This document presents an emotion sensor tool that uses machine learning algorithms and Python libraries to detect emotions in sentences and tweets. The main objective is to analyze emotions like happy, sad, angry, and neutral using techniques like supervised learning, speech recognition, tokenization, and sentiment analysis. The tool analyzes text and speech inputs and classifies emotions using algorithms like logistic regression, linear regression, naive Bayes, and support vector machines. Future areas of improvement include analyzing more complex statements, additional emotions, and improving overall accuracy.

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.