emotion-classification-sentiment-analysis (ECSA)

A text contains sentiments and emotions of the typist. Here the project is meant to develop in order to identify those. Therefore the project , can be called as mini research, is known as Emotion classification and sentiment analysis of the text. It is a Jupyter Notebook based project.

Technology Review

1. Elicitation and data gathering.
2. Design and development of Algorithm.
3. Training of the model.
4. Testing of the model.
5. Conclusion.

Goals

1. Develop an ML based model to perform sentiment analysis and emotion classification.
2. Achieve accuracy in sentiment analysis and emotion detection.
3. Perform Emotion classification on short messages and collection of texts.

Specifications

The basic technology stack for the project is mentioned below:

1. Jupyter Notebook - An open source to create and share documents that contains live code.
2. Numpy - To deal with numerical analysis.
3. Scipy - An ecosystem for mathematics, science and engineering.
4. Pandas - Any of the package to handle csv files
5. Scikit-learn - To utilize the ML based tools to create a model.
6. Matplotlib - To visualize the data in form figures and graphs.
7. NLTK - Natural language toolkit for natural language processing.
8. Python >=3.6.5 - Programming Language.