This repository contains code and resources for performing sentiment analysis using Natural Language Processing (NLP) techniques on tweet data. The project aims to analyze the sentiment expressed in tweets and classify them into positive, negative, or neutral categories.

The dataset used for training and testing the sentiment analysis model is included in this repository. However, The dataset should be in a CSV format with two columns: "text" containing the tweet text and "label" containing the corresponding sentiment label.

To run the code in this repository, you need to have the following dependencies installed:

Python 
NLTK 
scikit-learn 
Pandas 
NumPy

You can install the required packages using pip:

pip install nltk scikit-learn pandas numpy

The tweet data needs to be preprocessed before training the sentiment analysis model. The preprocessing steps include tokenization, removal of stopwords, stemming or lemmatization, and vectorization of text data.

To train the sentiment analysis model, follow these steps:

Download the dataset from source and place it in the data/ directory.

Adjust the configuration parameters in the config.py file, such as the dataset path, preprocessing options, and model selection.

Run the Sentimental Analysis using Natural Language Processing.ipynb script to preprocess the data and train the sentiment analysis model:

python Sentimental Analysis using Natural Language Processing.ipynb

After training, the trained model will be saved in the saved_models/ directory.

To evaluate the performance of the sentiment analysis model, the accuracy and other relevant metrics can be calculated. The evaluation results will be displayed in the console.

To make sentiment predictions on new tweet data, follow these steps:

Place the tweet data in a CSV file with a "text" column containing the tweet text.

Adjust the configuration parameters in the config.py file, such as the path to the saved model and preprocessing options.

Run the Untitled.ipynb script to load the trained model and generate predictions for the tweet data:

python Untitled.ipynb

The predictions will be saved in a CSV file with an additional "label" column indicating the predicted sentiment.

Sentiment Analysis using Natural Language Processing on Tweet Data This repository contains code and resources for performing sentiment analysis using Natural Language Processing (NLP) techniques on tweet data. The project aims to analyze the sentiment expressed in tweets and classify them into positive, negative, or neutral categories.

Dataset The dataset used for training and testing the sentiment analysis model is not included in this repository. However, it can be obtained from source. The dataset should be in a CSV format with two columns: "text" containing the tweet text and "label" containing the corresponding sentiment label.

Requirements To run the code in this repository, you need to have the following dependencies installed:

Python (version X.X.X) NLTK (version X.X.X) scikit-learn (version X.X.X) Pandas (version X.X.X) NumPy (version X.X.X) You can install the required packages using pip:

shell Copy code pip install nltk scikit-learn pandas numpy Preprocessing The tweet data needs to be preprocessed before training the sentiment analysis model. The preprocessing steps include tokenization, removal of stopwords, stemming or lemmatization, and vectorization of text data.

Model Training To train the sentiment analysis model, follow these steps:

Download the dataset from source and place it in the data/ directory. Adjust the configuration parameters in the config.py file, such as the dataset path, preprocessing options, and model selection. Run the train_model.py script to preprocess the data and train the sentiment analysis model: shell Copy code python train_model.py After training, the trained model will be saved in the saved_models/ directory. Model Evaluation To evaluate the performance of the sentiment analysis model, the accuracy and other relevant metrics can be calculated. The evaluation results will be displayed in the console.

Prediction To make sentiment predictions on new tweet data, follow these steps:

Place the tweet data in a CSV file with a "text" column containing the tweet text. Adjust the configuration parameters in the config.py file, such as the path to the saved model and preprocessing options. Run the predict_sentiment.py script to load the trained model and generate predictions for the tweet data: shell Copy code python predict_sentiment.py The predictions will be saved in a CSV file with an additional "label" column indicating the predicted sentiment. License The code and resources in this repository are available under the MIT License.

The dataset used in this project is sourced from Kaggle.

The NLTK library is used for text preprocessing. Please refer to the NLTK documentation for more details.

[https://journalofbigdata.springeropen.com/articles/10.1186/s40537-015-0015-2]

For any questions or inquiries, please contact [[email protected]].