NLP Tools and Libraries

Text are unstructured data that needs to be structured while carrying out any processing or analysis on them. To achieve this, there are varieties of tools, both libraries and cloud-based applications, that are available for different tasks in the NLP pipeline. Since we can't go throuh all these tools, we'll focus on 2 popular libraries - NLTK and Spacy, and a cloud-based solution - Amazon comprehend.

NLTK

Natural Language Toolkit (NLTK) is a powerful and widely used Python library for working with human language data. It provides tools, algorithms, and resources that enable us to perform various NLP tasks, ranging from basic text processing to more advanced linguistic analysis.

Spacy

SpaCy is another popular Python library designed specifically for natural language processing tasks. It's known for its speed, efficiency, and ease of use, making it a favorite among developers and researchers working with large amounts of text data. SpaCy provides a streamlined API for various NLP tasks, allowing users to quickly process and analyze text without the need for extensive configuration.

One of the key features of SpaCy is its pre-trained models that can perform tasks like tokenization, part-of-speech tagging, named entity recognition, and syntactic parsing.

Amazon comprehend

Amazon Comprehend is a cloud-based service provided by Amazon Web Services (AWS). It's designed to help us analyze and gain insights from text data in a scalable and efficient manner.

One of the advantages of Amazon Comprehend is that it's a managed service, which means AWS takes care of the underlying infrastructure, making it easier to incorporate NLP capabilities into applications without worrying about the technical details. It can perform tasks such as...

• Sentiment analysis
• Entity recognition
• Keyphrase extraction
• Language detection
• Topic modeling.

This means it can automatically determine the sentiment (positive, negative, neutral) expressed in a piece of text, identify entities (like names, dates, and locations), extract key phrases that summarize the content, detect the language the text is written in, and uncover the main topics discussed in the text.

➡️ Next, we'll look at Text preprocessing... 🎯.