The document discusses Hardik Dalal's research on aspect-level sentiment analysis of customer reviews, used to extract product feature and sentiment word pairs. It highlights the importance of customer reviews in purchasing decisions and details methods such as the double propagation algorithm for analyzing sentiments based on grammatical relations. The work concludes with the effectiveness of the proposed approach and its potential applications in text mining.

1. Hardik Dalal
Faculty of Computer Science,
Dalhousie University
Aspect-level sentiment
analysis of customer reviews
using Double Propagation
31st March, 2016

2. Contents
 Introduction to application scenario
 Problem statement
 Objective
 Approach
 Data Gathering and Preparation
 Dataset description
 Data cleaning
 Implementation
 Double propagation algorithm
 Filter frequent unnecessary words
 Classify sentiment words
 Evaluation
 Results
 Conclusion

3. Introduction to application scenario
 Web 2.0 Summit in 2004, Tim O’Reilly emphasized on user-generated
content and its usage
 Allows people to connect globally in the world of Web
 Explosion of digital content
 Blogs and Instant Messaging (IM) for starters
 Followed by MySpace, Facebook, Twitter, Wikipedia, YouTube and so
on
 Alongside the rise of customer-generated review websites; Yelp and
Epinions
 And of course the E-commerce giant; Amazon

4. Introduction to application scenario (cont.)
 Customer reviews is a rich source of information for other customers
and sellers
 “78% of Americans Read Online Reviews Before Making a Purchase
Decision” by YouGov
 “97% of consumers found online review accurate” by ComScore
 “92% users have more confidence in online reviews then sales clerk
and other sources” by WSJ

6. Problem statement
 Which information can help purchasing a product based on reviews
easier?
 Read all the reviews?
 A preferable choice is to evaluate products based on its features’
rating not just overall rating like traditional 5-star rating

7. Objective
 Extract all product feature and sentiment word pairs from reviews
 Summarize them
3
-1
1
2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
3
3.5
Cost Quality Speed Customer service

8. Approach
 Based on grammatical relations and Part-Of-Speech (POS) tagging
 Exploit relations between aspect terms and opinion words
 How?
 Aspect terms are mostly nouns (NN or NNS)
 Opinion words are mostly adjectives (JJ, JJR, JJS)
 Relations between them:
 Adjectival modifier (amod)
 Direct object (dobj)
 Nominal subject (nsubj)
 Conjunction (conj)
 Has_a

9. An example
“These/DT, speakers/NNS, are/VBP,
incredibly/RB, amazing/JJ”
nsubj(amazing-5,
speakers-2)
Aspect => speakers
Opinion => amazing
(positive)
Noun
Adjective

10. Data Gathering
 (Liu & Liu, 2015) freely available for opinion mining/sentiment
analysis and used in 24th International Joint Conference on Artificial
Intelligence (IJCAI) 2015
 Approximately 2000 reviews from 3 products fetched from Amazon
 XML format, manually annotated

11. Data Cleaning
 Removed URLs in some of the reviews
 Missing start or end tags
 Symbols such as '&' had to be replaced by & due to Java XML
parser restrictions

12. Double
Propagation
Algorithm
Extract opinions
using aspects
Extract aspects
using existing
aspects
Extract opinions
using existing
opinions
Extract aspects
using opinions
Aspect list
• …
• …
• …
Opinion list
• good
• Bad
• …

13. Filter frequent unnecessary words
 Some words appear too frequently and do not carry value in further
stages of mining process for example, nothing, someone, anybody
 A common stop word list built using TF-IDF

14. Classify Opinion words
 Each word in SentiWordNet have a Positive and a Negative score
 Used score to determine polarity of opinion words
 Summation of all sentiment scores associated to an aspect is its final
score
Positive score
Negative score

15. Evaluation
 Prepare gold standard from annotated data
 Calculate precision and recall using gold standard and results

16. Conclusion
 Grammatical relations are vital in understanding NLP problems
 Double propagation can be used for several other text mining
problems

17. References
 Liu, Q., Liu, B., (2015). Annotated: More Customer Review Datasets (3
products) [Dataset]. Retrieved from
www.cs.uic.edu/~liub/FBS/CustomerReviews-3-domains.rar.
 Garcıa-Pablos, A., Cuadros, M., Gaines, S., & Rigau, G. (2014). V3:
Unsupervised Generation of Domain Aspect Terms for Aspect Based
Sentiment Analysis. In SemEval 2014, 833.
 SentiWordNet, from http://sentiwordnet.isti.cnr.it/

18. Thank you