Why Text Needs to Be Converted into Numbers
Machine learning algorithms cannot directly understand text. They can only process numerical data. Therefore, before training a model, we must convert the review text into numerical features.
There are several techniques for converting text into numbers, such as:
- Bag of Words (BoW)
- TF-IDF (Term Frequency-Inverse Document Frequency)
- Word2Vec
- GloVe
- BERT Embeddings
In this project, we will use TF-IDF, one of the most popular and effective techniques for text classification.
Why Use TF-IDF?
TF-IDF helps identify the importance of words in a review by assigning higher weights to words that are frequent in a specific review but less common across all reviews.
Advantages of TF-IDF
- Reduces the importance of common words.
- Highlights meaningful words.
- Improves classification accuracy.
- Efficient for text classification tasks.
- Widely used in Natural Language Processing.
Fake Review Detection System using Machine Learning
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