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Literature Review
Recommender Systems in E-Commerce[1]
• Help their customers find products
to purchase.
• Ratings,comments,likes.
• Recommend the items that suits
the customer.
• Amazon
 Customer comments
 Book matcher
Recommender Systems in E-Commerce[1]
(cont.)
• Amazon
 Customer comments
 Book matcher
• E bay
 Feedback profile
Personalized Academic Research Paper
Recommendation System[5]
•
•
•
•
•

Key based search or browsing.
Recommend the related articles.
Bag of words model
Heuristic
Naïve Recommender
TOWARDS AUTOMATIC DETECTION OF
SENTIMENTS IN CUSTOMER REVIEWS [2]
• Automatic extraction of human
opininons from web documents.
• Data collection,data
preparation,feature
extraction,sentiment classification,
• Online review,personal blogs.
• Provide the products basis of feedback
A Feature Extraction Process for
Sentiment Analysis of Opinions on Services [3]
• E-commerce is popular
• What matter system
• Improve the quality of service
Semantic Tagging at the Meaning Level[4]
•
•
•
•

Positive,negative and neutral
Net overlap score
Valance shifters
Improve accuracy
Sentiment Polarity Tagging Based on Nearby
Words[6]
• Extracting the adjectives and adverbs
• Create positive and nagative wordlist
• Polarity tagging of the text
Selecting Bi-Tags for Sentiment Analysis of
Text[7]
•
•
•
•
•

Determine the overall sentiment.
Apply part of switch tag
Extraction of bi-grams
Predict overall sentiment orientation
Prediction
References
1) J. Ben Schafer, Joseph Konstan, John Riedl.
GroupLens Research Project
Department of Computer Science and Engineering
University of Minnesota
2) ANSHU JAIN, DR. SURESH JAIN,MRS. PRAGYA SHUKLA,HITESH BANDIYA
International Journal of Information Sciences and Techniques (IJIST)
Vol.2, No.4, July 2012
3) Henrique Siqueira and Flavia Barros
Centro de Informatica (CIn) - Universidade Federal de Pernambuco (UFPE)
Recife - PE – Brazil
4) Alina Andreevskaia, Sabine Bergler, CLaC Laboratory
Computer Science and Software Eng.
1455 de Maisonneuve Blvd. West
5) Joonseok Lee, Kisung Lee, Jennifer G. Kim
Georgia Institute of Technology
Atlanta, GA 30318
6) Joonseok Lee, Kisung Lee, Jennifer G. Kim, University of Twente, The
Netherlands
Faculty of Electrical Engineering, Mathematics and Computer Science
References
7)Rahman Mukras, Nirmalie Wiratunga, and Robert Lothian
School of Computing
The Robert Gordon University
St Andrew Street
Aberdeen UK, AB25 1HG
Thank you

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Literature review

  • 2. Recommender Systems in E-Commerce[1] • Help their customers find products to purchase. • Ratings,comments,likes. • Recommend the items that suits the customer. • Amazon  Customer comments  Book matcher
  • 3. Recommender Systems in E-Commerce[1] (cont.) • Amazon  Customer comments  Book matcher • E bay  Feedback profile
  • 4. Personalized Academic Research Paper Recommendation System[5] • • • • • Key based search or browsing. Recommend the related articles. Bag of words model Heuristic Naïve Recommender
  • 5. TOWARDS AUTOMATIC DETECTION OF SENTIMENTS IN CUSTOMER REVIEWS [2] • Automatic extraction of human opininons from web documents. • Data collection,data preparation,feature extraction,sentiment classification, • Online review,personal blogs. • Provide the products basis of feedback
  • 6. A Feature Extraction Process for Sentiment Analysis of Opinions on Services [3] • E-commerce is popular • What matter system • Improve the quality of service
  • 7. Semantic Tagging at the Meaning Level[4] • • • • Positive,negative and neutral Net overlap score Valance shifters Improve accuracy
  • 8. Sentiment Polarity Tagging Based on Nearby Words[6] • Extracting the adjectives and adverbs • Create positive and nagative wordlist • Polarity tagging of the text
  • 9. Selecting Bi-Tags for Sentiment Analysis of Text[7] • • • • • Determine the overall sentiment. Apply part of switch tag Extraction of bi-grams Predict overall sentiment orientation Prediction
  • 10. References 1) J. Ben Schafer, Joseph Konstan, John Riedl. GroupLens Research Project Department of Computer Science and Engineering University of Minnesota 2) ANSHU JAIN, DR. SURESH JAIN,MRS. PRAGYA SHUKLA,HITESH BANDIYA International Journal of Information Sciences and Techniques (IJIST) Vol.2, No.4, July 2012 3) Henrique Siqueira and Flavia Barros Centro de Informatica (CIn) - Universidade Federal de Pernambuco (UFPE) Recife - PE – Brazil 4) Alina Andreevskaia, Sabine Bergler, CLaC Laboratory Computer Science and Software Eng. 1455 de Maisonneuve Blvd. West 5) Joonseok Lee, Kisung Lee, Jennifer G. Kim Georgia Institute of Technology Atlanta, GA 30318 6) Joonseok Lee, Kisung Lee, Jennifer G. Kim, University of Twente, The Netherlands Faculty of Electrical Engineering, Mathematics and Computer Science
  • 11. References 7)Rahman Mukras, Nirmalie Wiratunga, and Robert Lothian School of Computing The Robert Gordon University St Andrew Street Aberdeen UK, AB25 1HG