Project 2: Twitter Trends

What do people tweet?
Draw their feelings on a map
to discover trends.


In this project, you will develop a geographic visualization of Twitter data across the USA. You will need to use dictionaries, lists, and data abstraction techniques to create a modular program. This project uses ideas from Sections 2.1, 2.2, 2.3, and 2.4, of the Composing Programs online textbook.

The map displayed above depicts how the people in different states feel about Texas. This image is generated by:

  1. Collecting public Twitter posts (tweets) that have been tagged with geographic locations and filtering for those that contain the "texas" query term,
  2. Assigning a sentiment (positive or negative) to each tweet, based on all of the words it contains,
  3. Aggregating tweets by the state with the closest geographic center, and finally
  4. Coloring each state according to the aggregate sentiment of its tweets. Red means positive sentiment; blue means negative.

The details of how to conduct each of these steps is contained within the project description. By the end of this project, you will be able to map the sentiment of any word or phrase. The archive contains all the starter code and all data (81 MB).

The project uses several files, but all of your changes will be made to the first one.

A starter implementation of the main project file.

Geographic positions, 2-D projection equations, and geographic distance functions.

Functions for drawing maps.

Functions for loading Twitter data from files.

A simple Python graphics library.

Utility functions for 61A.

Test code you can run yourself.

Utility functions for grading.

The data directory contains all the data files needed for the project, and it's necessary to run the project. The archive contains this directory: download it to get started. Downloading each file individually is error-prone.


This is a one-week project. You'll work in a team of two people, and you can complete all problems together with your partner.

Start early! Feel free to ask for help early and often. The course staff is here to assist you, but we can't help everyone an hour before the deadline. Piazza awaits. You are not alone!

In the end, you and your partner will submit one project. There are 15 possible points (12 for correctness and 3 for composition). You only need to submit the file You do not need to modify any other files for this project. To submit the project, change to the directory where the file is located and run submit proj2.

The Autograder

We've included an autograder which includes tests for each question. You can invoke it for a particular question number as follows:

python3 -q <question number>

You can also invoke the autograder for all problems at once using:


Debugging Tips

Phase 1: The Feelings in Tweets

In this phase, you will create an abstract data type for tweets, split the text of a tweet into words, and calculate the amount of positive or negative feeling in a tweet.


First, we will define an abstract data type for tweets. To ensure that we do not violate abstraction barriers later in the project, we will create two different representations:

(A) The constructor make_tweet returns a Python dictionary with the following entries:

  'text':      a string, the text of the tweet, all in lowercase
  'time':      a datetime object, when the tweet was posted
  'latitude':  a floating-point number, the latitude of the tweet's location
  'longitude': a floating-point number, the longitude of the tweet's location

(B) The alternate constructor make_tweet_fn returns a function that takes a string argument that is one of the keys above and returns the corresponding value.

Problem 1 (1 pt). Implement the missing selector and constructor functions for these two representations: tweet_text, tweet_time, tweet_location correspond to representation (A); make_tweet_fn corresponds to representation(B). For tweet_location you should return a position object. The constructors and selectors for this abstract data type can be found in Remember to preserve data abstraction!

The two representations created by make_tweet and make_tweet_fn do not need to work together, but each constructor should work with its corresponding selectors. The doctests for make_tweet and make_tweet_fn ensure that this is the case. They can be run along with other tests using:

python3 -q 1

Next, we will retrieve the words from a tweet and compute their sentiment.

Problem 2 (2 pt). Improve the extract_words function as follows: Assume that a word is any consecutive substring of text that consists only of ASCII letters. The string ascii_letters in the string module contains all letters in the ASCII character set. The extract_words function should list all such words in order and nothing else.

When you complete this problem, tests for question 2 should pass:

python3 -q 2

Problem 3 (1 pt). Implement the sentiment abstract data type, which represents a sentiment value that may or may not exist. The constructor make_sentiment takes either a numeric value within the interval -1 to 1, or None to indicate that the value does not exist. Implement the selectors has_sentiment and sentiment_value as well. You may use any representation you choose, but the rest of your program should not depend on this representation.

When you complete this problem, the question 3 tests should pass:

python3 -q 3
You can also call the print_sentiment function to print the sentiment values of all sentiment-carrying words in a line of text.
python3 -p computer science is my favorite!
python3 -p life without lambda: awful or awesome?

