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.

Visualize word frequency with a word cloud where word size reflects frequency in the tweets dataset. Clean text and highlight the top 20 terms to support 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.

Natural language processing is a subfield of linguistics, computer science and AI that enables computers to process large natural language data using supervised, unsupervised, and deep learning.

Develop skills in text preprocessing with nltk, performing tokenization, stopword removal, and stemming or lemmatization, and comparing algorithms like Lancaster stemming, to prepare data for sentiment analysis.

Explore the lexicon-based approach to sentiment analysis, using dictionaries of positive and negative words to assign sentiment values to phrases, with negation and intensification considered, and note Textbook and VEDO.

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.

Harness the Vader lexicon-based sentiment analysis approach within machine learning, exploring polarity, compound scores, and emoji handling through practical demos.

Analyze sentiment of downloaded tweets using a lexicon-based approach by cleaning duplicates and tokenizing with a tweet tokenizer. Apply custom Vader and TextBlob functions to compute polarity.

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.

Learn to clean and tokenize tweets, create labeled positive and negative data, and prepare features for a RandomForest sentiment classifier using a lexicon and part-of-speech information.

Build a random forest model for sentiment analysis using supervised learning, train/test splits, and idf-weighted tf-idf features to classify tweets as positive or negative.

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.

Extend a lexicon-based sentiment analyzer by loading a classifier and vectorizer to run a sentiment test on tweets. Prepare to compare accuracy across three approaches.

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.