Emotional Annotation of Text

36 Pages·2011·0.96 MB·English

by  Nithya Akhil

Seminar Report
on

Emotional Annotation of Text

Submitted in partial fulfillment of the requirements for
the award of the degree of

Master of Technology
in
Computer and Information Science
by

Nithya G.P.

DEPARTMENT OF COMPUTER SCIENCE
COCHIN UNIVERSITY OF SCIENCE AND TECHNOLOGY
COCHIN-682022
2011
DEPARTMENT OF COMPUTER SCIENCE
COCHIN UNIVERSITY OF SCIENCE & TECHNOLOGY
COCHIN-682022

CERTIFICATE

This is to certify that the report entitled “Emotional Annotation of
Text” is a bonafide record of the seminar presented by Mrs. Nithya G.P. in partial
fulfillment of the requirements for the award of M.Tech. Degree in Computer and
Information Science from Cochin University of Science & Technology, during the
academic year 2010-2011.

Dr. K. Poulose Jacob G. Santhosh Kumar
Professor & Seminar Guide
Head of the Department Senior Lecturer
Dept. of Computer Science Dept. of Computer Science
CUSAT CUSAT
ACKNOWLEDGEMENT

Apart from the efforts of me, the success of this seminar depends largely on the
encouragement and guidelines of many others. I take this opportunity to express my
gratitude to the people who have been instrumental in the successful completion of this
seminar.

I am extremely grateful to Prof. Dr. K Poulose Jacob, Director, Department of
Computer Science, for the guidance and encouragement and for providing me with best
facilities and atmosphere for the creative work.

I would like to thank my seminar guide, Mr. G. Santhosh Kumar, Senior Lecturer,
Department of Computer Science, for the valuable guidance, care and timely support
throughout the seminar work. He has always a constant source of encouragement.

I would like to thank Dr. Sumam Mary Idiculla, Reader, Department of Computer
Science, for her valuable suggestions towards the preparation of my seminar.

I thank all the staff members of our department for extending their cooperation
during my seminar.

I would like to thank my friends for their encouragement, which helped me to keep
my spirit alive and to complete this work successfully.

Finally, I express my deepest gratitude to my husband Akhil Chandran and our son
Nayantheerth for their love and support during these busy days.

Nithya G.P.
ABSTRACT

Emotion is at the core of understanding ourselves and others, and the
automatic expression and detection of emotion could enhance our experience with
technologies. Emotion analysis is a rapidly developing area in computational linguistics.
With the advent of affective computing, the task of adequately identifying, representing
and processing the emotional connotations of text has acquired importance. This seminar
concentrates on how the emotion of the text is annotated. The emotion of a sentence of
text should be derived by composition of the emotions of the words in the sentence.
Existing approaches to this task rely most often on a simplified representation of the
sentence as a bag of words, where all words contribute in equal measure. However,
intuitively certain words can probably be considered more significant; depending on the
role they play in the word from their syntactic or semantic structure. Of the various
existing approaches for representing emotions, some are better suited for some problems
and some for others. In this seminar I am focusing on the various problems and the
technologies used to implement the same and how the emotional annotation of a text is
done by ontological reasoning.

Keywords

Affective computing, Emotional annotation, Emotion analysis, Reasoning, Ontologies
CONTENTS

1 INTRODUCTION 1

2 PROBLEM DOMAIN AND THE TECHNOLOGIES 5
2.1 Computational representations of emotions 5
2.1.1 Classification of emotions 5
2.1.2 Structure of emotions 6
2.2 Semantic Web technologies 7
2.2.1 Ontology web language 8
2.2.2 Frameworks and APIs 8
2.2.3 Ontology editor 9
2.2.4 Reasoner 10
2.3 Dependency analysis 10
2.4 Automated mark up of emotions in text 12

