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

Outline Introduction Related work

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

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, 1094 for joy, 1096 for sadness, 1096 for shame

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

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.

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.

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

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

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

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

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

Related Work – EmotiNet Process of Action chain

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

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

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