CS1026: Assignment 3 ‐ Sentiment Analysis
(updated October 28, 2019)
Due: November 13th, 2019 at 9:00pm.
Weight: 12%
Learning Outcome:
By completing this assignment, you will gain skills relating to
using functions,
complex data structures,
nested loops,
text processing,
file input and output,
exceptions in Python,
using Python modules,
testing programs,
writing code that is used by other programs.
Background:
With the emergence of Internet companies such as Google, Facebook, and Twitter, more and more
data accessible online is comprised of text. Textual data and the computational means of processing it
and extracting information is also increasingly more important in areas such as business, humanities,
social sciences, etc. In this assignment, you will deal with textual analysis.
Twitter has become very popular, with many people “tweeting” aspects of their daily lives. This “flow
of tweets” has recently become a way to study or guess how people feel about various aspects of the
world or their own life. For example, analysis of tweets has been used to try to determine how certain
geographical regions may be voting – this is done by analyzing the content, the words, and phrases, in
tweets. Similarly, analysis of keywords or phrases in tweets can be used to determine how popular or
unpopular a movie might be. This is often referred to as sentiment analysis.
Task:
In this assignment, you will write a Python module, called sentiment_analysis.py (this is the name of
the file that you should use) and a main program, main.py, that uses the module to analyze Twitter
information. In the module sentiment_analysis.py, you will create a function that will perform simple
sentiment analysis on Twitter data. The Twitter data contains comments from individuals about how
they feel about their lives and comes from individuals across the continental United States. The
objective is to determine which timezone (Eastern, Central, Mountain, Pacific; see below for more
information on how to do this) is the “happiest”. To do this, your program will need to:
Analyze each individual tweet to determine a score – a “happiness score”.
The “happiness score” for a single tweet is found by looking for certain keywords (which
are given) in a tweet and for each keyword found in that tweet totaling their “sentiment
values”. In this assignment, each value is an integer from 1 to 10.
The happiness score for the tweet is simply the sum of the “sentiment values” for
keywords found in the tweet divided by the number of keywords found in the tweet.
If there are none of the given keywords in a tweet, it is just ignored, i.e., you do NOT
count it.
To determine the words in a tweet, you should do the following:
o Separate a tweet into words based on white space. A “word” is any sequence of
characters surrounded by white space (blank, tab, end of line, etc.).
o You should remove any punctuation from the beginning or end of the word (do NOT
worry about punctuation within a word). So, “#lonely” would become “lonely” and
“happy!!” would become “happy”; but “not‐so‐happy” is just “not‐so‐happy”.
o You should convert the “word” into just lower case letters. This gives you a “word”
from the tweet.
o If you match the “word” to any of the sentiment keywords (see below), you add the
score of that sentiment keyword to a total for the tweet; you should just do exact
matches. For example, if the word “hats” is in the tweet and the word “hat” is a
sentiment keyword, then they DO NOT MATCH. Of course, if “hats” is in the list of
sentiment keywords, then there is a match.
For each region, you should count the number of tweets in that region and you should
count the number of tweets with keywords, i.e. a “keyword tweet”. A “keyword tweet” is
a tweet in the region in which there was at least one matched keyword. [Note: if your
output has the number of tweets in a region equal to the number of “keyword tweets” in
that region, then you have done something wrong!]
The “happiness score” for a timezone is just the total of the scores for all the counted
tweets in that region divided by the number of keyword tweets in that region; again, if a
tweet has NO keywords, then it is NOT to be counted as a “keyword tweet” in that
timezone.
A file called tweets.txt contains the tweets and a file called keywords.txt contains keywords
and scores for determining the “sentiment” of an individual tweet. These files are described in
more detail below.
File tweets.txt
The file tweets.txt contains the tweets; one per line (some lines are quite long). The format of
a tweet is:
[lat, long] value date time text
where:
[lat, long] ‐ the latitude and longitude of where the tweet originated. You will need
these values to determine the timezone in which the tweet originated.
value – not used; this can be skipped.
date – the date of the tweet; not used, this can be skipped.
time – the time of day that the tweet was sent; not used this can be skipped.
text – the text in the tweet.
File keywords.txt
The file keywords.txt contains sentiment keywords and their “happiness scores”; one per line.
The format of a line is:
keyword, value
where:
keyword ‐ the keyword to look for.
value – the value of the keyword; values are limited to 1, 5, 7 and 10, where 1
represents very “unhappy” and 10 represents “very happy”.
(you are free to explore different sets of keywords and values at your leisure for the sheer fun of it!).
