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CS1026A Fall 2024
Assignment 3: YouTube Emotions

Important Notes:

Change Log:

1. Learning Outcomes

2. Background

Much work has been done towards the goal of creating large datasets of word affinity or sentiment. One such effort is the National Research Council (NRC) Emotion Lexicon which is a list of English words and their associations with eight basic emotions (anger, fear, anticipation, trust, surprise, sadness, joy, and disgust) and two sentiments (negative and positive).

Our goal in this assignment is to use a simplified version of the NRC Emotion Lexicon to classify YouTube comments based on one of the following emotions anger, joy, fear, trust, sadness, or anticipation. Based on the emotion contained in each comment for a particular video we then want to generate a report that details the most common emotions YouTube users have towards that video based on their comments.

3. Datasets

Your Python program will deal with two datasets, a keywords data set that contains a simplified version of the NRC Emotion Lexicon (this dataset will remain the same for all tests of your program) and a Comma-Separated Values (CSV) file that contains the comments for a particular YouTube video (this dataset will change for each test of your program).

3.1 Keywords Dataset (TSV File)

Each line starts with a keyword and is followed by a score (0 or 1) for each emotion in this order: anger, joy, fear, trust, sadness, and anticipation. If a 1 is present it means that keyword is related to that emotion. If a 0 is present the keyword is unrelated to that emotion.

For example, according to the above the word “abacus” is related to the emotion of trust and no other emotions. The word “abandon” is related to the emotions fear and sadness and no other emotions.

All words in the dataset will be related to at least one emotion. This file’s contents will remain the same for all tests but may be given a different filename based on the users input (e.g. it could be named keys.tsv or words.tsv rather than keywords.tsv).

3.2 Comments Dataset (CSV File)

The following is an example of a possible line from this file (the file may contain one or more lines). Note that this document wraps the line on to multiple lines but in the file this is one line ended by a line break (\n):

2,PixelPioneer24,brazil,The excavation scenes in the movie were excellent but the unnecessary derision of the hero's motives seemed unfair. His eventuality of success was not adequately showcased.Each line of this file will contain four values separated by a single comma character (,). The values will always be in the following order:

Comment ID is a unique positive integer identifier for the comment. Username is the username of the user who posted the comment. Country is the user’s home country, and comment text is the text the of the comment posted by the user.

No value will contain a line break or a comma character. The capitalization of country names could be different for each line even if it is for the same country, but the country will always be spelled the same.

Space characters will only occur in the comment text or country name.

4. Tasks

To accomplish this, you will need to do the following:

1. Accept input from the user: The user will specify the file names of the keywords and comments data sets as well as the name of the report file your program will create. The user will also input the name of the country they wish to filter the comments by.
2. Read. Your program will read in the keyword and comments datasets and store them in the formats described in the functional specification (in Section 5).
3. Clean. The text of the comments will be cleaned to remove any punctuation and convert them to all lowercase letters.
4. Determine Emotion. You will use the keyword’s dataset to determine the overall emotion expressed in each comment.
5. Generate Report. Based on your analysis of each comment, you will create a report file that contains a summary of the most common emotion expressed as well as how common each emotion was (as specified in Section 5).Additionally, you must follow the functional specification presented in Section 5 and the rules and requirements in Section 8.

5. Functional Specification

The functions described in this section should be present in your emotions.py file and must be used in some way in your program to read, clean, process, analyze, or report on the comments in the given dataset. Each function and its parameters must have the same name and spelling as specified below: 

clean_text(comment)

This function should have one parameter, comment, which is a string that contains the text of a single comment from the comments dataset. The function should clean this text by replacing any characters that are not letters (A to Z) and replacing them with space characters. It should also convert the comment’s text to all lower case.

This function should return the cleaned text as a string.

will result in this output:

this is an example it s a b t silly

This function should read the Tab-Separated Values (TSV) keywords file as described in Section 3.1. keyword_file_name is a string containing the name of the keywords file. This function can safely assume that this file exists, is in the current working directory, and is properly formatted. Checks on the file’s existence will be done in the main.py file described later in this document.

The function should return a dictionary with keys for each word in the file and the values of this dictionary should be a new dictionary for each keyword that contains a value for each emotion (anger, joy, fear, trust, sadness, and anticipation).

Example:

make_keyword_dict("keywords.tsv")

}

Note that to pass the Gradescope tests this function must return a dictionary and not another collection such as a list, the keyword keys must be spelled exactly as listed in keywords.tsv, and the emotions must be spelled correctly and in lower case.

Hint: You may find a number of the Python string methods helpful when creating this function.

make_comments_list(filter_country, comments_file_name)

This function should read the Comma-Separated Values (CSV) file as described in Section 3.2. comments_file_name is a string containing the name of the CSV file and filter_country is a string containing either a country name or the string “all”. This function should read the CSV file and return a list containing only comments for the given country listed in filter_country (or all countries if the string “all” is given).

The list should contain one element for each comment in the file that matches the country in the filter (or all comments if “all” is given). Each element in the list should be a dictionary that contains a key for the Comment ID, Username, Country and Comment Text. The keys should be named 'comment_id', 'username', 'country', and 'text' respectively.

