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PRESENTATION ON
EMOTION SENSOR
SUBMITTED BY :
Lalit Mohan Kumalta
Pramod Singh
INTRODUCTION
 Emotion Sensor is simply a tool to detect emotions of sentences or
user provided tweets.
 It uses various machine learning algorithms and python libraries.
 Few of these libraries include numpy,pandas,scikit learn.
OBJECTIVE
 The main objective of this project is to detect emotions of sentences
or user provided tweets.
 Texts as well as speech recognition is used to provide inputs.
VARIOUS ML ALGO USED
 Logistic Regression
 Linear Regression
 Naïve Bayes Classifier
 Support Vector Machines
 Multinomial Naïve Bayes Algorithm
CONCEPTS USED FOR BETTER
PERFORMANCE
 Segmentation
 Tokenization
 Stemming
 Lemmatization
Role of Supervised Learning
 Supervised learning allows collecting data and produces data output
from previous experiences.
 Helps to optimize performance criteria with the help of experience.
 Supervised machine learning helps to solve various types of real-
world computation problems
Role of Speech Recognition
 Speech recognition enables hands-free control of various devices and
equipment (a particular boon to many disabled persons), provides
input to automatic translation, and creates print-ready dictation.
 Among the earliest applications for speech recognition were
automated telephone systems and medical dictation software.
Various emotions Analyzed
Various SENTIMENTAL ANALYZER analyzed through this tool are happy ,
sad , angry , neutral etc.
Future Scopes
 Multi-statements analysis.
 Complex statements analysis.
 emotions behind open ended questions.
 Adding of additional emotions.
 Improving accuracy of the tool.

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PPT Emotion Sensor.pptx

  • 1. PRESENTATION ON EMOTION SENSOR SUBMITTED BY : Lalit Mohan Kumalta Pramod Singh
  • 2. INTRODUCTION  Emotion Sensor is simply a tool to detect emotions of sentences or user provided tweets.  It uses various machine learning algorithms and python libraries.  Few of these libraries include numpy,pandas,scikit learn.
  • 3. OBJECTIVE  The main objective of this project is to detect emotions of sentences or user provided tweets.  Texts as well as speech recognition is used to provide inputs.
  • 4. VARIOUS ML ALGO USED  Logistic Regression  Linear Regression  Naïve Bayes Classifier  Support Vector Machines  Multinomial Naïve Bayes Algorithm
  • 5. CONCEPTS USED FOR BETTER PERFORMANCE  Segmentation  Tokenization  Stemming  Lemmatization
  • 6. Role of Supervised Learning  Supervised learning allows collecting data and produces data output from previous experiences.  Helps to optimize performance criteria with the help of experience.  Supervised machine learning helps to solve various types of real- world computation problems
  • 7. Role of Speech Recognition  Speech recognition enables hands-free control of various devices and equipment (a particular boon to many disabled persons), provides input to automatic translation, and creates print-ready dictation.  Among the earliest applications for speech recognition were automated telephone systems and medical dictation software.
  • 8. Various emotions Analyzed Various SENTIMENTAL ANALYZER analyzed through this tool are happy , sad , angry , neutral etc.
  • 9. Future Scopes  Multi-statements analysis.  Complex statements analysis.  emotions behind open ended questions.  Adding of additional emotions.  Improving accuracy of the tool.