The document discusses a seminar presentation on semantics in retrieval, highlighting ideas on making information access more natural and relevant. It details various research areas, including aspect mining, sentiment analysis, text mining, and their applications in query construction and resource retrieval. The document also outlines collaborative projects and the postgraduate team's focus on aspect/opinion mining and sentiment extraction.

1. Semantics in Retrieval by Gan Keng Hoon
on 19th October 2017 for School of Computer Sciences Staff Seminar

2. Talk Theme
What do we do.
Anything concerning Semantics in Retrieval
Why are we doing it.
Making information access more Natural and Relevant.

3. Talk Outline
Research
* Aspect Mining *Semantic-Syntax Query Model
Team
* Postgraduate
Project
* Faceted Search *Sentiment Dictionary
Collaboration

4. Research: Connecting Two Ends
Who is the most
active researcher in
the area of security
for the past three
years?
Which hotel is the
cleanest and near
to a mall at KB?
A list of hotels and
their reviews,
photos, ratings etc.
A list of experts
and their scholarly
articles etc.

5. Research: Semantics in Resources
A list of hotels and
their reviews,
photos, ratings etc.
A list of experts
and their scholarly
articles etc.
Document
Paragraph
Sentence
Phrase
Word
Object
Attribute
Value
Article
Section
Attribute
Value

6. Research: Semantics in Resources

7. Research: Area in Sentiment Analysis
Cititel
[hotel]
Room
[aspect]
RM350 [price] clean
[sentiment] 0.750
[polarity]
Object
Attribute
Value

8. Research: Area in Sentiment Analysis
ASPECT SENTIMENT SPATIAL
POLARTIY
POLARTIY
Text Mining
Information
Extraction
Contextual
Analysis

9. Research: Text Mining
Discover something that you do not know.
• Natural Language Processing
• Entity Discovery
• Relation Discovery

10. Research: Aspect Mining
A domain independent approach for identifying explicit opinionated
features and attributes that are strongly related
Published Work: Saif Addeen Alrababah, Gan Keng Hoon, Tan Tien
Ping: Mining Opinionated Product Features using WordNet
Lexicographer Files. Journal of Information Science: SAGE (2016).

11. Research: Aspect Mining
Improve relevancy of mined items
Using aspect ranking by merging sentiment analysis and TOPSIS
(Technique for Order Performance by Similarity to Ideal Solution)

12. Research: Aspect Mining
Aspect1
Aspect2 Aspectm
Freq(A)
OS(A)
Relevancy(A)

13. Research: Aspect Mining
Aspect ranking using MCDM, i.e. Topsis and Vikor.
Improvisation of MCDM.
Research Article: Saif Addeen Alrababah, Gan Keng Hoon, Tan Tien
Ping: Comparative Analysis of MCDM Methods for Product Aspect Ranking:
TOPSIS and VIKOR. International Conference on Information and
Communication Systems, ICICS 2017, 4-6 April, 2017, Irbid, Jordan.
Research Article: Saif Addeen Alrababah, Gan Keng Hoon, Tan Tien
Ping: Product Aspect Ranking using Sentiment Analysis and TOPSIS. The Third
International Conference on Information Retrieval and Knowledge
Management, CAMP 2016, 23-24 August, 2016, Melaka, Malaysia. [Best
Paper]

14. Research: Revisit Area in Sentiment Analysis
ASPECT SENTIMENT SPATIAL
POLARTIY
POLARTIY
Text Mining
Information
Extraction
Contextual
Analysis

15. Research: Big Picture
Aspect/Opinion
Mining
Aspect-Sentiment
Extraction
Polarity Scoring
Text
Mining
Information
Extraction
Contextual
Analysis
Text
Classification
Aspect/Review
Classification
Knowledge
Representation
Semantics in Resources Semantics in Query
Sentiment Analysis Scientific Articles
Search
Facet-Value
Extraction
Article
Classification
Semantic Relation
Extraction
Research Outcome
Examples
MCDM-based
Aspect Ranking
Framework
Sem-Syn Query
Model
A-S Extraction Rule
Content
Annotation
Domain A-S Lex
Unstructured to
Structured
Natural Language
Query Interface
Structured Query
Construction
Unstructured-
Structured Data
Integration

16. Research: Semantics in Query
Interpreting keywords query
Construct structured query
Research Article: Gan Keng Hoon, Phang Keat Keong: A query transformation
framework for automated structured query construction in structured retrieval
environment. Journal of Information Science 40(2): 249-263, SAGE (2014).
Research Article: Gan Keng Hoon, Phang Keat Keong: Finding Target and Constraint
Concepts for XML Query Construction. International Journal of Web Information
Systems 11(4): Emerald Insight (2015)

17. Research: Semantics in Query
Flexibility to constructing
different types of structured query
Figure showing the semantic
representation
that can be constructed into NEXI
query.
Research Article: Gan Keng Hoon,
Phang Keat Keong: A Semantic-Syntax
Model for XML Query Construction.
International Journal of Web
Information Systems 13(2): 155-172,
Emerald Insight (2017).

19. Team: Postgraduate
Text
Mining
Information
Extraction
Contextual
Analysis
Text
Classification
Knowledge
Representation
Semantics in Resources Semantics in Query
Sentiment Analysis
Content
Annotation
TK (Msc Mix)
Identification of Supporting Sentence
for Comparative Opinion Mining
Rizvana (Msc Mix)
Rule-based Aspect-
Sentiment Pair Extraction
Saif (Phd)
Aspect-based Sentiment Analysis
using MCDM
Aspect/Opinion
Mining
Aspect-Sentiment
Extraction
Polarity Scoring
Aspect/Review
Classification
Krol (Msc Mix)
Polarity Detection for
Contrastive/Conditional
Sentence
Erum (Phd)
Spatial and Sentiment
Extraction for POI Graph
Issa (Phd)
Event-based Short Text
Classification

20. Team: Big Picture of Postgraduate
Aspect/Opinion
Mining
Aspect-Sentiment
Extraction
Polarity Scoring
Text
Mining
Information
Extraction
Contextual
Analysis
Text
Classification
Aspect/Review
Classification
Knowledge
Representation
Semantics in Resources Semantics in Query
Sentiment Analysis Scientific Articles
Search
Facet-Value
Extraction
Article
Classification
Semantic Relation
Extraction
Research Outcome
Examples
MCDM-based
Aspect Ranking
Framework
Intermediate
Query Model
A-S Extraction Rule
Content
Annotation
Domain A-S Lex
Unstructured to
Structured
Natural Language
Query Interface
Structured Query
Construction
Unstructured-
Structured Data
Integration

21. Project: Linking Research and Application
Expert Search
http://ir.cs.usm.my/exsearch3/
SummaRev: Reviews Analysis and Summarization
http://ir.cs.usm.my/summarev4hotel/admin/sentiment_dictionary.php

22. Collaboration/Discussion
Making research industry relevant
Research application prototyping (FYP != programmer?)
My direction vs your direction
Collaboration setting
THANK YOU
Visit our work at
ir.cs.usm.my