PhD Thesis Structure and Content
1. Working Titles of the Research Project:
“Sentiment Analysis Techniques for Enhancing eParticipation in Law Making Process”
2. Supervisors
Prof. Monica Palmirani
Prof. Fabio Vitali
Prof. Leon van der Torre
3. Abstract
Sentiment analysis is an active study of text data mining which deals with computational
treatment of subjective information such as opinions, feeling, and attitudes articulated in
textual format. There have been an immense interests in sentiment analysis as public opinions
are now widely available over the internet. Researchers have long explored and proposed an
extensive variety of tools and techniques to analyse textual words and phrases starting from
keywords spotting, statistical methods, syntactic dependencies, and semantic relations. Yet,
the complex nature of natural language still brings many challenges and issues to be
addressed, thus open the opportunity for a new research and refinement.
This research focus to use sentiment analysis techniques, including ontology and NLP, for
detecting, classifying and visualizing opinions coming from citizens and members of
parliament in order to help the law-making process. The research was inspired by the
intertwining concepts of deliberative democracy and user-created content on Web 2.0, which
has facilitated public participation in discussion and debate among citizens regarding political
decisions (procedure, action, or policy) throughout online discussion platforms and social
media.
The research limits itself in implementing sentiment analysis of opinions in the legal domain,
especially linked to the online debate around legal drafting process of a bill. The research
seeks to establish a set of mechanism which capable to detect the real support argument in
legal drafting analysis, then to summarize the gathered opinions and to discover its patterns.
Finally, the research explore the visualization of the discovered patterns with respect to the
original source of the bill.
The proposed design offers a methodical approach for generating deep knowledge about
users’ emotions, as a basic requirement to learn users’ opinion based on a conceptual
representation of both legal documents and users’ emotions. The research design explore the
possibility to generate a fine-grained sentiment analysis techniques through the combination
of sub-sentence level analysis, frequency measure, sentence orientation measure, and
multiple lexicons.
4. Thesis Structure
1. Introduction
1.1. Background and Motivation
1.1.1. Deliberative Democracy and Citizen Empowerment
1.1.2. Online Discussion Forum and Opinions
1.1.3. Public Opinions and Sentiment Analysis
1.2. Problem Statement
1.3. Research Context
1.4. Research Goal and Questions
1.5. Research Contribution
1.6. Thesis Organization
2. State of the Art
2.1. Introduction
2.2. History and Background Overview
2.3. Theoretical Works
2.4. Empirical Works
2.5. Discussion
2.6. Conclusion
3. Sentiment Measure and Opinion Construction
3.1. Classification of Sentiment Measure
3.2. Sentiment Measure in Practice
3.3. Evaluation of Sentiment Measure
3.4. Discussion
3.5. Conclusion
4. Construction of Legal Drafting Sentimental Index
4.1. Introduction
4.2. Pre-Development
4.2.1. Data Set
4.3. Development
4.4. Evaluation
4.5. Conclusion
5. Data Analysis
5.1. Experimental Setup
5.2. Sentiment Classification
5.3. Research Results and Evaluation
6. Political Comments in Legal Drafting process
6.1. Tracking Political Sentiment in Online Discussion Forum
6.2. Results
6.3. Robustness Checks
6.4. Visualizations and Data Interpretation
6.5. Discussion
6.6. Conclusion
7. Conclusion and Future Direction
7.1. Conclusion
7.2. Summary for Contribution
7.3. Future Works
5. Research Design
Opinion of People
Features
Extraction
POS Tagging
Frequent Feature
Feature Prunning
Sentence
Analysis
Opinion Sentence Orientation and
Identification
Opinion
Words
Opinion Words Extraction
Opinion Orientation Identification
Sentiment
Summarization
Evaluation
Summary
Generalization
Visualization
6. Literature
Steps
Features Extraction
Literature
Siqueira, H., & Barros, F. (2010). A feature extraction process for sentiment
analysis of opinions on services. In Proceedings of International Workshop on
Web and Text Intelligence.