3 ONTOLOGY-SUPPORTED MARK UP OF EMOTIONS 14
3.1 Dependency analysis ontology for the obtaining of words implied
in the mark-up process 14
3.1.1 Structure 14
3.1.2 Generation of the OWL file based on the dependency analysis
of Minipar 15
3.1.3 Obtaining the words implied in the mark-up process 16
3.1.4 Example 17
3.2 Emotional ontology for the correct interpretation of final mark-up
values 17
3.2.1 Structure 19
3.2.2 Datatype properties 21
3.2.3 Data range 21
3.2.4 Automatic classification of emotions using datatype properties 22

4 AN EXAMPLE APPLICATION OF THE WHOLE PROCESS 25
4.1 Using dependency ontology to obtain the words implied in the
mark-up process 25
4.2 Using automatic classification to interpret emotional dimensions
as emotional categories 26
4.3 Configuring a voice synthesizer based on the emotional mark up 26

5 CONCLUSION 29

REFERENCES 31
Seminar Report Emotional Annotation of Text
CHAPTER 1

INTRODUCTION

Emotional annotation of text is a step towards implementing affective computing.
Affective Computing is computing that relates to, arises from, or deliberately influences
emotion and other affective phenomena. The field was originally named and defined
treating affect and emotion essentially synonymously and there is still no widely agreed
upon definition of either the term "emotion" or "affect" in the literature; however, there is
a general acceptance that affect is the broader term, and that states such as "interest ―are
affects, whether or not they are emotions, while states such as "anger" are both an
emotion and an affect. Regardless of the resolution of the precise definitions of emotion
and affect, research in Affective Computing addresses the broader sense of the two terms,
and contributes to Artificial Intelligence, Pattern Recognition, Machine Learning,
Human-Computer Interaction, Social Robotics, Autonomous Agents, Cognitive and
Affective Sciences, Affective Neuroscience, Neuroeconomics, Health-behavior Change,
and many other areas where technology is used to detect, recognize, measure, model,
simulate, communicate, elicit, handle, or otherwise understand and directly influence
emotion and other affective phenomenon.
Imagine your robot entering the kitchen as you prepare breakfast for guests. The
robot looks happy to see you and greets you with a cheery "Good morning." You mumble
something it does not understand. It notices your face, vocal tone, smoke above the stove,
and your slamming of a pot into the sink, and infers that you do not appear to be having a
good morning. Immediately, it adjusts its internal state to ―subdued," which has the effect
of lowering its vocal pitch and amplitude settings, eliminating cheery behavioral displays,
and suppressing unnecessary conversation. Suppose you exclaim unprintable curses that
are out of character for you, yank your hand from the hot stove, rush to run your fingers
under cold water, and mutter "... ruined the sauce." While the robot's speech recognition
may not have high confidence that it accurately recognized all of your words, its
assessment of your affect and actions indicates a high probability that you are upset and
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Seminar Report Emotional Annotation of Text
possibly hurt. At this moment it might turn its head with a look of concern, search its
empathetic phrases and select, "Burn-Ouch ... Are you OK?" and wait to see if you are,
before selecting the semantically closest helpful response, "Shall I list sauce recipes?" As
it goes about its work helping you, it watches for signs of your affective state changing -
positive or negative. It may modify an internal model of you, representing what is likely
to elicit displays such as pleasure or displeasure from you, and it may later try to
generalize this model to other people, and to development of common sense about
people's affective states. It looks for things it does that are associated with improvements
in the positive nature of your state, as well as things that might frustrate or annoy you. As
it finds these, it also updates its own internal learning system, indicating which of its
behaviors you prefer, so it becomes more effective in how it works with you.
This seminar deals with the various technologies behind annotation of text. Given
a text how to get the emotion corresponding to that text?. The way in which the meaning
of a sentence is built from the meaning of its words has been a subject of study in
computational linguistics for a long time. No such study has been carried out for the way
in which the emotional connotations of a sentence are affected by the emotional
connotations of its words. Existing approaches to this task rely most often on a simplified
representation of the sentence as a bag of words, where all words contribute in equal
measure, much in the way information retrieval simplifies the treatment of text. However,
intuitively certain words can probably be considered more significant, depending on the
role they play in the word from their syntactic or semantic structure. An important
hypothesis is that if this kind of sentence structure were to be represented
computationally in a way that modeled how the emotional contributions of words affect
the emotional connotations of the sentence, it would provide the means for capturing
these intuitions. A static ontology of word dependencies within a sentence fulfills the
requirements for such a representation.
An important challenge in addressing issues of affective computing is having an
adequate representation of emotions. Existing approaches vary from identifying a set of
basic categories—with a name tag assigned to each one of them—to design a multi
dimensional space in terms of primitive elements—or emotional dimensions—such that
any particular emotion can be defined in terms of a tuple of values along the different
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Seminar Report Emotional Annotation of Text
dimensions. If one were to operate computationally with a representation of emotions
expressed in more than one format, one is faced with the task of being able to convert
from one to another. This task is reasonably easy when converting from emotional
categories to emotional dimensions: it would suffice to assign a particular tuple of values
for the emotional dimensions of each emotional category. When trying to convert from
emotional values expressed in terms of emotional dimensions to a representation in terms
of emotional categories this is not so simple. The problem lies in the fact that, given the
subjectivity associated with emotional perception, the particular values assigned to a
given impression by one person usually deviate slightly from what a different person
would have assigned. This suggests that the process of converting from emotional
dimensions to emotional categories should be carried out in a manner that allows a
certain tolerance, so that a region of space in the universe of emotional dimensions is
assigned to each emotional category, rather than just a single point in the universe.
A separate problem arises from the fact that there are a large number of emotional
categories, and the differences and similarities between them are not clear cut. In some
cases, it is reasonable to assume that certain emotional categories may be subsumed by
others. For example, the emotion anger subsumes the emotions sulking, displeasure and
annoyance which may be seen as different types of anger. This suggests that a taxonomy
of emotional categories as a hierarchy might be useful in finding correspondence between
more specific emotional categories and more general emotional categories.
In this context, the development of an ontology of emotional categories based on
description logics, where each element is defined in terms of a range of values along the
space of emotional dimensions, provides a simple and elegant solution.
This seminar describes the development of a system that relies on two ontologies,
together with its application as an interface between a text input marked up in terms of
emotional dimensions and a set of rules for configuring an emotionally enabled voice
synthesizer. By reasoning over the word dependency ontology, the emotional
connotations of any node in the structure can be computed separately, from the
contribution of single words to the contribution of entire sentences, including intervening
substructures. By reasoning over the emotion ontology, insertion of new instances of
emotional concepts into the ontology results in their automatic classification under the
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Seminar Report Emotional Annotation of Text
corresponding branch of the hierarchy. The system can then trace the ascendants in the
ontology of the corresponding value, until a more general concept is found that satisfies
the condition that specific rules are available for generating an appropriate voice
synthesis configuration for expressing the intended emotional impression.
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CHAPTER 2

