A Fuzzy Approach For Multi-Domain Sentiment Analysis

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

Outline

1. Background on Sentiment Analysis and Fuzzy Logic

2. Motivations

3. The Approach

4. Evaluation of the Implemented System

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”.

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

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

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.

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”

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.

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

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.

Concept Extraction - 2

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.

Multi-Domain Fuzzy Propagation - 2

Domain Initialization:

Information Propagation:

Stop Condition:

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

Multi-Domain Fuzzy Propagation - 4

Modeling of Fuzzy Shapes - 1

Value computed from the training set.

Value obtained after the propagation phase.

Support computed based on the variance value.

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

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

Evaluation of the System - 2

How fuzzy polarities are aggregated?

x

Evaluation of the System - 3

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:

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):

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.

Mauro Dragonihttps://shell.fbk.eu/index.php/Mauro_Dragoni dragoni@fbk.eu