Emotion Detection In

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Emotion Detection In E-Learning Using Opinion Mining Technique: 

Emotion Detection In E-Learning Using Opinion Mining Technique Remya G P S7-IT Roll No: 55

CONTENTS: 

CONTENTS INTRODUCTION E-LEARNING EMOTION OPINION MINING NATURAL LANGUAGE PROCESSING(NLP) AREA OF SIGNIFICANCE CONCEPTUAL SOLUTION CONCLUSION

INTRODUCTION: 

INTRODUCTION Traditional class rooms to E-learning. Much interest in educational institution and individuals. Teaching effect propagating through the use of computers. Emotion can effect the E-learning. Emotion means how one feals.

INTRODUCTION: 

INTRODUCTION 2 types of emotions. Makes positive impact on the learning. Makes negative impact on the learning. Opinion mining technique detecting emotions. Extracting & analyzing user data and provide it in a useful format.

E-LEARNING: 

E-LEARNING All forms of electronically supported learning and teaching. Information & communication system serve as media to implement e-learning. Most e-learning application disregard learning method. E-learning issues. Emotion has significant impact on e-learning.

EMOTION: 

EMOTION Experience of an individuals state of mind. It is associated with mood, personality, motivation etc. It can influence the knowledge and overall goals of the students. Autotutor project used to detect the emotion.

OPINION MINING: 

OPINION MINING Extraction and analysis of opinion. Terms used with opinion mining Affective rating. Sentiment analysis. Sentiment extraction. Sentiment classification.

OPINION MINING: 

OPINION MINING Different techniques used with opinion mining Appraisal theory. Systematic functional linguistics. Natural language processing. Statistical schema matching.

NLP: 

NLP Natural language processing. Design and build software that will analyze, understand, and generate language that humans use naturally. Knowing what concept a word or phrase stands for and knowing how to link in a meaningful way. It is easiest for humans to learn and use hardest for a computer to master.

CHALLENGES: 

CHALLENGES A single keyword can be used to convey 3 different opinions. eg: 1) This is a great camera. 2) A great amount of money was spent for promoting this. Eg: fighting, disease is –Ve in a war context but +Ve in a medical one. Spam opinions , mislead reader.

AREA OF SIGNIFICANCE: 

AREA OF SIGNIFICANCE A conceptual framework that can extract, analyze and predict the emotion of learner. This assist the lecturer to make wise decisions to make the teaching methods and process. The area “A” the convergence zone , we could explore a new area of research.

ONCEPTUAL VIEW: 

ONCEPTUAL VIEW Opinion 2 mining E-learning A 1 3 Emotion detection

CONCEPTUAL SOLUTION: 

CONCEPTUAL SOLUTION Divided into 3 parts -> E-learning student, tutor and database -> Emotion detection & opinion mining identifies the emotion using opinion mining technique. ->out put Output of our emotion detection system.

PowerPoint Presentation: 

E-Learning Provide material to learn. Accepting opinions from the students. Opinion mining and emotion detection Extract & analyze the data. General text and language infrastructure (GATE). Tool used for research purpose. Language processing software. word splitter, tokenizer, gazetteer, and pos tagger.

PowerPoint Presentation: 

Output for e-learning Using graphics. For analysis by the lecturer or unit coordinator. In the GATE software sentence split using splitter. tokenize using tokenizer. applying annotation rules.

CONCLUSION: 

CONCLUSION We have discussed the mile stones attained in opinion mining and e-learning. Determine the emotions experienced by students in e-learning. There is no generalized algorithm. A basic task in sentiment analysis is classifying the polarity of a given text document and the opinion in the document.

PowerPoint Presentation: 

Thank you