Text classification is one of the core tasks in Natural Language Processing (NLP). It involves categorizing text documents into predefined classes or categories based on their content. Text classification has wide-ranging applications such as spam filtering, sentiment analysis, and topic categorization.

In order to perform text classification, we need to follow a series of steps. The first step is data preprocessing, where we clean and prepare the text data for further analysis. This includes removing punctuation, stopwords, and performing lowercase conversion. After preprocessing, we tokenize the text, which involves breaking it down into individual words or tokens.

Bag-of-Words (BoW) is a common technique used for text classification. It represents the text documents as a collection of tokens, disregarding their order. BoW creates a matrix representation of the documents, where each row corresponds to a document and each column corresponds to a unique word in the corpus. The values in the matrix indicate the frequency or presence of the word in the document.

Another popular technique for text classification is TF-IDF (Term Frequency-Inverse Document Frequency). TF-IDF takes into account the importance of a word in the document as well as its rarity in the entire corpus. The TF-IDF score for a word is higher if it appears frequently in the document but rarely in other documents of the corpus.

Word embeddings are a more advanced technique for text classification. They represent words as dense vectors in a high-dimensional space, capturing their semantic relationships. Popular word embedding models include Word2Vec and GloVe.

By utilizing these techniques, we can train classification models that can effectively categorize text documents. With the increasing amount of textual data available, the demand for text classification in various domains is growing.

Keep exploring the fascinating world of text classification with NLP and unlock the power of understanding and analyzing textual data! Happy learning!