Assignment - 7

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Text classification is a natural language processing (NLP) task that involves categorizing text documents into predefined classes or categories. It is widely used in various applications such as sentiment analysis, spam detection, topic classification, and language identification.
The process of text classification typically involves the following steps:

• Data Collection: Gathering a dataset containing text documents along with their corresponding labels or categories.
• Text Preprocessing: Cleaning and preprocessing the text data by removing noise, such as HTML tags, punctuation, and stopwords, and performing tasks like tokenization, stemming, and lemmatization.
• Feature Extraction: Converting the preprocessed text data into numerical or vector representations suitable for machine learning algorithms. Common techniques for feature extraction include bag-of-words, TF-IDF (Term Frequency-Inverse Document Frequency), word embeddings (such as Word2Vec or GloVe), and more advanced techniques like BERT embeddings.

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