Natural Language Processing with nltk We will concentrate on Natural Language Processing in this lecture using the well-known Python library NLTK. By the end of this essay, you need to be able to define NaturalLanguage Processing, or NLP, give a few instances of NLP in practical applications, anddefine NLTK. Data science research in the area of natural language processing, or NLP, focuses on how computers and human language interact. Because of the ambiguity of human language,algorithms have a difficult time deciphering what someone is saying in context. NLP aims tocreate and enhance algorithms and data techniques that enable efficient and speedyunderstanding of natural language. NLP has many practical applications in daily life. For instance, NLP techniques are used by speech recognition systems like Siri, Google Now, and Alexa. These computers continuallyincrease their accuracy as they learn what and how people speak over time. Similar to this,automatic translators like Google Translate and Facebook's automatic translation of statusesuse NLP approaches that consider context by looking at the words around the text they aretranslating in addition to words and phrases. Another example of NLP is chatbots that can answer questions via Facebook Messenger. These chatbots use NLP engines to process the questions, categorize them, and matchthem to existing answers. We'll walk through a notebook that makes use of NLTK for natural language processing in this tutorial. The most well-known Python NLP package, NLTK, offers modules for importing,cleaning, and preprocessing text data in human language before applying computationallinguistics or machine learning algorithms to these datasets, such as sentiment analysis. To get started with our notebook, please locate the notebook called "Natural Language Processing of Movie Reviews Using NLTK" in your Freegate folder. In the notebook's firstline, we import NLTK, the Natural Language Processing toolkit. NLTK offers over 50 datasetsto start working with, including the movie database we will use in our example notebook. To download the movie reviews dataset, we will use the NLTK download function. After executing the function, the dataset will be copied into your home folder. You can find thisfolder if you wish to. You can also download all the other datasets or pick a few to downloadinteractively by typing NLTK download. In NLP, a corpus is a collection of texts that have been pre-processed and annotated with linguistic information. NLTK provides several corpora that we can use to train our models.One of the most popular corpora is the Brown Corpus, which contains text from a variety ofgenres, such as news, fiction, and academic writing.
We will utilize the movie reviews corpus, which comprises movie reviews from the website IMDb, in our example notebook. This corpus is simple for us to work with because it hasalready been pre-processed and annotated. To import the corpus into our notebook, we will use the NLTK corpus module. The first thing we need to do is download the corpus using the following code: import nltknltk.download('movie_reviews') Once the corpus is downloaded, we can import it into our notebook using the following code: from nltk.corpus import movie_reviews