Effective Opinion Words Extraction for Food Reviews Classification

被引：0
|
作者：
Phuc Quang Tran [1 ]
Ngoan Thanh Trieu [2 ]
Nguyen Vu Dao [3 ]
Hai Thanh Nguyen [2 ]
Hiep Xuan Huynh [2 ]
机构：
[1] Peoples Police Coll II, Dept Foreign Languages & Informat, Hcm City, Vietnam
[2] Can Tho Univ, Coll Informat & Commun Technol, Can Tho, Vietnam
[3] Can Tho Univ, Dept Phys Educ, Can Tho, Vietnam
来源：
INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS | 2020年 / 11卷 / 07期
关键词：
Review classification; opinion words; machine learning; important features; Amazon;
D O I：
10.14569/IJACSA.2020.0110755
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Opinion mining (known as sentiment analysis or emotion Artificial Intelligence) holds important roles for e-commerce and benefits to numerous business and organizations. It studies the use of natural language processing, text analysis, computational linguistics, and biometrics to provide us business valuable insights into how people feel about our product brand or service. In this study, we investigate reviews from Amazon Fine Food Reviews dataset including about 500,000 reviews and propose a method to transform reviews into features including Opinion Words which then can be used for reviews classification tasks by machine learning algorithms. From the obtained results, we evaluate useful Opinion Words which can be informative to identify whether the review is positive or negative.
引用
收藏
页码：421 / 426
页数：6
相关论文
共 50 条
  • [1] Collective Extraction for Opinion Targets and Opinion Words from Online Reviews
    Jiang, Xiangxiang
    Lin, Yuming
    Li, You
    Zhang, Jingwei
    [J]. 2016 7TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND BIG DATA (CCBD), 2016, : 367 - 373
  • [2] Opinion Extraction & Classification of Reviews from Web Documents
    Shandilya, Shishir K.
    Jain, Suresh
    [J]. 2009 IEEE INTERNATIONAL ADVANCE COMPUTING CONFERENCE, VOLS 1-3, 2009, : 924 - 927
  • [3] Co-extraction of Opinion Targets and Opinion Words from Online Reviews Based on Opinion and Semantic Relations
    Mathapati, Savitha
    Shreelekha, B. S.
    Tanuja, R.
    Manjula, S. H.
    Venugopal, K. R.
    [J]. 2018 FIFTEENTH INTERNATIONAL CONFERENCE ON WIRELESS AND OPTICAL COMMUNICATIONS NETWORKS (WOCN), 2018,
  • [4] Opinion Words Extraction and Sentiment Classification with Character Based Embedding
    Jiang, Kun
    Zhang, Yueguo
    Yao, Lihong
    Jiang, Xinghao
    Sun, Tanfeng
    [J]. PROCEEDINGS OF 2019 IEEE 13TH INTERNATIONAL CONFERENCE ON ANTI-COUNTERFEITING, SECURITY, AND IDENTIFICATION (IEEE-ASID'2019), 2019, : 136 - 141
  • [5] Mining opinion targets and opinion words from online reviews
    Tran T.K.
    Phan T.T.
    [J]. International Journal of Information Technology, 2017, 9 (3) : 239 - 249
  • [6] A Joint Model for Target-Oriented Opinion Words Extraction and Sentiment Classification
    Dai, Chenyang
    Shen, Bo
    Yan, Fengxiao
    [J]. JOURNAL OF INFORMATION & KNOWLEDGE MANAGEMENT, 2023, 22 (03)
  • [7] OPINION EXTRACTION FROM CUSTOMER REVIEWS
    Loh, Han Tong
    Sun, Jie
    Wang, Jingjing
    Lu, Wen Feng
    [J]. ASME INTERNATIONAL DESIGN ENGINEERING TECHNICAL CONFERENCES AND COMPUTERS AND INFORMATION IN ENGINEERING CONFERENCE, PROCEEDINGS, VOL 2, PTS A AND B, 2010, : 753 - 758
  • [8] Opinion classification for Chinese movie reviews
    Lin, B
    Lu, T
    Ye, QA
    [J]. Proceedings of the 2005 International Conference on Management Science & Engineering (12th), Vols 1- 3, 2005, : 205 - 208
  • [9] An Approach for Detecting Polarity of Online Product Reviews Based on Opinion Target and Opinion Words from Reviews
    Bhongade, Bhagyashree G.
    Zadgaonkar, Ashwini V.
    [J]. HELIX, 2018, 8 (05): : 3874 - 3878
  • [10] Probabilistic extraction of strings of words related to an opinion
    Lavalley, Remi
    Clavel, Chloe
    Bellot, Patrice
    [J]. TRAITEMENT AUTOMATIQUE DES LANGUES, 2010, 51 (03): : 101 - 130
12345