Twitter_data

Sentimental Analysis Using ML

Code Link and Output :- https://colab.research.google.com/drive/1sv7YKW62XG-NLvU8-LrQUeRGMnoBzx3Q?usp=sharing

Dataset Link : https://www.kaggle.com/datasets/kazanova/sentiment140

Tools used :- Google Collab

Language : Python

Machine Learning Model.

Twitter Sentiment Analysis using Machine Learning

This project focuses on sentiment analysis of tweets using machine learning techniques. It utilizes a dataset from Twitter to train a model that can classify tweets as positive or negative based on their content.

Steps to Run the Project

1. Setup Kaggle API:

   • Install the Kaggle library using pip: !pip install kaggle.
   • Upload your Kaggle API key (kaggle.json) file.
   • Configure the path of the kaggle.json file.

2. Download Dataset:

   • Use the Kaggle API to download the sentiment dataset: !kaggle datasets download -d kazanova/sentiment140.
   • Extract the dataset from the compressed file.

3. Data Processing:

   • Import necessary libraries and dependencies.
   • Load the dataset into a pandas DataFrame.
   • Check for missing values and data distribution.
   • Convert the target labels (4 to 1 for positive sentiment).
   • Perform text preprocessing including stemming to reduce words to their root form.

4. Splitting Data:

   • Split the dataset into training and testing data using train_test_split.

5. Feature Extraction:

   • Convert textual data into numerical data using TF-IDF vectorization.

6. Training the Model:

   • Utilize Logistic Regression for sentiment classification.
   • Train the model on the training data.

7. Model Evaluation:

   • Evaluate the model's accuracy on both training and testing data.

8. Saving the Model:

   • Save the trained model using pickle for future use.

9. Predictions:

   • Use the saved model to make predictions on new data (tweets).

Model Details

• Algorithm Used: Logistic Regression
• Accuracy on Training Data: 77.8%
• Framework/Libraries Used: Python (NumPy, pandas, scikit-learn, NLTK)

File Structure

• Twitter Sentiment Analysis using ML.ipynb: Google Collab Notebook containing the project code.
• trained_model.sav: Saved trained model for future predictions.

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