1. Aspect-based sentiment analysis
topic 1
Aspect-based sentiment analysis
9/19/2018
Huy V. Nguyen

2. Outline Sentiment analysis problem Aspect-based sentiment analysis
Data
The dominance of online reviews
Computational models
Two-stage approaches
Joint topic-sentiment models
Evaluation
Achievements and Challenges
9/19/2018
Huy V. Nguyen

3. Why sentiment analysis
Customers: need peer opinions to make purchase decisions
Business providers:
need customers’ opinions to improve product
need to track opinions to make marketing decisions
Social researchers: want to know people’s reactions about social events
Government: wants to know people’s reactions to a new policy
Psychology, education, etc.
9/19/2018
Huy V. Nguyen

4. Sentiment analysis problem
(Sentiment Analysis and Opinion Mining book, Liu 2012)
Definition: given an opinion text, discover all opinion quintuples <entity, aspect, sentiment, holder, time>
Tasks: extract, identify, or otherwise characterize the sentiment content of a text unit
►Not all elements (of the quintuple) are (necessarily) addressed at all levels of granularity
Document/sentence: sentiment classification
Word: sentiment lexicon generation
9/19/2018
Huy V. Nguyen

5. Aspect-based sentiment analysis
(Liu 2012)
The (almost) complete variant of the problem
(Actually) discovers how people think about what
Two tasks studied extensively:
Aspect extraction
Aspect sentiment classification
►Particular genres make the other tasks (more) easy
Reviews v. blogs/discussions
9/19/2018
Huy V. Nguyen

6. Data News articles Blogs Online reviews Hotel/restaurant reviews
Amazon product reviews
Ebay seller reviews
IMDB reviews
Online reviews have been studied extensively
Limited amount of work on new articles
9/19/2018
Huy V. Nguyen

7. (2) Advantages of reviews
Product and service reviews
Known entity, opinion holder, time
Metadata
Well categorized/defined aspects
Product specifications, service standards
Highly focused and opinion rich
Little irrelevant information
►Reduce computational difficulty
9/19/2018
Huy V. Nguyen

8. (3) Difficulties of news articles
Identifies the targets
Aspect may not be a proper term
Good/bad news content v. good/bad expressed sentiment
Different views: author, reader, and opinion holder
►Little amount of work has been done in this genre

9. Computational models Two-stage approaches Aspect extraction
Aspect sentiment classification
Jointly modeling aspects and opinions
Inherent relationship between sentiments/opinions and their targets (aspects/topics)
►How much simultaneously is called jointly
Double propagation is not jointly
9/19/2018
Huy V. Nguyen

10. Two-stage approaches Aspect extraction
Frequent item mining (Hu & Liu 2004, Popescu & Etzioni 2005)
In relation with opinions (Zhuang et al. 2006, Wu et al. 2009, Qiu et al. 2009)
Topic models (Brody & Elhadad 2010)
Aspect sentiment classification
Lexicon-based starting
Sentiment propagation
Contextual polarity

11. (2) Pros and Cons Rule-based approaches work well
Simple sentence structure (concise writing)
Sentiment and features expressed clearly (strong opinions)
Shallow syntactic parsing (less prone to writing errors)
Sentiment lexicon helps as a good start, but contextual polarity needs to be addressed
Rule-based (Ding et al. 2008)
Relaxation labeling (Popescu & Etzioni 2005)
►Rule-based approaches were tailored for review data
? Blogs/discussions, news articles
Possible solution: Topic models + sentiment models

12. Joint topic-sentiment models
Topic sentiment mixture (Mei et al. 2007)
Topic models, positive/negative sentiment models
Multi-aspect sentiment model (Titov & McDonald 2008)
Multi-grain LDA + aspect rating MaxEnt classifier
Joint topic sentiment model (Lin & He 2009)
Each document is associated with number of topic distributions (= number of sentiment labels)
Hybrid joint topic sentiment model (Zhao et al. 2010)
General+specific aspect/opinion models
MaxEnt component to classify topic and opinion words
Unified aspect sentiment model (Jo & Oh 2011)
Word is generated from unified language model, has aspect and sentiment attributes

13. (2) Topic v. sentiment
Separates topic and opinion words using model priors
Positive/negative sentences as training data for sentiment models (Mei et al. 2007)
Subjectivity lexicons to assign sentiment label to words (Lin & He 2009), to encode asymmetric prior sentiment information (Jo & Oh 2011)
Supervised models to discriminate topic and opinion words (Zhao et al. 2010)
►Supervision is needed to help discriminate
Aspect v. sentiment
Positive v. negative opinions

14. (3) Motivation and issues
First attempts of using topic-model
Easily evaluate models’ output with review data
Less domain specific (unsupervised)
Less prone to spelling/grammatical errors
Issues
Not clear separation between topic words and opinion words
Not easily compare to rule-based approaches (how)
Not yet adapt to other domain (politics/social events)
Or other different genres (news, online discussions)

15. Evaluation Human-labeled reviews Explicit and implicit aspects
Aspect-sentiment pairs
Sentence opinion
User-generated ratings (aspect ratings v. overall rating)
Intrinsic evaluation
Precision and recall of aspect extraction
Aspect sentiment accuracy
Aspect rating correlation with true labels (Lu et al. 2009, Wang et al. 2010)
Aspect rating regression (Wang et al. 2010)

16. (2) Evaluation Evaluates topic-sentiment models is less quantitatively
Post-hoc: human-judged aspect extraction with associated opinions
Mapping accuracy from sentence (local) topics to rated aspects (Titov & McDonald 2008, Brody & Elhadad 2010, Zhao et al. 2010)
Document sentiment classification (Lin & He 2009, Jo & Oh 2011)
Extrinsic evaluation
? Summarization

17. Achievements and Challenges
Aspect-sentiment summarization
Opinion Observer (Liu et al. 2005)
OPINE (Popescu & Etzioni 2005)
Topic life cycles and sentiment dynamics (Mei et al. 2007)
Predicting sale performance (Liu et al. 2007)
News article challenges
Not well-defined targets/aspects
Diverse and sparse topics
Implicit opinions
Fine-grained opinion unit extraction
Frequent item mining does not work, topic models either

18. Conclusions
Review data has been studied extensively Rule-based approaches are simple but perform well Topic-sentiment models are proven promising News articles are challenging