Sentiments

Open up smile in sentiments/. Suffice it to say that file’s name doesn’t end in .py, even though the file contains a program written in Python. But that’s okay! Notice the "shebang" atop the file:

That line tells a computer to interpret (i.e., run) the program using python3 (aka python on CS50 IDE), an interpreter that understands Python 3.

Notice next that the program imports a class called Analyzer from a module called analyzer as well as a function called colored from a module called termcolor. The former you’ll actually soon implement in a file called analyzer.py. (Recall that a class in Python is like a struct in C except that a class can also contain functions, otherwise known as "methods" when they’re inside a class.) The latter colorizes output in terminal windows, as we’ll soon see.

This program defines only one function, main, which gets called per the file’s last line. Within main, we first make sure that sys.argv contains the expected number of command-line arguments. We then "instantiate" (i.e., allocate) an Analyzer object. We then pass to that object’s analyze method the word that a user has provided in sys.argv[1]. As we’ll soon see, that method will return a positive int if its input is positive, a negative int if its input is negative, and 0 if its input is neither positive nor negative. The program ultimately prints a colored smiley accordingly.

Open up anlyzer.py in sentiments/. Not much going on in there (yet)! Notice, though, that it imports the Natural Language Toolkit, among whose features is a tokenizer that you can use to split a tweet (which is maximally a 140-character str object) into a list of words (i.e., shorter str objects).

In there is our definition of that Analyzer class, which has two methods: __init__, which is called whenever Analyzer is instantiated; and analyze, which can be called to analyze some text. That first method takes two arguments in addition to self: positives, whose value is the path to a text file containing positive words; and negatives, whose value is the path to a text file containing negative words. Meanwhile, analyze takes one argument in addition to self: a str to be analyzed for sentiment. Though that function is (temporarily) hardcoded to return 0 no matter what.

Recall that methods are automatically passed that first reference to self so that they have a way of referring to objects' "instance variables."

Open up positive-words.txt and negative-words.txt (without changing them). Notice that atop each file is a bunch of comments, each of which starts with a ;. (Those are just text files, though, so the authors' choice of ; is arbitrary.) The lists of positive and negative words, respectively, begin below those comments, after a blank line.

Open up tweets. Ah, another shebang. But nothing else besides a TODO! More on that soon.

Open up helpers.py. You should see two functions: chart and get_user_timeline. Given three values (positive, negative, and neutral, each an int or a float), chart generates HTML (as a str) for a pie chart depicting those values. Given a screen name, meanwhile, get_user_timeline returns a list of tweets (each as a str). That function uses Twython (har har), a library for Python, to retrieve those tweets via Twitter’s API (application programming interface), a free service that can be queried programmatically for tweets. Notice how the function expects two "environment variables" to exist. Environment variables exist within your terminal window, key/value pairs that programs (like tweets) can access programmatically. We’ll soon use two, API_KEY and API_SECRET, to store credentials for Twitter.

Open up application.py. In this file is a "controller" for a Flask-based web app with two endpoints: / and /search. The first displays the simplest of forms via which you can search for a user on Twitter by screen name. The second displays one of those pie charts categorizing that user’s tweets. Notice, though, how 100% of those tweets are (temporarily) assumed to be neutral.

Open up templates/index.html. In there is that simplest of forms. Notice how it figures out via url_for, a function that comes with Flask, to what URL the form should be submitted.

Open up templates/search.html. Notice how this template renders a user’s screen name as well as that pie chart.

Open up templates/layout.html. In here is a layout on which index.html and search.html depend. It leverages Bootstrap to override browsers' default aesthetics.

Open up requirements.txt (without changing it, though you can later if you’d like). This file specifies the libraries, one per line, on which all of this functionality depends.