Abbasi, A., Chen, H., & Salem, A. (2008). Sentiment analysis in multiple
languages: Feature selection for opinion classification in Web forums. ACM
Transactions on Information Systems (TOIS), 26(3), 12.
Wilson, T., Wiebe, J., & Hoffmann, P. (2009). Recognizing contextual polarity:
An exploration of features for phrase-level sentiment analysis. Computational
linguistics, 35(3), 399-433.
Sentence Analysis
Pang, B., & Lee, L. (2004, July). A sentimental education: Sentiment analysis
using subjectivity summarization based on minimum cuts. In Proceedings of the
42nd annual meeting on Association for Computational Linguistics (p. 271).
Association for Computational Linguistics.
Wilson, T., Wiebe, J., & Hoffmann, P. (2005, October). Recognizing contextual
polarity in phrase-level sentiment analysis. In Proceedings of the conference on
human language technology and empirical methods in natural language
processing (pp. 347-354). Association for Computational Linguistics.
Nasukawa, T., & Yi, J. (2003, October). Sentiment analysis: Capturing
favorability using natural language processing. In Proceedings of the 2nd
international conference on Knowledge capture (pp. 70-77). ACM.
O’Keefe, T., & Koprinska, I. (2009). Feature selection and weighting methods
in sentiment analysis. ADCS 2009, 67.
Opinion Words
Summarization
Pang, B., & Lee, L. (2004, July). A sentimental education: Sentiment analysis
using subjectivity summarization based on minimum cuts. In Proceedings of the
42nd annual meeting on Association for Computational Linguistics (p. 271).
Association for Computational Linguistics.
Mei, Q., Ling, X., Wondra, M., Su, H., & Zhai, C. (2007, May). Topic
sentiment mixture: modeling facets and opinions in weblogs. In Proceedings of
the 16th international conference on World Wide Web (pp. 171-180). ACM.
Benamara, F., Cesarano, C., Picariello, A., Recupero, D. R., & Subrahmanian,
V. S. (2007, March). Sentiment Analysis: Adjectives and Adverbs are better
than Adjectives Alone. In ICWSM.
Wilson, T., Hoffmann, P., Somasundaran, S., Kessler, J., Wiebe, J., Choi, Y., ...
& Patwardhan, S. (2005, October). OpinionFinder: A system for subjectivity
analysis. In Proceedings of hlt/emnlp on interactive demonstrations (pp. 34-35).
Association for Computational Linguistics.
Sentiment
Summarization
Beineke, P., Hastie, T., Manning, C., & Vaithyanathan, S. (2004). Exploring
sentiment summarization. In AAAI Spring Symposium on Exploring Attitude and
Affect in Text: Theories and Applications (AAAI tech report SS-04-07).
Lerman, K., Blair-Goldensohn, S., & McDonald, R. (2009, March). Sentiment
summarization: evaluating and learning user preferences. In Proceedings of the
12th Conference of the European Chapter of the Association for Computational
Linguistics (pp. 514-522). Association for Computational Linguistics.
Nishikawa, H., Hasegawa, T., Matsuo, Y., & Kikui, G. (2010, July). Optimizing
informativeness and readability for sentiment summarization. In Proceedings of
the ACL 2010 Conference Short Papers (pp. 325-330). Association for
Computational Linguistics.
Hu, M., & Liu, B. (2004, August). Mining and summarizing customer reviews.
In Proceedings of the tenth ACM SIGKDD international conference on
Knowledge discovery and data mining (pp. 168-177). ACM.
Zhu, J., Zhu, M., Wang, H., & Tsou, B. K. (2009, November). Aspect-based
sentence segmentation for sentiment summarization. In Proceedings of the 1st
international CIKM workshop on Topic-sentiment analysis for mass opinion
(pp. 65-72). ACM.
Evaluation
Whitelaw, C., Garg, N., & Argamon, S. (2005, October). Using appraisal
groups for sentiment analysis. In Proceedings of the 14th ACM international
conference on Information and knowledge management (pp. 625-631). ACM.
