nltk corpora Hope You Were Able to Explore the Datasets NLTK Offers Interactively: A Comprehensive Review of the Corpora NLTK Provides The demand for high-quality datasets grows as natural language processing (NLP) gains traction in the fields of computer science, linguistics, and data science. Large volumes of text or other linguistic data are necessary for NLP approaches. Corpora are the collective name for these digital collections. Another term you'll hear is corpus, which is corpora's single form. We shall examine the corpora that the Natural Language Toolkit (NLTK) offers in this article. By the end of this article, you should be able to describe what corpus means, list some datasets in the NLTK corpora, and recite the basic features of the movie reviews corpus in NLTK. NLTK is a Python library that provides tools for NLP tasks such as tokenization, stemming, and named entity recognition, among others. It also provides means to download some of these large datasets. NLTK is widely used by data scientists and linguists alike. Its extensive corpus of text data is one of the reasons why it is popular. The corpora contain a vast array of text data from various sources such as newspapers, books, movie reviews, and more. These corpora are essential for training models in NLP tasks such as sentiment analysis, text classification, and machine translation. One of the most well-liked datasets in NLTK is the corpus of movie reviews. 2000 movie reviews make up this corpus, with 50 percent favorable and 50 percent negative reviews. Two directories—one for good reviews and one for negative reviews—store each review as a distinct file. The average word count for the reviews is close to 800 words. To access the movie reviews corpus, you can import it from the nltk.corpus module using the following line of code: from nltk.corpus import movie_reviews Once you have imported the movie_reviews corpus, you can use the fileids() method to access a list of all the files available. You can then use the len() function to find the length of the list. reviews = movie_reviews.fileids() print(len(reviews)) The output of this code will be 2000, which is the total number of reviews in the corpus. You can use list comprehension to make two lists of favorable and unfavorable reviews in order to filter the reviews based on their sentiment. The code that follows makes two lists, one for favorable evaluations and one for unfavorable ones: pos_reviews = [r for r in reviews if r.startswith('pos/')] neg_reviews = [r for r in reviews if r.startswith('neg/')] Now that we have our positive and negative reviews in separate lists, we can access the raw text of any review using the raw() method. For example, the following code prints the first positive review: print(movie_reviews.raw(pos_reviews[0])) This will print the raw text of the first positive review. As you can see, the review is quite lengthy, and it can be challenging to work with this text as is. One way to make the text more manageable is to tokenize it. Tokenization is the process of breaking down text into smaller chunks such as words or sentences. NLTK provides a tokenizer that can tokenize text based on various rules. In the next video, we will talk more about this technique to tokenize any text like this and use it for natural language processing. In conclusion, the NLTK offers a sizable library of corpora that are critical for developing natural language processing training models. The corpus of movie reviews is just one of the many datasets offered by NLTK.