An emerging field within Sentiment Analysis concerns the investigation about how sentiment polarities towards concepts have to be adapted with respect to the different domains in which they are used. In this paper, we explore the use of fuzzy logic for modeling concept polarities, and the uncertainty associated with them, with respect to different domains. The approach is based on the use of a knowledge graph built by combining two linguistic resources, namely WordNet and SenticNet. Such a knowledge graph is then exploited by a graph-propagation algorithm that propagates sentiment information learned from labeled datasets. The system implementing the proposed approach has been evaluated on the Blitzer dataset by demonstrating its viability in real-world cases.

1. A Fuzzy Approach For Multi-Domain
Sentiment Analysis
Mauro Dragoni
Fondazione Bruno Kessler (FBK), Shape and Evolve Living Knowledge Unit (SHELL)
https://shell.fbk.eu/index.php/Mauro_Dragoni - dragoni@fbk.eu
work done in collaboration with
Prof. Andrea G.B. Tettamanzi and Prof. Celia da Costa Pereira
INRIA Sophia Antipolis
June, 19th 2014

2. Outline
1. Background on Sentiment Analysis and Fuzzy Logic
2. Motivations
3. The Approach
4. Evaluation of the Implemented System

3. Sentiment Analysis - 1
 Natural Language Processing task for identifying the opinion given by
someone with respect to something.
 Opinions may be positive, negative, or neutral.
 The value associated with the opinion is called “polarity”.

4. Sentiment Analysis - 2
 Basic challenges:
 Identification of the polarities for each term in the text.
 Deciding how to aggregate the different polarities.
 Advanced challenges:
 Identification of the entities in each sentence (subjects).
 Identification of the features describing each entity.
 Adaptation of the sentiment model to different domain.
 Manage the uncertainty of each learned information within the single domain

5. Fuzzy Logic
 Allows to represent imprecise information.
 With respect to classical logic, truth-values of assertions may assume all
values in the interval [0, 1]
 The main element of the fuzzy logic are Fuzzy Sets
Hot temperature.
x
y

6. Motivations - 1
 The same concept may have different polarities in different domains.
 The polarity associating a concept to a domain may be uncertain due to
the different contexts in which it is used.

7. Motivations - 2
 The assignment of a unique polarity value to the entire text leads to
imprecise information.
 In the same text, different aspects have to be analyzed.
 A significant concept extraction capability is required.
“I bought a new smartphone: the screen is awesome, even if some
colors are not very brilliant, but the battery is too short”

8. The Approach
 Creation of the knowledge base.
 Concept extraction.
 Learning of the preliminary sentiment information.
 Propagation of the learned information through the knowledge graph.
 Modeling of the fuzzy shapes.

9. Creation of the Knowledge Base
 Based on the integration of WordNet with SenticNet
 WordNet has been enriched with terms extracted from the Roget’s
Thesaurus
 The links between WordNet and SenticNet have been built by taking into
account the synonyms of each WordNet synset and the synonyms of each
SenticNet concept.
 In order to avoid ambiguities not all associations have been created.
 Example: concept “base”
WordNet: 20 senses (for the noun)
SenticNet: base (beneath, below, understructure)  WordNet sense 2

10. Concept Extraction - 1
 Two samples:
1. Today I went to the mall and bought some desserts and a lot of very nice
Christmas gifts.
2. The touchscreen is awesome but the battery is too short.

11. Concept Extraction - 2

12. Multi-Domain Fuzzy Propagation - 1
 Polarity information is propagated through the knowledge base by using
an algorithm implementing the simulated annealing strategy.
 The propagation of the values is driven by three parameters: annealing
rate, propagation rate, and convergence limit.
 The intermediate polarity values measured on each concept at the end of
each iteration are stored in order to build the final fuzzy shape associated
with each combination concept-domain.
 A different model is learned for each domain.

13. Multi-Domain Fuzzy Propagation - 2
Domain Initialization:
Information Propagation:
Stop Condition:

14. Multi-Domain Fuzzy Propagation - 3
 Trade-offs:
 high propagation rate  risk of polarity convergence
 low convergence limit  risk of polarity convergence
 lower propagation rate  incomplete graph
 high annealing rate  premature stop of the algorithm
 high convergence limit  premature stop of the algorithm

15. Multi-Domain Fuzzy Propagation - 4

16. Modeling of Fuzzy Shapes - 1
Value computed from the
training set.
Value obtained after the
propagation phase.
Support computed based on the
variance value.

17. Modeling of Fuzzy Shapes - 2
Type 1 level of uncertainty: the core
of the fuzzy trapezoid crosses the
neutral polarity
Type 2 level of uncertainty: only the
support of the fuzzy trapezoid crosses
the neutral polarity

18. Evaluation of the System - 1
 Evaluation on the Blitzer dataset:
 25 domains
 ~3000 reviews for each domain in the balanced dataset
 75% of instances for the training, 25% for the validation
 Three baselines: SVM, Max-Entropy, and Naïve-Bayes
 Compared the performance by discarding the different levels of uncertainty
 Evaluation on:
 Elementary Polarity Computation
 Concept Extraction + Polarity Computation

19. Evaluation of the System - 2
 How fuzzy polarities are aggregated?
x

20. Evaluation of the System - 3

21. Evaluation of the System - 4
Approach Avg. Precision Avg. Recall
SVM 0.8068 1.0
Naïve-Bayes 0.8227 1.0
Max-Entropy 0.8225 1.0
MDFSA 0.8617 ~ 1.0
MDFSA
(Type 1 Uncertainty excluded)
0.8735 ~ 0.7
MDFSA
(Type 1 & 2 Uncertainty excluded)
0.8991 ~ 0.5
Elementary Polarity Computation:

22. Evaluation of the System - 5
Approach Precision Recall F-Measure
MDFSA 0.25 0.26 0.25
IBM 0.24 0.14 0.18
UNI-NEGEV 0.12 0.05 0.07
Concept Extraction + Polarity Computation (ESWC 2014 Challenge):

23. Future Work
 Integration of more knowledge bases into the system.
 Improve how ambiguities are addressed.
 Improve the concept extraction module.
 Extending the approach for addressing multilingualism.
 Apply the approach to the social network environment.

24. Mauro Dragoni
https://shell.fbk.eu/index.php/Mauro_Dragoni
dragoni@fbk.eu