跳到主要內容

臺灣博碩士論文加值系統

(44.201.97.0) 您好!臺灣時間:2024/04/15 10:08
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

我願授權國圖
: 
twitterline
研究生:魏晨珊
研究生(外文):Chen-Shan Wei
論文名稱:適用於多領域虛假評論之判斷模型
論文名稱(外文):Devising a cross- domain model to detect deceptive review comments
指導教授:許秉瑜許秉瑜引用關係
學位類別:碩士
校院名稱:國立中央大學
系所名稱:企業管理學系
學門:商業及管理學門
學類:企業管理學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:中文
論文頁數:53
中文關鍵詞:判斷虛假評論Stimuli-Organism-Response (S-O-R) 框架word2vec
相關次數:
  • 被引用被引用:0
  • 點閱點閱:228
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
目錄
內容
中文摘要 ................................................................. i
Abstract.................................................................. ii
目錄 ................................................................... iii
圖目錄 ................................................................... v
表目錄 .................................................................. vi
一、 緒論 ................................................................ 1
1-1 研究背景與動機 ................................................... 1
1-2 研究方法與目的 ................................................... 2
1-3 效益與貢獻 ....................................................... 4
1-4 研究架構 ......................................................... 5
二、 文獻探討 ............................................................ 6
2-1 線上評論之相關研究 ............................................... 6
2-2 辨別虛假評論之相關研究 ........................................... 7
2-3 Stimulus-Organism-Response(S-O-R)框架 ........................... 11
三、 研究方法與設計 ..................................................... 13
3-1 研究架構與步驟 .................................................. 13
3-2 SOR 類別篩選方法 ................................................. 15
3-3 方法一:SOR 與評論之特徵權重 ..................................... 16
3-4 方法二:詞向量頻繁特徵 .......................................... 16
四、 研究實驗 ........................................................... 18
4-1 實驗資料 ........................................................ 18
4-1-1 資料預處理 ................................................ 19
4-2 SOR 類別資料 ..................................................... 19
iv

4-3 評論與 SOR 詞特徵權重 ............................................ 20
4-4 實驗一:SOR 與評論之特徵權重 ..................................... 25
4-5 實驗二:詞向量頻繁特徵 .......................................... 26
五、 結論與建議 ......................................................... 32
5-1 研究結果 ....................................................... 32
5-2 未來建議 ....................................................... 32
參考文獻 ................................................................ 33
附錄一 .................................................................. 37
附錄二 .................................................................. 38
附錄三 .................................................................. 41
參考文獻
[1]. 網路資料 online resources︰iSURVEY 東方線上:消費者相信誰?線上評論如何影
響消費者線上/線下購買決策。2018 年 10 月 21 日,取自
https://www.smartm.com.tw/article/35343537cea3
[2]. 網路資料 online resources︰BuzzFeed.News, Her Amazon Purchases Are Real. The
Reviews Are Fake. November 20, 2019, 取自
https://www.buzzfeednews.com/article/nicolenguyen/her-amazon-purchases-are-real-the
reviews-are-fake
[3]. 網路資料 on line resources︰《Boston 25 News》: Shopping on Amazon, how to tell if
reviews are fake.取自 https://www.boston25news.com/news/consumer/shopping-on
amazon-how-to-tell-if-reviews-are-fake-1/694913717, November 23, 2019,
[4]. Adelaar, T., Chang, S., Lancendorfer, K. M., Lee, B., & Morimoto, M. (2003). Effects of
Media Formats on Emotions and Impulse Buying Intent. Journal of Information
Technology, 18(4), 247-266. doi:10.1080/0268396032000150799
[5]. Aslam, U., Jayabalan, M., Ilyas, H., & Suhail, A. (2019). A survey on opinion spam
detection methods. International Journal of Scientific and Technology Research, 8(9).
[6]. Banerjee, S., & Chua, A. Y. (2014). Applauses in hotel reviews: Genuine or deceptive?
Paper presented at the 2014 Science and Information Conference.
[7]. Björk, P., Bosnjak, M., & Osti, L. (2010). Atmospherics on tour operators’ websites:
Website features that stimulate emotional response. Journal of Vacation Marketing,
16(4), 283-296. doi:10.1177/1356766710372243
[8]. Boujbel, L., & d'Astous, A. (2015). Exploring the Feelings and Thoughts that
Accompany the Experience of Consumption Desires. Psychology & Marketing, 32(2),
219-231. doi:10.1002/mar.20774
34

