代做CS1026、Python编程语言代写、代做Python代写、代写Analysis

日期：2019-03-23 11:14

CS1026: Assignment 3 - Sentiment Analysis

Due: March 20, 2019 at 9:00pm. Weight: 11%

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,

Task:

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 time zone, 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. 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.

Additional Information

For both files, it is advised that when you read in the files, you can use the line below to avoid

encoding errors.

open("fileName.txt","r",encoding="utf-8") or

open('fileName.txt', encoding='utf-8', errors='ignore')

What You Will Submit and Be Marked On:

1. A 1-3 page written part (with Word, text, PDF file) which shows the happiness score for each

timezone and the numbers of tweets found in that timezone. Explain how you get them.

2. Your Python source file(s). Make sure that your code meets the requirements (such as using

functions and list described above). The name of the Python program you submit should be

your UWO userid_Assign2.py. Make sure you attach your Python file to your assignment; DO

NOT put the code inline in the textbox or the written part above. Make sure that you develop

your code with Python 3.7 as the interpreter. TAs will not endeavor to fix code that uses earlier

versions of Python.

3. Non-functional specifications: as described below

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