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readme.txt
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Emotion_Intensity_Predictor
The Emotion Intensity Predictor is a Natural Language Processing (NLP) project aimed at predicting the intensity of emotions expressed in textual data.
By leveraging advanced NLP techniques, the model aims to provide insights into the strength or magnitude of various emotions conveyed in the text.
Requirements:
1. Python 3.x
2. Jupyter Notebook
3. Python Libraries - Numpy, sklearn, tensorflow , pandas , matplotlib , seaborn , nltk
Installation
1. Clone the Repository
git clone https://github.com/Rishi-Jain2602/Emotion_Intensity_Predictor.git
2. Install the Project dependencies
pip install -r requirements.txt
Model Training
Knowlege Required
1. Natural Language Processing (NLP)
2. Python Libraries - Numpy, sklearn, tensorflow , pandas , matplotlib , seaborn , nltk
3. Machine Learning Algorithm - SVM , Random Forest classifier , XGB Classifier
Tools
- Vs code , Codelab
A) Machine Learning Model (Model1)
Sentence Embeddings using Sentence Transformers
Library Used:
Sentence Transformers(Hugging Face)
Model Used
SVM - It's accuracy is 77.32%
XGB Classifier- It's accuracy is 71.33%
Random Forest Classifier is 67.88%
Classification Reports of the models
https://github.com/Rishi-Jain2602/Emotion_Intensity_Predictor/blob/main/README.md#classification-reports-of-the-models
SVM is giving better result in comparison to other two models
Few Test Cases
https://github.com/Rishi-Jain2602/Emotion_Intensity_Predictor/blob/main/README.md#few-test-cases
B) Deep Learning (Model2)
Classification Report
https://github.com/Rishi-Jain2602/Emotion_Intensity_Predictor/blob/main/README.md#classification-report
It's accuracy was coming out ot be 84%
Note
1. Make sure you have Python 3.x installed
2. It is recommended to use a virtual environment to avoid conflict with other projects.
3. For deep learning, a laptop with a powerful GPU, a high-performance CPU, at least 8GB of RAM, a fast SSD, and an efficient cooling system is recommended.
4. If you encounter any issue during installation or usage please contact rishijainai262003@gmail.com or rj1016743@gmail.com