評論內容屬性與評論幫助性關係之研究

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姓名

魏怡如(Yi-Ru Wei) 查詢紙本館藏

畢業系所

企業管理學系

論文名稱

評論內容屬性與評論幫助性關係之研究
(Important factors that affect perceived reviews helpfulness)

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至系統瀏覽論文 (2024-8-31以後開放)

摘要(中)

隨著科技的發展以及網路購物的普及，線上評論對於電商領域可謂說是越
來越重要，有學者認為評論對於消費者的影響甚至會比賣家提供的資訊還大。
評論不僅可以讓消費者了解到產品的品質，也可以提升消費者對於平台的黏著
度。
過去有文獻探討評論內容的廣度、情緒對於評論幫助性的影響，本研究更
深入探討當消費者閱讀經驗性商品與搜尋性商品評論時，覺得有幫助的評論是
否會不一樣。本篇研究使用文字探勘的手法分析評論的情緒以及評論的廣度，
以及分析消費者閱讀不同產品類別時吸收資訊的方式，進而提供平台營運上的
建議。

摘要(英)

Online reviews have become more and more important in Ecommerce industry and are thought to be more helpful than seller generated information. Moreover, reviews have been shown to improve customers’ perception of the product quality, and can also increase customers’ stickiness to the website.
With these benefits of review, understanding the important attributes of a helpful review let the Ecommerce be able to identify and promote more informative content, to develop encouraging content, and ultimately increase customers’ stickiness and user satisfaction to the website.
Previous researches have analyzed how review breadth and review sentiment affects perceived review helpfulness; moreover, there are also some researches mention the differences of information processing method between search goods and experience when reading reviews.
Therefore, the study utilizes text mining technique to analyze how product types affect the perceived review helpfulness. Positive and negative emotion is calculated using VADER, and this study also conduct BERTopics to calculate the topic distribution for each review. Finally, this paper discover the different information processing method between search goods and experience goods, and hence give the suggestion to Ecommerce platform base on the finding.

關鍵字(中)

★ 情感分析
★ 文字探勘
★ 經驗性商品
★ 搜尋性商品

關鍵字(英)

★ sentiment analysis
★ text mining
★ search goods
★ experience goods

論文目次

中文摘要 .................................................... I ABSTRACT................................................... II 目錄 TABLE OF CONTENTS..................................... IV 表目錄 LIST OF TABLES........................................ V 圖目錄 LIST OF FIGURES..................................... VI
I.INTRODUCTION ............................................ 1
1.1 BACKGROUND AND MOTIVATION.............................. 1
LITERATURE REVIEW.......................................... 3
2.1 NEGATIVITY BIAS ....................................... 3 2.2 ANGER REVIEW .......................................... 3 2.3 PRODUCT TYPES: EXPERIENCE GOODS AND SEARCH GOODS....... 4
2.4 THEORETICAL FRAMEWORK ................................. 5
III. EXPERIMENT ........................................... 6
3.1 DATA COLLECTION ....................................... 6 3.2 DATA EXTRACTION........................................ 7 3.2.1 Data extraction based on product types............... 7
3.2.2 Filtering out reviews with at least one vote ........ 8 3.3 TEXT PRE-PROCESSING ................................... 9
3.4 VARIABLE CONSTRUCTION ................................. 10
3.4.1 Sentiment Analysis................................... 10
3.4.2 Topic Comprehensiveness ............................. 12
3.4.3 Detailed Explanation................................. 16
3.4.4 Extreme Star Rating ................................. 18
3.4.5 Number of Words...................................... 18
3.4.6 Number of Days ...................................... 19
3.4.7 Review Helpfulness .................................. 19
IV. DATA ANALYSIS AND RESULT .............................. 20
4.1 VARIABLE DESCRIPTION .................................. 20
4.2 CORRELATION MATRIX .................................... 22
4.3 RESULT ................................................ 24
4.3.1 The general effect of OLS result .................... 25
4.3.2 OLS analysis for Product Types ...................... 25
V. CONCLUSION ............................................. 27
5.1 THEORETICAL IMPLICATIONS .............................. 27
5.2 PRACTICAL IMPLICATIONS ................................ 27
VI. REFERENCE ............................................. 28

參考文獻

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指導教授

陳炫碩(Shiuann-Shuoh Chen)

審核日期

2022-9-5

推文