TEXT CLASSIFIER

The goal of text classification is to categorize text documents into different classes. This is an extremely important analysis technique in NLP. We will use a technique, which is based on a statistic called tf-idf, which stands for term frequency—inverse document frequency. This is an analysis tool that helps us understand how important a word is to a document in a set of documents. This serves as a feature vector that's used to categorize documents.

SENTIMENT OF A SENTENCE

Sentiment analysis is one of the most popular applications of NLP. Sentiment analysis refers to the process of determining whether a given piece of text is positive or negative. In some variations, we consider "neutral" as a third option. This technique is commonly used to discover how people feel about a particular topic. This is used to analyze sentiments of users in various forms, such as marketing campaigns, social media, e-commerce customers, and so on.

IDENTIFYING THE GRNDER

Identifying the gender of a name is an interesting task in NLP. We will use the heuristic that the last few characters in a name is its defining characteristic. For example, if the name ends with "la", it's most likely a female name, such as "Angela" or "Layla". On the other hand, if the name ends with "im", it's most likely a male name, such as "Tim" or "Jim". As we are sure of the exact number of characters to use, we will experiment with this.