How to use fastText for sentiment analysis on Linux?

To use fastText for sentiment analysis on Linux, you can follow these steps:

1. Install fastText: Install the fastText package on your Linux machine. You can download the package from the fastText GitHub repository or install it using a package manager such as apt.
2. Prepare the data: Prepare the data in fastText format, where each line is a single text document followed by a label that indicates the sentiment of the document (for example, 1 for positive and 0 for negative). You can use pre-labeled datasets such as the IMDB movie reviews dataset or label your own data.
3. Train the model: Train the fastText model on the labeled text data using the fasttext command-line tool. You can specify the training data file, the dimensionality of the word vectors, and other hyperparameters such as learning rate and epoch. For example, to train a sentiment analysis model with 100-dimensional word vectors, you can run:

fasttext supervised -input data.txt -output model -dim 100

1. Evaluate the model: Evaluate the model on a test set to see how well it performs in sentiment classification. You can use metrics such as accuracy, F1 score, and confusion matrix to evaluate the model’s performance.
2. Use the model for sentiment analysis: Use the trained model to predict the sentiment of new text documents by computing the word vectors for the documents and applying the model’s classification algorithm.

That’s it! With these steps, you should be able to use fastText for sentiment analysis on Linux.