Emotion Mining Thesis Topics Help for M.Tech and PhD

What is Emotion Mining?

Emotion mining is the science of detecting, analyzing, and evaluating humans’ feelings towards different events, issues, services, or any other interest. Emotion Mining is most important thing in the today’s scenario social media websites have become an important part of our life. An individual is free to express his opinion, share content with each other’s and access almost any information and data in a couple of seconds. Social media websites like Facebook, MySpace, Twitter welcomes millions of users daily, the almost all content is produced by these millions of users is obviously enormous. It has become important to analyze this data to finds the emotions from this views and opinions expressed by an individual user. The proposed system focuses on extracting comment of users from social media websites by using text mining processes. Sentiment analysis is the technique to processes those comments to finds the emotions in the text of comments. Along with this the report explains how the friendship strength between two individuals can be determined using emotion mining.

Emotion Mining can be divided into three categories.

1. In the first category aims to extract valence of the text. It indicates polarity of emotions associated to it.
2. In second category aims to determine whether text is subjective or factual. It determines if the text contains emotions or not.
3. In the third category aims to recognize intensity of emotions in the text.

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.Behaviors: 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 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 e.g. Jealousy, anger, fear, guilt, shame, frustration, sadness.

Techniques for Emotion Detection

Keyword Spotting Technique

Lexical Affinity Method

Learning Based Methods

Hybrid Methods

Keyword Spotting Technique  

The keyword pattern matching problem can be described as the problem of finding occurrences

of keywords from a given set as sub strings in a given string. This problem has been studied

in the past and algorithms have been suggested for solving it.  In the context of emotion

detection this method is based on certain predefined keywords. These words are classified into

categories such as disgusted, sad, happy, angry, fearful, surprised etc.

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.

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.

Hybrid Methods

• Combination of both keywords 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

Latest Emotion Mining Thesis Topics

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Emotion Mining Thesis Help M.Tech and PhD

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