EMOTION MINING IN TEXT

LOVEPREET SINGHSLIET Longowal

Definition of Text Mining

Text Mining is a subset of Unstructured Data Management.

An exploration and analysis of textual data by automatic and semi automatic means to discover new knowledge.

Definition of Emotion A strong feeling deriving from one's

circumstances, mood, or relationships with others.

In psychology, emotion is often defined as a complex state of feeling that results in physical and psychological changes that influence thought and behavior.

Synonyms: feeling, sentiment, sensation

Elements 1.Thoughts: Ideas or images that pop into your head when you are experiencing an emotion. 2.Your Body's Response: The physical changes you experience (for example, increased heart rate, feeling queasy) when you experience an emotion. 3.Behaviours: The things you want or feel an urge to do when you experience a certain emotion. 

Emotions from Text The purpose is not to identify specific

emotions but rather to find the emotional state or mind set of a writer while writing the text.

Theories of Emotion

The major theories of emotion can be grouped into three main categories: Physiological Neurological Cognitive

Theories of EmotionPhysiological theories suggest that responses within the body are responsible for emotions. Neurological theories propose that activity within the brain leads to emotional responses. Cognitive theories argue that thoughts and other mental activity play an essential role in the formation of emotions.

Positive & Negative Emotions

Positive: any emotion that makes us feel good eg. appreciation, joy, love, passion, freedom, excitement. 

Negative: emotions stop us from thinking and behaving rationally and seeing situations in their true perspective eg. Jealousy, anger, fear, guilt, shame, frustration, sadness.

Example: Emotion by viewing an object

Factors on which Emotion depends

Gender Situation Age

Techniques for Emotion Detection

Keyword Spotting Technique Lexical Affinity Method Learning Based Methods Hybrid Methods

1. Keyword Spotting Technique

Text Tokenization Identify Emotions Word

Analysis of Intensity

Negation Check

Emotion

2. Lexical Affinity Method Extension of keyword spotting technique. It assigns a probabilistic ‘affinity’ for a

particular emotion to arbitrary words apart from picking up emotional keywords.

These probabilities are often part of linguistic corpora.

Disadvantages : Assigned probabilities are biased toward corpus-specific genre of texts.

Example The word ‘accident’, having been assigned a

high probability of indicating a negative emotion, would not contribute correctly to the emotional assessment of phrases like “I avoided an accident” or “I met my girlfriend by accident”.

3. Learning based methods

Classify the input texts into different emotions

Learning-based methods try to detect emotions based on a previously trained classifier, which apply various theories of machine learning to determine which emotion category should the input text belongs.

4. Hybrid Methods Combination of both keyword spotting

technique and learning based method Improve accuracy

Limitations of above methods

Ambiguity in keyword definition Incapability of recognizing sentences

without keywords Lack of Linguistic Information Difficulties in determining emotion

indicators

1. Ambiguity in Keyword Definitions

Using emotion keywords is a straightforward way to detect associated emotions, the meanings of keywords could be multiple and vague, as most words could change their meanings according to different usages and contexts.

2. Incapability of Recognizing Sentences without Keywords

Keyword-based approach is totally based on the set of emotion keywords.

Therefore, sentences without any keyword would imply that they do not contain any emotion at all, which is obviously wrong.

For example, “I passed my qualify exam today” and “Hooray! I passed my qualify exam today” should imply the same emotion (joy), but the former without “hooray” could remain undetected if “hooray” is the only keyword to detect this emotion.

3. Lack of Linguistic Information Syntax structures and semantics also have

influences on expressed emotions. For example, “I laughed at him” and “He

laughed at me” would suggest different emotions from the first person’s perspective.

As a result, ignoring linguistic information also poses a problem to keyword-based methods.

Architecture

Input text Output text

Emotion Detector

Emotion Word

Ontology

Emotion Class

Example: Social Network Communication

This research examines the extent to which emotion is present in MySpace comments, using a combination of data mining and content analysis, and exploring age and gender.

Sample: 819 comments to and from USA users.

Classification: Positive and Negative Emotions.

Step 1: Data set Comments from and to active, normal, long-

term US Members. Members having public profile- Normal Comments filtered for standard picture

comments , spam, chain messages using regular expressions.

Resulting comments formed the raw data.

Step 2: Classification Positive & Negative comments. Example: “I Miss You” can be interpreted as

positive emotion & is almost synonym of “I Love You” , even though it suggested sadness.

Classification deals only with text of individual comment rather than emotional state of commenter.

Reasons for choosing particular comment is not considered.

Results Females send and receive significantly more

positive emotions than men. Negative emotion is much rarer than

positive emotions. Limitation: We are considering only public

comments, so the situation for the private messages may be different from above.

THANKS