Introduction to Python Text Processing

Text processing directly applies to natural language processing (NLP). NLP aims to process language communication between humans, whether spoken or written. This differs from communication between computers and humans, which occurs either through human-written programs or through gestures like clicking a mouse. NLP attempts to understand the natural language spoken by humans and to categorize, analyze, and, if necessary, respond to it. Python has a rich library that meets the needs of NLP. The Natural Language Tool Kit (NLTK) is one such library that provides the necessary functionality for NLP.

Below are some applications that use NLP and, indirectly, Python, NLTK.

Summary

Often, we need to obtain summaries of news articles, movie plots, or longer stories. These are written in human language, and without NLP, we must rely on a human to interpret and present these summaries. However, with the help of NLP, we can write programs using NLTK to summarize long texts based on various parameters, such as the desired percentage of the text in the final output and the selection of positive and negative words for the summary. Online news feeds rely on this summarization technique to present news insights.

Voice-Based Tools

Voice-based tools like Apple’s Siri and Amazon’s Alexa rely on NLP to understand human interactions. They have large training datasets for interpreting and processing human questions or commands. Although voice is involved, it is indirectly converted into text, which is then processed by an NLP system to produce the results.

Information Extraction

Web page scraping is a common example of using Python code to extract data from web pages. While it may not be strictly NLP-based, it does involve text processing. For example, if we only need to extract the title present in an HTML page, we can search the page structure for h1 tags and find a way to extract the text between these tags. This requires a Python text processing program.

Spam Filtering

By analyzing the text in the subject line and email content, spam can be identified and eliminated. Since spam emails are often sent in bulk to many recipients, even small variations in the subject and content can be matched and marked as spam. This again requires the use of the NLTK library.

Language Translation

Computerized language translation relies heavily on NLP. As more and more languages are used online, it becomes necessary to automatically translate from one human language to another. This involves programming that handles the vocabulary, grammar, and contextual tags of the languages involved. Again, NLTK is used to handle such needs.

Sentiment Analysis

To determine the overall reaction to a movie’s performance, we might need to read thousands of audience feedback posts. However, this can also be automated by using a classification of positive and negative feedback and sentence analysis. The overall sentiment of the audience can then be determined by measuring the frequency of positive and negative comments. This obviously requires analyzing the human language written by the audience, and NLTK is heavily used here to process the text.