Koçyiğit, A., Tapucu, D., Yanikoglu, B., & Saygın, Y. (2012). An aspectlexicon creation and evaluation tool for sentiment analysis researchers. In
Machine Learning and Knowledge Discovery in Databases (pp. 804-807).
Springer Berlin Heidelberg.
Summary Generalization
Zhuang, L., Jing, F., & Zhu, X. Y. (2006, November). Movie review mining and
summarization. In Proceedings of the 15th ACM international conference on
Information and knowledge management (pp. 43-50). ACM.
Wilson, T. A. (2008). Fine-grained subjectivity and sentiment analysis:
recognizing the intensity, polarity, and attitudes of private states. ProQuest.
Potts, C., & Schwarz, F. (2008). Exclamatives and heightened emotion:
Extracting pragmatic generalizations from large corpora. Ms., UMass Amherst,
1-29.
Visualization
Na, J. C., Thet, T. T., Khoo, C. S., & Kyaing, W. Y. M. (2011). Visual
sentiment summarization of movie reviews. In Digital Libraries: For Cultural
Heritage, Knowledge Dissemination, and Future Creation (pp. 277-287).
Springer Berlin Heidelberg.
Wanner, F., Rohrdantz, C., Mansmann, F., Oelke, D., & Keim, D. A. (2009).
Visual sentiment analysis of rss news feeds featuring the us presidential election
in 2008. Bibliothek der Universität Konstanz.
Hao, M., Rohrdantz, C., Janetzko, H., Dayal, U., Keim, D. A., Haug, L., & Hsu,
M. C. (2011, October). Visual sentiment analysis on twitter data streams. In
Visual Analytics Science and Technology (VAST), 2011 IEEE Conference on
(pp. 277-278). IEEE.
Oelke, D., Hao, M., Rohrdantz, C., Keim, D. A., Dayal, U., Haug, L., &
Janetzko, H. (2009, October). Visual opinion analysis of customer feedback
data. In Visual Analytics Science and Technology, 2009. VAST 2009. IEEE
Symposium on (pp. 187-194). IEEE.
7. Possible Case Study
No
Country
Note
1 Australia
OpenAustralia
2 Canada
OpenParliament
Debates of the Senate
(Hansard)
3 European
Votewatch
Parliament It's Your Parliament
4
5
6
7
8
Link
http://www.openaustralia.org.au/
http://openparliament.ca
http://www.parl.gc.ca/Content/Sen/Chamber/
412/Debates/097db_2014-11-20-e.htm
www.votewatch.eu
http://www.itsyourparliament.eu
Ireland
New
Zealand
South
Africa
United
Kingdom
KildareStreet
CommonNZ
www.kildarestreet.com
http://votes.wotfun.com/
Parliamentary
Monitoring Group
They Work for You
PublicWhip
http://www.pmg.org.za/programmes/hearings
United
States of
America
Gov Tract of the US
MapLight
https://www.govtrack.us/congress/bills/113/s1086
http://maplight.org/us-congress/bill
http://www.theyworkforyou.com/debates/
www.publicwhip.org.uk/
8. Seminar and Conference
Title
Sentiment
Analysis
Innovation
DATA
ANALYTICS
2015
Visualizatio
n and Data
Analysis
(VDA 2015)
the 9th
internation
al aaai
conference
on web
and social
media
WSDM
Sentiment
Symposium
Date
29-30
April 2015
Location
San Francisco
Link
http://theinnovationenterprise.com/summits/sentim
ent-analysis-san-francisco-2015
19-24 July
2015
Nice, France
http://www.iaria.org/conferences2015/DATAANALYT
ICS15.html
8-12
February
2015
San Francisco
http://vda-conference.org/
26-29
May 2015
Oxford, UK
http://www.icwsm.org/2015/index.php
2-6
February
2015
5-6 March
2015
Shanghai,
China
http://www.wsdm-conference.org/2015/call-forpapers/
New York
http://sentimentsymposium.com/agenda.html