Determining timezones across the continental United States
Given a latitude and longitude, the task of determining exactly the location that it corresponds
to can be very challenging given the geographical boundaries of the United States. For this
assignment, we simply approximate the regions corresponding to the timezones by rectangular
areas defined by latitude and longitude points. Our approximation looks like:
So the Eastern timezone, for example, is defined by latitude‐longitude points p1, p2, p3, and
p4. To determine the origin of a tweet, then, one simply has to determine in which region the
latitude and longitude of the tweet belongs. The values of the points are:
p1 = (49.189787, ‐67.444574)
p2 = (24.660845, ‐67.444574)
p3 = (49.189787, ‐87.518395)
p4 = (24.660845, ‐87.518395)
p5 = (49.189787, ‐101.998892)
Pacific Mountain Central Eastern
p6 = (24.660845, ‐101.998892)
p7 = (49.189787, ‐115.236428)
p8 = (24.660845, ‐115.236428)
p9 = (49.189787, ‐125.242264)
p10 = (24.660845, ‐125.242264)
Note: if the latitude‐longitude of a tweet is outside of all these regions, it is to be skipped.
Functional Specifications:
Developing code for the processing of the tweets and sentiment analysis.
1. Your module sentiment_analysis.py must include a function compute_tweets that has two
parameters. The first parameter will be the name of the file with the tweets and the second
parameter will be the name of the file with the keywords. This function will use these two files to
process the tweets and output the results. This function should also check to make sure that both
files exist and if either does not exist, then your program should generate an exception and the
function compute_tweets should return an empty list (see part 1.d below).
a. The function should input the keywords and their “happiness values” and store them in a data
structure in your program (the data structure is of your choice, but you might consider a list).
b. Your function should then process the file of tweets, computing the “happiness score” for each
tweet and computing the “happiness score” for each timezone. You will need to read the file
of tweets line by line as text and break it apart. The string processing functions in Python (see
Chapter 7) are very useful for doing this. Your program should not duplicate code. It is
important to determine places that code can be reused and create functions. Your program
should ignore tweets from outside the time zones.
c. Your function, compute_tweets, should return a list of tuples:
I. The list should contain the results in a tuple for each of the regions, in order: Eastern,
Central, Mountain, Pacific.
II. Each tuple should contain three values: (average, count_of_keyword_tweets,
count_of_tweets), where average is the average “happiness value” of that region,
count_of_keyword_tweets is the number of tweets found in that region with keywords
and count_of_tweets is the number of tweets found in that region. These values
should be in the order specified.
III. Note: if there is an exception from a file name that does not exist, then an empty list
should be returned.
2. Your main program, main.py, will prompt the user for the name of the two files – the file
containing the keywords and the file containing the tweets. It will then call the function
compute_tweets with the two files to process the tweets using the given keywords. Your main
program will get the results from compute_tweets and print the results; it should print the
results in a readable fashion (i.e., not just numbers).
3. You are also given a program, driver.py, and some test files. The test files are small files of tweets
and keywords that driver.py uses to test your program – that is, it will import your program and
will make use of the function compute_tweets. The files tweets1.txt and tweets2.txt are small
files with tweets and the files key1.txt and key2.txt contain keywords and “happiness values”. The
program driver.py will use these to test your function. You should use the program and these files
to test your code. Note: while driver.py does some testing, it is by no means guaranteed to test
for all possibilities; you should do some of your own testing.
Additional Information
For both files, it is advised that when you read in the files you the line below to avoid encoding errors.
open("fileName.txt","r",encoding="utf‐8") or open('fileName.txt', encoding='utf‐8', errors='ignore')
Non‐functional Specifications:
1. Include brief comments in your code identifying yourself, describing the program, and describing
key portions of the code.
2. Assignments are to be done individually and must be your own work. Software may be used to
detect cheating.
3. Use Python coding conventions and good programming techniques, for example:
Meaningful variable names
Conventions for naming variables and constants
Use of constants where appropriate
Readability: indentation, white space, consistency
You should submit the files main.py and sentiment_analysis.py (others are not required). Make sure
you upload your Python file to your assignment; DO NOT put the code inline in the textbox.
What You Will Be Marked On:
Functional specifications:
Is the program named correctly for testing, i.e., is the module correctly named
sentiment_analysis.py ? Is there a function compute_tweets and are the parameters in
the correct order?
Is there a program main.py which imports and makes use of the module
sentiment_analysis.py?
Does the program behave according to specifications? Does it work on with the test
program, driver.py ?
Does the program handle incorrect function names?
Is there an effective use of functions beyond compute_tweets ?
Is the output according to specifications?
Note: A program like driver.py and test files will be used to test your program as well.
Non‐functional specifications: as described above.
Assignment submission: via the OWL, though the assignment submission in OWL.