1. Presenter: Jia-Kuan Lin Advisor: Chung-Hsien Wu
Proposal
Presenter: Jia-Kuan Lin
Advisor: Chung-Hsien Wu

2. Outline
Introduction
Related work

3. Introduction
No matter in the microblog or forum, a post may contain many sentiments
Text Emotion Detecting can help us find out someone’s emotions from a post automatically
Many related works just use positive, negative or natural to classify emotion from a post

4. Introduction
But the emotions of human beings may not contain just three categories
Emotion Categories [R. Plutchik, 2001]
anger, fear, disgust, shame, sadness, joy or guilt
Corpus: ISEAR Databank [K. Scherer, et al., 1997 ]
English Databank
7666 sentences, 7 emotions categories
1096 for anger, 1096 for disgust, 1094 for fear, 1094 for guilt, for joy, 1096 for sadness, 1096 for shame

5. Related work – ISEAR Databank
The ISEAR Questionnaire and Codebook[K. Scherer, et al.,1997]
A corpus containing examples of self-reported affect, the student respondents, both psychologists and non- psychologists
The final data set thus contained reports on seven emotions each by close to 3000 respondents in 37 countries on all 5 continents

6. Related work – ISEAR Databank
Emotion
Example
anger
When my chief accused me of an error that I hadn't made.
My mother in law slept in my bed.
When I was robbed in a bus.
disgust
When I read racist slogans on the walls.
I saw in the street a man spitting.
Getting into a crowded bus.
fear
Before my final examination.
When my mother was robbed in a shop.
When I had to walk along a dark road alone.

7. Related work – ISEAR Databank
Emotion
Example
guilt
Telling white lies.
When quarreling with friends.
Late for a lecture and therefore missed it.
joy
When I got married.
When I entered at the University.
Succeeding in helping others.
sadness
A very close friend left me.
Finding out that the girl I like does not like me.
Lost a friend.
shame
I could not finish my homework on time.
When I did not buy what I had promised to.
Getting a low grade on a midterm.

8. Related work - EmotiNet
EmotiNet: A Knowledge Base for Emotion Detection in Text Built on the Appraisal Theories [Balahur et al., 2011]
The majority of the approaches contemplate only the word level
But emotion is not always expressed through specific words
This paper presents detection of emotion from text based on commonsense knowledge on concepts, their interaction and their affective consequence

9. Related work - EmotiNet
Emotion classify: 7 most basic emotions
anger, fear, disgust, shame, sadness, joy or guilt, (or the neutral)
Generating 4-tuples (subject, action, object, emotion)
fear
anger
Basic Emotions
joy
shame
sadness
disgust
guilt

10. Related Work – EmotiNet
Detecting implicit expressions of emotion in text: A comparative analysis [Balahur et al., 2012]
Contribution
Analyze the characteristics of the corpus with respect to those of the existing lexical resources that are used for emotion detection in WordNet Affect and LIWC
Testing two method for emotion detection:
Supervised: Using SVM SMO learning with uni- bi- trigrams and similarity
Lexicon knowledge-based taking in to account only the emotion- related words found in WordNet Affect and LIWC
Extension of the knowledge contained in EmotiNet with new sources subsequent evaluation of the new extended resource

11. Related Work – EmotiNet
ISEAR Databank in EmotiNet
1081/7666 sentences
175 examples (25 examples per emotion) to construct the core knowledge in EmotiNet
Other examples to test the approach of EmotiNet
Additional source: WordNet Affect and LIWC

12. Related Work – EmotiNet
Three Domain
Emotion, Kinship relations and Actions

13. Related Work – EmotiNet
Process of Action chain

14. Related Work
Using a contextual entropy model to expand emotion words and their intensity for the sentiment classification of stock market news[Yu et al., 2013]
In this paper, they propose the presence and intensity of emotion words as features to classify the sentiment of stock market news articles
A contextual entropy model is developed to expand a set of seed words generated from a small corpus of stock market news articles with sentiment annotation

15. Related Work Contextual Entropy Model Vector representation
Consider co-occurrence strength and contextual distribution between the candidate words and seed words
Vector representation

16. Related Work Similarity measure Expansion Procedure
Use of the Kullback–Leibler (KL) distance to measure the difference between the probabilistic context distributions of a seed word and a candidate word
Expansion Procedure
To determine the sentiment class (i.e., positive or negative) for each candidate