[9]. Cagnina, L. C., & Rosso, P. (2017). Detecting deceptive opinions: intra and cross
domain classification using an efficient representation. International Journal of
Uncertainty, Fuzziness and Knowledge-Based Systems, 25(Suppl. 2), 151-174.
[10]. Chang, H.-J., Eckman, M., & Yan, R.-N. (2011). Application of the Stimulus-Organism
Response model to the retail environment: the role of hedonic motivation in impulse
buying behavior. The International Review of Retail, Distribution and Consumer
Research, 21(3), 233-249.
[11]. Chevalier, J. A., & Mayzlin, D. (2006). The effect of word of mouth on sales: Online
book reviews. Journal of marketing research, 43(3), 345-354.
[12]. Cui, G., Lui, H.-K., & Guo, X. (2012). The effect of online consumer reviews on new
product sales. International Journal of Electronic Commerce, 17(1), 39-58.
[13]. Eroglu, S. A., Machleit, K. A., & Davis, L. M. (2001). Atmospheric qualities of online
retailing: A conceptual model and implications. Journal of Business Research, 54(2),
177-184.
[14]. Ettis, S. A. (2017). Examining the relationships between online store atmospheric color,
flow experience and consumer behavior. Journal of Retailing and Consumer Services,
37, 43-55.
[15]. Feng, S., Banerjee, R., & Choi, Y. (2012). Syntactic stylometry for deception detection.
Paper presented at the Proceedings of the 50th Annual Meeting of the Association for
Computational Linguistics: Short Papers-Volume 2.
[16]. Gatautis, R., & Vaiciukynaite, E. (2013). WEBSITE ATMOSPHERE: TOWARDS
REVISITED TAXONOMY OF WEBSITE ELEMENTS. Economics & Management,
18(3).
[17]. Hennig-Thurau, T., Gwinner, K. P., Walsh, G., & Gremler, D. D. (2004). Electronic
word-of-mouth via consumer-opinion platforms: what motivates consumers to articulate
themselves on the internet? Journal of interactive marketing, 18(1), 38-52.
35

[18]. Jindal, N., & Liu, B. (2007). Analyzing and Detecting Review Spam. 547-552.
doi:10.1109/icdm.2007.68
[19]. Kavanagh, D. J., Andrade, J., & May, J. (2005). Imaginary relish and exquisite torture:
the elaborated intrusion theory of desire. Psychological review, 112(2), 446.
[20]. Kim, S., Lee, S., Park, D., & Kang, J. (2017). Constructing and evaluating a novel
crowdsourcing-based paraphrased opinion spam dataset. Paper presented at the
Proceedings of the 26th International Conference on World Wide Web.
[21]. Klaus, T., & Changchit, C. (2017). Toward an Understanding of Consumer Attitudes on
Online Review Usage. Journal of Computer Information Systems, 59(3), 277-286.
doi:10.1080/08874417.2017.1348916
[22]. Li, J., Ott, M., Cardie, C., & Hovy, E. (2014). Towards a general rule for identifying
deceptive opinion spam. Paper presented at the Proceedings of the 52nd Annual Meeting
of the Association for Computational Linguistics (Volume 1: Long Papers).
[23]. Liu, P., Xu, Z., Ai, J., & Wang, F. (2017). Identifying Indicators of Fake Reviews Based
on Spammer's Behavior Features. Paper presented at the 2017 IEEE International
Conference on Software Quality, Reliability and Security Companion (QRS-C).
[24]. Liu, W., Jing, W., & Li, Y. (2019). Incorporating feature representation into BiLSTM for
deceptive review detection. Computing, 1-15.
[25]. Menon, S., & Kahn, B. (2002). Cross-category effects of induced arousal and pleasure on
the Internet shopping experience. Journal of retailing, 78(1), 31-40.
[26]. Mukherjee, A., Venkataraman, V., Liu, B., & Glance, N. (2013). What yelp fake review
filter might be doing? Paper presented at the Seventh international AAAI conference on
weblogs and social media.
[27]. Mummalaneni, V. (2005). An empirical investigation of Web site characteristics,
consumer emotional states and on-line shopping behaviors. Journal of Business
Research, 58(4), 526-532. doi:10.1016/s0148-2963(03)00143-7
36

[28]. Oh, J., Fiorito, S. S., Cho, H., & Hofacker, C. F. (2008). Effects of design factors on store
image and expectation of merchandise quality in web-based stores. Journal of Retailing
and Consumer Services, 15(4), 237-249. doi:10.1016/j.jretconser.2007.03.004
[29]. Ott, M., Cardie, C., & Hancock, J. T. (2013). Negative deceptive opinion spam. Paper
presented at the Proceedings of the 2013 conference of the north american chapter of the
association for computational linguistics: human language technologies.
[30]. Ott, M., Choi, Y., Cardie, C., & Hancock, J. T. (2011). Finding deceptive opinion spam
by any stretch of the imagination. Paper presented at the Proceedings of the 49th annual
meeting of the association for computational linguistics: Human language technologies
volume 1.
[31]. Ren, Y., & Ji, D. (2017). Neural networks for deceptive opinion spam detection: An
empirical study. Information Sciences, 385-386, 213-224. doi:10.1016/j.ins.2017.01.015
[32]. Savage, D., Zhang, X., Yu, X., Chou, P., & Wang, Q. (2015). Detection of opinion spam
based on anomalous rating deviation. Expert Systems with Applications, 42(22), 8650
8657.
[33]. Vermeulen, I. E., & Seegers, D. (2009). Tried and tested: The impact of online hotel
reviews on consumer consideration. Tourism management, 30(1), 123-127.
電子全文 電子全文(網際網路公開日期:20250113)
連結至畢業學校之論文網頁點我開啟連結
註: 此連結為研究生畢業學校所提供,不一定有電子全文可供下載,若連結有誤,請點選上方之〝勘誤回報〞功能,我們會盡快修正,謝謝!
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top