluation with unequal star ratings and evaluation content, and to explore whether unequal purchaser’s evaluation has an effect on the classification .
In this study, we first crawled the purchaser reviews from the Shopee website as experimental data, and pre-processed the crawled data, then evaluated the star level sentiment polarity classification and sentiment index polarity classification, and finally compared the sentiment index polarity classification results. Emotional polarity classification is evaluated by two vector conversion methods, Word2Vec and Doc2Vec, and a total of five classification methods, SVM, MLP, CNN, LSTM, and Bi-LSTM. The experimental results showed that the difference between the classification of starred affective polarity into three categories and two categories was not significant for either w2v or d2v, and the classification of starred affective polarity into two categories was better than three categories for both w2v and d2v. Then, in the sentiment index polarity analysis, the data of the star rating and the sentiment index evaluation classification were indeed found to be unequal, and the results of the vector transformation w2v and d2v under the sentiment index polarity classification evaluation were not very different. Finally, when comparing the results of the star sentiment and sentiment index polarity classification, the sentiment index polarity classification (without unequal evaluation data) is better than the star sentiment (with unequal evaluation data) regardless of whether the vector is transformed to w2v or d2v, so unequal purchaser evaluation has an effect on the sentiment polarity classification.

摘要 i
ABSTRACT ii
目錄 iii
表目錄 v
圖目錄 vi
一、緒論 1
1.1 研究背景與動機 1
1.2 研究目的 4
1.3 論文架構 4
二、文獻探討 5
2.1 網路口碑 5
2.2 情感分析 5
2.3 機器學習 6
2.4 深度學習 7
2.4.1 多層感知器 8
2.4.2 卷積神經網路 8
2.4.3 長短期記憶網路 9
2.4.4 雙向長短期記憶網路 11
2.5 評估指標 11
2.6 相關研究 13
三、研究方法 14
3.1 系統架構 14
3.2 資料蒐集 16
3.3 資料前處理 17
3.4 星級情感極性分類評估 19
3.5 情感指數極性分類評估 20
3.6 星級情感與情感指數極性分類結果比較 23
四、系統實驗 24
4.1 實驗環境 24
4.2 實驗對象 24
4.3 實驗結果 25
4.3.1 資料蒐集 25
4.3.2 資料前處理 28
4.3.3 星級情感極性分類評估 30
4.3.4 情感指數極性分類評估 36
4.3.5 星級情感與情感指數極性分類結果比較 38
五、結論 42
參考文獻 44

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