Machine learning and Lexicon approach to Sentiment analysis

Install and set up the Anaconda distribution to access Python libraries, then launch and customize a Jupyter notebook with themes, font sizes, and dark mode.

Discover how to set up a Twitter developer account and obtain an API key to access Twitter data for sentiment analysis using machine learning and lexicon methods.

Learn to access and authenticate APIs with Python, then fetch tweets via the Twitter API for sentiment analysis using a lexicon approach.

Download five hundred english tweets from the Twitter API, store them in a dataframe with time, username, tweet text, retweets, and followers, and save the data for easy analysis.

Learn practical text cleaning for tweets using regular expressions, lowercase conversion, and removal of links, numbers, symbols, and emojis, via a custom cleaning function for sentiment analysis.

Explore sentiment analysis with lexicon based and machine learning approaches to extract polarity and score text from reviews and social networks for advertising campaigns, political campaigns, and stock analyses.

Explore TextBlob, a Python library for natural language processing, to perform sentiment analysis using polarity, subjectivity, and lexicon-based intensity and confidence, with noun phrase and POS tagging.

Compare lexicon-based sentiment analysis with text blob results on tweets, noting where they diverge. Visualize positive, negative, and neutral distributions with pie charts.

Learn machine learning fundamentals and random forest ensembles, using multiple decision trees for classification. Understand entropy, information gain, bias and variance, and apply to sentiment analysis with natural language processing.

Clean and consolidate a random forest sentiment classifier by organizing imports, downloading data, building reusable clean, prepare, and train functions, then save the trained vectorized model for reuse.

Compare lexicon-based and machine learning approaches using labeled data from NLTK to assess accuracy; load data, build features, and test with a random forest and sentiment intensity analyzer.