Python for Natural Language Processing (NLP)

Explore the pandas library for data manipulation with data frames, and learn to read CSV and JSON files, using head, tail, shape, and drop duplicates for data cleaning.

Master pandas part 2 by working with dataframes, handling missing values with drop and imputation options, and using info, describe, sort, rename, and time series conversions.

Learn to work with strings in Python by creating and printing them, indexing with square brackets, looping over characters, and checking length and membership with len and in.

Explore more complex Python string methods in this text methods part 2 lecture for NLP, including capitalize, casefold, count, endswith, find, format, islower, and isnumeric, with practical examples.

Explore Python string methods: replace to alter multiple words in one line with an optional limit, and the space method to detect and remove empty entries in data.

Explore spaCy's English pipeline, load the small model, and perform tokenization to split text into tokens, then visualize token relationships and part-of-speech tags with adjustable distance.

Learn to perform part-of-speech tagging and named entity recognition with spacey, exploring tokenization, POS codes, and entity labels such as person, date, and location.

Use train_test_split in Python to divide data into training and testing sets, create x and y variables, train models on the training set, and evaluate predictions on the test set.

Explore how a confusion matrix visualizes classifier performance by detailing true positives, false positives, true negatives, and false negatives with an illness detection example, and compute accuracy in Python.

Import pandas and read the Spotify dataset with read_csv, data analysis with ratings and reviews; inspect with head and tail, then drop unused columns for sentiment analysis and text classification.

Identify and clean the data by removing duplicates, checking nulls and empty entries. Create a rating-based sentiment label, apply it, and compare positives and negatives.

Perform sentiment analysis with vader lexicon from the natural language toolkit, reading a CSV file, removing duplicates, and labeling ratings as positive or negative with a scores column.

Create a compound column by applying compound scores per row with a lambda function, label reviews as positive or negative, and evaluate accuracy with the accuracy score under threshold choices.

Apply text classification with natural language processing by labeling text as positive or negative and building a cross-validated pipeline using tf-idf vectorization for emails and messages.

Build a pipeline with tf-idf vectorization and a linear support vector classifier, train with x_train and y_train, evaluate on x_test, and achieve about 83% accuracy.

Learn data analysis in NLP by using pandas to check for empty entries, clean data, and create a positive/negative rating column applied to the dataframe; observe mainly positive counts.

Build a text classification model with a scikit-learn pipeline, tf-idf vectorizer, and a linear support vector classifier, using a train/test split and achieving 0.90 accuracy.

Analyze movie reviews from IMDB with polar discourse to assign compound scores, perform sentiment analysis, and build a text classification model to predict review sentiment using pandas in Python.

Explore sentiment analysis in Python using native language toolkits and the sentiment intensity analyzer to compute negativity, neutral, positivity, and compound scores, then apply them to a dataset for labeling.

Explore text classification to separate positive and negative reviews using a pipeline with a linear support vector classifier, evaluate with a train/test split, and visualize accuracy and the confusion matrix.