PROBLEM DOMAIN AND THE TECHNOLOGIES

Four basic topics are coming under problem domain and technologies: the
computational representation of emotions, the Semantic Web technologies that have been
employed, the natural language processing technique employed to obtain the syntactic
structure of sentences (dependency analysis) and the review of the system chosen as
domain of application.

2.1 computational representations of emotions

Emotions are not easy to define, because there are a lot of factors that contribute
to them. A good definition of emotion must take into consideration: conscious feeling of
emotion, process which takes place in the nervous system and in the brain and expressive
models of emotion. Emotions take place when something unexpected happens and the so-
called ―emotional effects‖ begin to take control.

2.1.1 Classification of emotions

Many of the terms used to describe emotions and their effects are difficult to tell
apart from one another, as they are usually not well defined. This is due to the fact that
the abstract concepts and the feelings associated with such concepts are very difficult to
express with words. For this reason, there are a lot of methods for describing the
characteristics of emotions: emotional categories and emotional dimensions which
represent the essential aspects of emotional concepts.

Emotional categories: The most common method for describing emotions is the use of
emotional words or affective labels. Different languages provide assorted labels of
varying degrees of expressiveness for the description of emotional states. There are
significant differences between languages in terms of the granularity with which these
labels describe particular areas of emotional experience. Even within a given language,
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