1. Wrap up
Well done! This is our final video! You've learned the basics of performing a sentiment analysis using Python. We covered a lot of material in a short period of time, so you should be proud of yourself.
2. The Sentiment Analysis world
We defined the three parts of a sentiment analysis system: sentiment/emotion of an opinion holder; the subject being talked about, and an opinion holder.
3. Sentiment analysis types
We classified the sentiment analysis algorithms as rule-based or automated.
The rule or lexicon based methods had a predefined list of words with a positivity score. The algorithm matches the words from the lexicon to the words in the text.
Automated systems are based on machine learning. Using historical data with known sentiment, we predict the sentiment of a new piece of text. This is what we did in this course.
4. The automated sentiment analysis system
In our sentiment analysis process, we followed 3 steps: after exploration of the data, we built new features related to the sentiment column. We then transformed it to numeric features. And finally, we built a machine learning model that classifies the sentiment.
5. Congratulations!
Thank you for the time and effort you dedicated to this course! I wish you the best of luck in your further learning.