Facial Feature-Based Human Emotion Detection Using Machine Learning

2021 ◽  
pp. 107-120
Author(s):  
Mritunjay Rai ◽  
Agha Asim Husain ◽  
Rohit Sharma ◽  
Tanmoy Maity ◽  
R. K. Yadav
Keyword(s):  
Machine Learning ◽  
Facial Feature ◽  
Emotion Detection ◽  
Human Emotion ◽  
Feature Based

Human Emotion Detection using Machine Learning Techniques

2020 ◽  
Author(s):  
Punidha Angusamy ◽  
Inba S ◽  
Pavithra K.S ◽  
Ameer Shathali M ◽  
Athiparasakthi M
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Emotion Detection ◽  
Human Emotion ◽  
Learning Techniques

Machine Learning in Human Emotion Detection from the Speech

2021 ◽  
Author(s):  
Xiaoli Qiu ◽  
Wei Li ◽  
Yang Li ◽  
Hongmei Gu ◽  
Fei Song ◽  
...  
Keyword(s):  
Machine Learning ◽  
Emotion Recognition ◽  
Pulse Power ◽  
Speech Emotion Recognition ◽  
Learning Science ◽  
Emotion Detection ◽  
Classification Techniques ◽  
Human Emotion ◽  
Research Findings

The identification of speech emotions is amongst the most strenuous and fascinating fields of machine learning science. In this article, Chinese emotions are classified as a disruptive atmosphere that classifies several feelings into four major emotional organizations: pleasure, sorrow, resentment, and neutrality. A machine learning in human emotion detection (ML-HED) framework is proposed. The technology suggested removing prosodic and spectrum elements of an audio wave, such as a pulse, power, amplitude, Cepstrum melt frequency correlations, linearly fixed Cepstral, and identification with a template. In all, 87,75% of performers’ statements and 93% of women’s actors were given reliability. The research findings show that the revolutionary technology achieves greater precision by accurately interpreting the feelings, which contrasts with current speech emotion recognition approaches. Besides, the derived characteristics were contrasting with various classification techniques in this study for the comprehensive idea.

scholarly journals Emotion Detection using Social Media and Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 4491-4494
Author(s):  
Mr. Bhavar Shivam S.
Keyword(s):  
Machine Learning ◽  
Social Networking ◽  
Social Networking Sites ◽  
Training Dataset ◽  
Emotion Detection ◽  
Pos Tagging ◽  
Testing Dataset ◽  
Svm Algorithm ◽  
The Right ◽  
Negative Sentiment

Today we do a lot of things online from shopping to data sharing on social networking sites. Social networking (SNS) is good for releasing stress and depression by sharing one’s thoughts. Thus, emotion detection has become a hot trend to day. But there is a problem in analyzing emotions on a SNS like twitter as it generates lakhs of tweets each day and it is hard to keep track of the emotion behind each tweet as it is impossible for a human being to read and decide the emotions behind tweets. So, to help understand behind the texts in a SNS site we thought of designing a project which will keep track of the tweets and predict the right emotion behind the tweets whether they have a positive or a negative sentiment behind them. This thought of project can be achieved by a integration of SNS with NLP and machine learning together. For SNS we will use Twitter as it generates a lot of data which is accessible freely using an API. First, we will enter a keyword and fetch tweets from the twitter. Then stop words will be removed from these tweets using NLTK stop words database. Then the tweets will be passed for POS tagging and only right form of grammatical words will be kept and others will be removed. Then we create a training dataset with two types positive and negative. Then SVM algorithm will be trained using this training dataset. Then each tweet will be passed to the SVM as testing dataset which in turn will return classification of each tweet as a whole in two classes positive and negative. Thus, our application will be helpful in recognizing emotion behind a tweet.

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