Problem 4 (1 pt). Implement analyze_tweet_sentiment, which takes a tweet (of the abstract data type) and returns a sentiment. Read the docstrings for get_word_sentiment and analyze_tweet_sentiment in to understand how the two functions interact. Your implementation should not depend on the representation of a sentiment!.

The tweet_words function should prove useful here: it combines the tweet_text selector and extract_words function from the previous questions to return a list of words in a tweet.

When you complete this problem, the question 4 tests should pass:

python3 -q 4

Phase 2: The Geometry of Maps

In this phase, we will implement two functions that together determine the centers of U.S. states. The shape of a state is represented as a list of polygons. Some states (e.g. Hawaii) consist of multiple polygons, but most states (e.g. Colorado) consist of only one polygon (still represented as a length-one list).

We will use the position abstract data type to represent geographic latitude-longitude positions on the Earth. The data abstraction, defined at the top of, has the constructor make_position and the selectors latitude and longitude.

Problem 5 (2 pt). Implement find_centroid, which takes a polygon and returns three values: the coordinates of its centroid and its area. The input polygon is represented as a list of position values that are consecutive vertices of its perimeter. The first vertex is always identical to the last.

The centroid of a two-dimensional shape is its center of balance, defined as the intersection of all straight lines that evenly divide the shape into equal-area halves. find_centroid returns the centroid and area of an individual polygon.

The formula for computing the centroid of a polygon appears on Wikipedia. The formula relies on vertices being consecutive (either clockwise or counterclockwise; both give the same answer), a property that you may assume always holds for the input.

Hint: latitudes correspond to the x values, and longitudes correspond to the yvalues.

The area of a polygon is never negative. Depending on how you compute the area, you may need to use the built-in abs function to return a non-negative number.

Manipulate positions using their selectors (latitude and longitude) rather than assuming a particular representation.

When you complete this problem, the question 5 tests should pass:

python3 -q 5

Problem 6 (1 pt). Implement find_state_center, which takes a state represented by a list of polygons and returns a position object, its centroid.

The centroid of a collection of polygons can be computed by geometric decomposition. The centroid of a shape is the weighted average of the centroids of its component polygons, weighted by their area.

When you complete this problem, the question 6 tests should pass:

python3 -q 6

Once you are finished, draw_centered_map will draw the 10 states closest to a given state (including that state). A red dot should appear over the two-letter postal code of the specified state.

python3 -d CA

Your program should work identically, even if you use the functional representation for tweets defined in question 1, using the -f flag.

python3 -f -d CA

Phase 3: The Mood of the Nation

In this phase, you will group tweets by their nearest state center and calculate the average positive or negative feeling in all the tweets associated with a state.

The name us_states is bound to a dictionary containing the shape of each U.S. state, keyed by its two-letter postal code. You can use the keys of this dictionary to iterate over all the U.S. states.

Problem 7 (2 pt). Implement group_tweets_by_state, which takes a sequence of tweets and returns a dictionary. The keys of the returned dictionary are state names (two-letter postal codes), and the values are lists of tweets that appear closer to that state's center than any other.

You should not include any states as keys that are not nearest to any tweet. You may want to define additional functions to organize your implementation into modular components. You will need to use the dictionary of us_states described above.

When you complete this problem, the question 7 tests should pass:

python3 -q 7

Problem 8 (2 pt). Implement average_sentiments. This function takes the dictionary returned by group_tweets_by_state and also returns a dictionary. The keys of the returned dictionary are the state names (two-letter postal codes), and the values are average sentiment values for all the tweets that have sentiment value in that state.

If a state has no tweets with sentiment values, leave it out of the returned dictionary entirely. Do not include a state with no sentiment using a zero sentiment value. Zero represents neutral sentiment, not unknown sentiment. States with unknown sentiment will appear gray, while states with neutral sentiment will appear white.

When you complete this problem, the question 8 tests should pass:

python3 -q 8

You should now be able to draw maps that are colored by sentiment corresponding to tweets that contain a given term. The correct map for Texas appears at the top of this page.

python3 -m texas
python3 -m sandwich
python3 -m obama
python3 -m my life

Your program should work identically, even if you use the functional representation for tweets defined in question 1, using the -f flag.

python3 -f -m texas

Congratulations! One more 61A project completed.


These extensions are optional and ungraded. In this class, you are welcome to program just for fun. If you build something interesting, come to office hours and give us a demo. However, please do not change the behavior or signature of the functions you have already implemented.

Acknowledgements: Aditi Muralidharan developed this project with John DeNero. Hamilton Nguyen extended it. Keegan Mann developed the autograder. Many others have contributed as well.