Text Mining and Natural Language Processing in Python

Understand what makes natural language natural and the challenges machines face, including contextual words, pronunciation-based meaning differences, and building a text classification model for sentiments, irony, and sarcasm.

Explore how natural language processing enables machines to understand and process human language, driving applications like machine translation, speech recognition, question answering, text summarization, and sentiment analysis.

compare and contrast nltk and spacy, highlighting their design goals, language support, and features like tokenization, stemming, and word vectors for production use in text processing.

Tokenization splits unstructured text into sentences and words to enable computer processing, cleaning, and normalization, forming the basis for downstream NLP tasks like stemming.

Explore sentence and word tokenization in Python with spaCy, loading the English core model to produce sentence and token lists and understand punctuation as tokens.

Learn practical text cleaning and case conversions in Python by scraping Wikipedia content, removing HTML and noise with regular expressions, correcting spelling, and normalizing case.

Explore stemming and lemmatization, reducing suffixes to base forms to improve text classification and search results; discuss overstepping, false positives, false negatives, and contextual analysis in lemmatization.

Explore stemming and lemmatization in python with the STEM package, including tokenization and applying the stem function to words, while examining wordnet limitations and pos tagging.

Identify stopwords—words that carry little meaning—and learn to edit the corpus with Spacey and A.K.; note that negations may be removed, but must be preserved for sentiment analysis.

Learn to filter stop words in Python for text mining and natural language processing by creating and updating a stop words list, tokenizing sentences, and removing nonessential words.

Transform texts into vectors with the bag of words approach by building a vocabulary and counting word occurrences. Highlight limitations like sparsity and lost context.

Explore bag-of-words limitations and how bag-of-n-grams captures sequences, representing documents as feature vectors using n-gram vocabularies and discussing tradeoffs between context and training time.

Explore how to implement a bag of n-grams model in Python by tokenizing text, generating bigrams, and constructing a presence vector for each sentence.

Learn how word2vec trains word embeddings by using a skip-gram neural network to map words and their context into vector representations, with one-hot inputs and a hidden layer.

Learn to implement word2vec in Python with the Jensen package, tokenize sentences and words, use skip-gram with vector size and window, train a neural network, and compute cosine similarity.

Explore BERT, a bidirectional encoder from transformers, and how pre-training enables transfer learning for NLP tasks. See how tokenization and embeddings yield context-aware word representations for sentiment analysis.

Learn to generate bert embeddings in python by loading a model with tensorflow hub, tokenizing with bert tokenizer, creating token IDs and attention masks, and extracting last hidden state embeddings.

Explore how convolutional neural networks classify text and images by using convolution and max pooling to extract salient features from word embeddings for sentiment analysis.

Explore python modules for sentiment analysis: pandas for data handling, regex for cleaning, and TensorFlow with bert embeddings to build a movie review sentiment classifier.

Select the IMDb dataset from Cagle for sentiment analysis; 50,000 English movie reviews are labeled positive or negative with two columns: review and sentiment.

Remove line breaks, punctuation, numbers, and stray characters from movie reviews using a cleaning function. One-hot encode sentiments and prepare numeric inputs for neural networks, enabling word-embedding tokenization.

Tokenize text with BERT embeddings using TensorFlow hub, define the vocabulary and lowercase settings, test the tokenization and token IDs, and prepare reviews by converting to IDs for the model.

Explain how to build and train a text sentiment model with embedding, multiple convolutional layers, pooling, dense layers, and dropout, for binary or multi-class classification.

Test a sentiment model in Python by tokenizing two reviews, creating a test dataset, and predicting sentiment, showing the model correctly identifies positive and negative reviews.