Description
In this assignment, you will write a complete program in Python that analyzes twitter
information. 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 (or
politics as with Donald Trump :). 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.
In this assignment, you will build a program 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” 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. So,
“#lonely” would become “lonely” and “happy!!” would become “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 can just do
exact matches.
• The “happiness score” for a timezone is just the total of the scores for all the tweets in
that region divided by the number of tweets; again, if a tweet has NO keywords, then it is
NOT counted as a 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 dif erent 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)
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)
Functional Specifications:
Part A: Developing code for the processing of the tweets and sentiment analysis.
1. Your program should read the keyword file in specific directory and prompt the user for the
name of the file containing the keywords.
2. Your program should then input the keywords and their “happiness values” from keywords
file and store them in a data structure in your program (the data structure is of your choice, but
you might consider a list).
3. Your program should read the tweets file in specific directory and then prompt the user for
the name of the file with tweets;
4. You 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 with no keywords and also ignore
tweets from outside the time zones.
5. Once you have completed processing the entire file, you should print out:
• The “happiness score” for each timezone.
• The number of tweets found in that timezone.
Part B (Bonus 1%): Once you have implemented your sentiment analysis program, you should
now extend the program to include a simple (but fun) graphical display. This part of the
assignment will make use of the given Python code in graphics.py and happy_histogram.py;
these can be found on the course website for Assignment #3.
• The Python code graphics.py is the graphics package described in the book; you can look in the
text for examples (see Chapter 2, section 2.6).
• The Python code happy_histogram.py does simple graph plotting using graphics.py. You will
need to import happy_histogram.py into your program. It makes a number of assumptions, so
make sure to read the comments in the code about the parameters and limitations of the
functions. It contains three functions:
– drawSimpleHistogram which will create a histogram of four values in a graphics
window; it assumes that the values are in the range of 0-10.
– drawHappyFace which draws a happy face on the histogram as determined by
drawSimpleHistogram.
– drawSadFace which draws a sad face on the histogram as determined by
drawSimpleHistogram.
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
The name of the file you submit should be your UWO userid_Assign3.py. For instance, if your
name is “Smith”, your assignment would be smith_Assign3.py. Make sure you attach your
python file to your assignment; DO NOT put the code inline in the textbox.
Make sure that you develop your code with Python 3.6 as the interpreter. TAs will not endeavor
to fix code that uses earlier versions of Python.
What You Will Be Marked On:
• Functional specifications:
• Does the program behave according to specifications?
• Do you use functions effectively?
• Do you use proper data structure effectively?
• Is the output according to specifications?
• Does the program produce a histogram?
• Non-functional specifications: as described above
• Assignment submission: via the OWL, though the assignment submission in OWL.
