Natural Language Processing with Python and NLTK

Master text wrangling by cleaning and preprocessing data—tokenization, stemming, and stop-word removal—to turn text into numbers for NLP, using CSV and JSON in Python on Colab.

Master text cleansing for diverse data, removing HTML tags, non-ASCII characters, and mentions. Extract clean text from books, tweets, emails, and XML using Python tools like requests and BeautifulSoup.

Learn to split large texts into sentences with NLTK's sentence tokenizer, download models, and train a custom tokenizer on your text to handle nonstandard punctuation for better accuracy.

Split sentences into words using spaces and punctuation to enable algorithms to work with words, download models for tokenization, and explore regex tokenizers for contractions and punctuation.

Explore how stemming reduces words to their roots, using Porter Stemmer, Lancaster Stemmer, and Snowball Stemmer to normalize tokens like walking, walk, and walks for efficient search indexing.

Compare stemming and lemmatization using Python's NLTK and WordNet, showing how mice become mouse and running becomes run through morphological analysis and POS-aware lemmatization.

Compare stemming and lemmatization to show that both yield base forms, yet stemming often produces nonword stems while lemmatization yields real words via morphology, dictionaries, and corpus context.

Remove stop words to improve information retrieval and classification. Use tokenized sentences and the nltk stop words list for preprocessing to build clean, discriminative features.

Explore part of speech tagging with nltk, including tag representations and named entity tagging. Examine preprocessing with tokenization, and why stop word removal affects tagging in practice.

Explore the nltk pos tagger by tokenizing a sentence and applying pos_tag to assign grammatical roles, generating tagged tokens (e.g., prp for pronoun and noun) and printing the results.

Learn sequential tagging using unigram, bigram, and trigram contexts, comparing pretrained and self-trained models, and grasp training, testing, and evaluating to build accurate natural language processing models.

Explore building a sequential tagger in Python with NLTK on the Brown corpus, using a 90/10 train–test split, tokenization, and backoff with a default tagger.

Learn named entity recognition with NLTK and the ne_chunk method to tag person, organization, and geopolitical entities such as California, with binary and type-tag outputs and practical coding steps.

Practice tokenizing sentences and words, lemmatization with pos tagging, named entity recognition on the Stanford movie review dataset, removing stop words and punctuation to prepare data for natural language processing.

Explore topic modeling, an NLP technique that clusters long texts into topics using NLTK preprocessing, tokenization, POS tagging, and lemmatization, then visualize topics with Gensim's LDA and pyLDAvis.

Learn to summarize a Wikipedia article on artificial intelligence using Python and NLTK: tokenize sentences, remove stop words, compute word frequencies and sentence weights, and assemble top sentences.

We build a sentiment analysis app using Vader in NLTK, tokenize sentences, calculate polarity scores, and classify sentences as negative, neutral, or positive for text like tweets.

Compare NLTK and SpaCy by examining algorithms, language support, and return objects; NLTK returns strings or lists, while SpaCy yields a document with tokens and tags.