EECS 285 Project 1: Tweet Sentiments

Project Due Friday, 17 Sep 2021, 8pm

In this project, you will analyze Twitter data to determine how people within a geographical region feel about a topic compared to people outside that region. You will define basic data structures for locations and tweets, assign sentiments to a tweet based on the words in the contains, and aggregate tweets according to location to determine the average sentiment within and outside a region.

The purpose of this project is to gain some basic literacy of Java, involving both reading and writing Java code. Unlike future projects, the classes and methods you’ll be writing are fully specified. You are required to adhere to the specifications here and in the starter code.


The project was written by Amir Kamil for EECS 285. It is based on the Twitter Trends project in the Composing Programs text.

The Twitter data in this project consists of actual tweets sent in late August and early September 2011.

Table of Contents

Project Roadmap

This is a big-picture view of what you’ll need to do to complete this project. Most of the pieces listed here also have a corresponding section later on in the spec that goes into more detail.

This project will be autograded for correctness, and the correctness portion is worth 100% of your project grade. We will not hand grade this project.

We will also run automated style and programming-practice checks on your code. However, these checks will not be worth any points on this project.

You must work alone for this project.

Download the starter code

The starter code is available at https://eecs285.github.io/p1-sentiments/starter-files.zip. The following files are included:

File(s) Description
Sentiments.java Data structure that maps words to sentiments
TweetReader.java Data structure that holds tweet data read from a file
TweetAnalyzer.java Class that analyzes tweet sentiments. You will need to complete this class.
TweetSentimentMain.java Main driver for tweet-sentiment analysis
Test.java Basic tests for Location and Tweet clases
Test.correct Correct output from running Test
data/ Directory containing sentiment data (sentiments.csv) and tweet data (*.txt)
Correct output from running sentiment analysis on obama.txt, soup.txt, and texas.txt

Extract the files to a temporary directory.

Set up your project

Follow the setup tutorial to set up your project.

Your Java files should all be in a package with the structure eecs285.proj1.<uniqname>, where <uniqname> is your uniqname. For example, I would put the following package directive at the top of each .java file:

package eecs285.proj1.akamil;

You will need to modify each of the starter files so that they have the correct package directive at the top.

Familiarize yourself with the starter code

Read through the starter code to understand what classes and methods are available. You need only read through the documentation; you do not have to understand how the starter code works.

Understand what you need to write

Read through the rest of the specification to determine what classes you must complete or write from scratch.

Implement and test your classes and methods

Implement the required classes and methods. Start by writing stubs, empty implementations (or just a trivial return for non-void methods) of the methods you need to write, so that the code will compile. Then write the actual implementation of each method, testing along the way.

In addition to the provided tests, you should write your own, as the tests in the starter code do not fully test the code you will write.


Submit the following files to the autograder.

Do not submit Test.java or TweetSentimentMain.java. We will use our own versions of these files, so make sure that your implementation does not require them to be modified.

As per course policy, we will grade your last submission to the autograder. It is your responsibility to ensure that your last submission is complete.

The Location Class

The Location class represents a geographical location, with a given latitude and longitude. You will need to create a file Location.java. Place the appropriate package directive at the top and define a public class Location. This class should have the following fields (data members):

It should also have the following public methods:

Once you have written the Location class, you should test it to make sure it is correct. We have provided a basic test as part of Test.java. You will need to write stubs for Tweet.java in order for the code to compile.

The Tweet Class

The Tweet class represents an individual tweet, keeping track of both the location where the tweet originated and the content of the tweet. You will need to create a file Tweet.java, with the appropriate package directive and a public class Tweet. The class should have the following fields:

You will need to define the following public methods:

The TweetAnalyzer Class

The TweetAnalyzer class analyzes the sentiment of tweets. Part of the class is already written for you. You will need to fill in the implementation of the following methods:

The TweetSentimentMain Class

The TweetSentimentMain class is the top-level driver for the analysis. Given a tweet file, it computes the average sentiment within an approximation of the Midwest region, as well as the average sentiment outside of it:

Along with changing the package directive, you will need to set the UNIQNAME constant to be your uniqname, so that the code can load data files from the proper location. Initially, the TWEET_FILE constant is set to "soup.txt", so that the program analyzes tweet data concerning “soup”. You can change the constant to one of the other data files in the data/ directory, or you can specify the tweet file at the command line:

$ java eecs285.proj1.akamil.TweetSentimentMain obama.txt

(Of course, you should replace akamil with your own uniqname.)

You can also specify the command-line arguments in IntelliJ, as described in the setup tutorial.

We have provided correct output files for a subset of the tweet-data files. For instance, the following is the result of running the analysis on obama.txt, which contains tweets with the word “obama” in them:

Tweet file: obama.txt
Location boundary: [37.0, -104.05] to [49.0, -80.517]
Sentiment within boundary:
  Average sentiment over 129 tweets: 0.0391
Sentiment outside boundary:
  Average sentiment over 627 tweets: 0.0088

Given the dataset, it appears that people within the Midwest had a slightly higher opinion of “obama” than those outside the Midwest. Perhaps because President Obama is from Chicago?

Requirements and Restrictions