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Emotion Recognition In Text

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EMOTION RECOGNITION IN TEXT

GOAL

To develop a model that can analyze text data and classify the emotions expressed (e.g., happiness, sadness, anger).

DATASET

https://www.kaggle.com/datasets/pashupatigupta/emotion-detection-from-text

DESCRIPTION

To analyze the dataset of emotion detction from text and build,train the model on the basis of different features and variables.

Visualization and EDA of different attributes:

sentiment distribution

WHAT I HAD DONE

1.Load the dataset which contains about 40000 entires.

2.Checked for missing values and cleaned the data accordingly.

3.Analyzed the data, found insights and visualized them accordingly.

4.Plotting distribution graphs to find corelations.

5.Found detailed insights of different columns with target variable using plotting libraries.

6.Trained the datasets by different models and saves their accuracies into a dataframe.

MODELS USED

Random forest classifier as it shows high accuracy,versatility and scalability.

Gradient booster which is a strong algorithm for classification and regression problems

XGBClassifier help to improve machine-learning model's accuracy.

LIBRARIES NEEDED

Numpy

Pandas

Word cloud

Matplotlib

Seaborn

Scikit-Learn

Scipy

Xgboost

Tensorflow

Keras

VISUALIZATION

INCLUSION OF IMAGES OF THE VISUALIZATION IS MUST (RESULT OF EDA).

ACCURACIES

Random forest classifier Score = 1.0

Gradient booster Score = 1.0

XGBClassifier Score = 0.25

CONCLUSION

Random forest classifier and Gradient booste rmodels show promising performance .

XGBClassifier shows less accuracy

YOUR NAME

SRUJANA