> For the complete documentation index, see [llms.txt](https://opencampus.gitbook.io/opencampus-machine-learning-program/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://opencampus.gitbook.io/opencampus-machine-learning-program/courses/archive/gan/week-1-1.md).

# Week 2 - Sentiment Analysis with Logistic Regression

For this week you should have gone through the lectures of week 1 of the first Coursera course on NLP, including the quiz and the assignment. <https://www.coursera.org/learn/classification-vector-spaces-in-nlp/home/week/1>

Let's start and dive into the fascinating world of NLP.

For an introduction we will look at how NLP was done a few years ago so that we can appreciate in the following courses what has changed in natural language processing for the better.

As a start we will do positive and negative sentiment analysis on Twitter tweets. :slight\_smile: Analyzing tweets is a huge topic in NLP. Hegdefonds use Twitter tweets to try to predict movement of stock prices, politician campaign managers analyze tweets to see how the sentiment about their candidate evolves. So we dive right to the heart of one of the use of NLP.

In this week we will count for each word in the dictionary how often it appears and positive respective negative tweets. We will use that dictionary to produce our input to a simple linear regression model and train it. *So we won't use the words as the input to the model what we will do in later weeks*. See the course videos for more details.

For the next week you should go through all the course videos, the assignment and the quiz of week 2 of course 1 in the NLP specialization. Take notes and notice if you have any questions about the material. In the next meeting we will discuss these.

<https://www.coursera.org/learn/classification-vector-spaces-in-nlp/home/week/2>

See you next week!


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