The comment text should be stripped of any leading and trailing whitespace.

Assuming that comments.csv only contains the following two lines (note that the line is wrapped in this document and in the .csv file this is only two lines):

2,PixelPioneer24,brazil,The excavation scenes in the movie were excellent but the unnecessary derision of the hero's motives seemed unfair. His eventuality of success was not adequately showcased. 

then calling

Example 2:

eventuality of success was not adequately showcased.'} ]

Example 3:

Note that to pass the Gradescope tests this function must return a list and not another collection such as a set or dictionary, the values of each list element must be a dictionary, and the keys used in that dictionary must match the spelling and lowercase capitalization given in this section.

This function takes the text of a comment and the keywords dictionary created by the make_keyword_dict function as parameters and classifies the comment as one of the possible emotions (anger, joy, fear, trust, sadness, and anticipation), returning the emotion as a string.

A comment is classified by first cleaning the text (using the clean_text function) and then checking each word in the comment against the keywords dictionary. A total for each possible emotion should be kept with each word in the comment matching a keyword adding to the totals (based on the values in the keywords dictionary).

The excavation scenes in the movie were excellent but the unnecessary derision of the hero's motives seemed unfair. His eventuality of success was not adequately showcased. the text should be first cleaned using clean_text to get: the excavation scenes in the movie were excellent but the unnecessary derision of the hero s motives seemed unfair his eventuality of success was not adequately showcasedthen each word should be checked against the keywords dictionary and the totals for each emotion kept. Words not matching any words in the dictionary (shown in black above) do not add to the scores. For example, using the full keywords.tsv dataset the words shown in blue above have matches in the keyword dataset and would result in the following totals:

In the event of a tie, the emotions should be given priority in this order: 1) anger, 2) joy, 3) fear, 4) trust, 5) sadness, and 6) anticipation.

Hint: You may find the string split method useful for looping through words rather than characters.

make_report(comment_list, keywords, report_filename)

This function takes the comment_list (created by the make_comments_list function), the keywords dictionary (created by the make_keyword_dict function), and a string containing the file name of the report to generate (report_filename) as parameters.

A new file should be created with the file name in report_filename and it should contain the name of the most common emotion classification in the comment_list dataset as well as a count of the number of comments classified as each emotion. In the event of a tie the emotions should be given priority in this order: 1) anger, 2) joy, 3) fear, 4) trust, 5) sadness, and 6) anticipation.The format of the report should match the following example which is based on the attached comments.csv and keywords.tsv with a country filter of “all”:

The emotion totals should occur in the same order (regardless of the counts) but the values would be different depending on the comment_list and keywords dictionary passed to the function.

All percentages should be rounded to two digits and all six emotions should always be listed even if their count is zero. Important: in your report file each percentage must be written with one or two decimal places. A value such as 20.000% or 6.6700% would be wrong even though it is technically rounded as there are too many decimal places. Your output must be formatted exactly as shown in the example above including the spacing and line breaks.

Reminder: The report should be saved to a file and not output to the screen or returned by the function. Only the name of the most common emotion should be returned. 

5.2 main.py

Your main.py file must contain the following two functions (ask_user_for_input and main) as specified:

This function takes no parameters but asks the user to input the file names of the keywords TSV file, the comments CSV file, the country to filter by, and the file name of the report to be generated. These three filenames and the country name are returned in a tuple in this order: 1) keyword filename, 2) comment filename, 3) country name (converted to lower case), and 4) report filename.

User input is shown in green and input prompts in black. Note that the filenames and country are based on the user’s input and can not be hardcoded to one set value. This means that the filenames could be different depending on the values input by the user.

Your ask_user_for_input() method must complete the following checks on the user input.

For each of the three filenames, if the user inputs a filename ending in the wrong file extension (.csv, .tsv, or .txt) the function should raise a ValueError exception with a message stating that the file extension is incorrect such as “Keyword file does not end in .tsv!”. The text of this message must be exactly the following for each file:

For the keyword and comment files you must check if the file exists using the os.path.exists function. If it does not, your function must raise a IOError exception with text explaining that the function does not exist. The message should have the text “<file name> does not exist!” where <file name> is replaced with the filename such as “keywords.tsv does not exist!", where keywords.tsv is the missing file.

Check 3: Valid Country

Keep in mind that this only limits the countries a user can filter by, it does not limit what country names can occur in the dataset.

This function handles calling the other functions in main.py and emotions.py to perform the tasks listed in Section 3. It should check for any exceptions being raised by the ask_user_for_input function, output the error message contained in the exception (this can be done by simply printing the exception with print()), and ask the user to input the values again if any exception is raised.

Once valid input has been received, it should call the functions from emotions.py required to analyze the comments and generate the report.

Lastly it should output to the screen the most common emotion in the comment data set.

This should be displayed as “Most common emotion is: <emotion name>” where emotion name is the name of the emotion such as “Most common emotion is: anger” if the emotion is anger.

Note that the above is one run of the program. It should keep asking for input again if an exception occurs in the ask_user_for_input function. Also note that in this example, keys.tsv and c.csv are valid files that exist and the file report.txt already exists. “Belgium” is not in the list of valid countries so it is rejected and “FrAnCe” is accepted despite it’s odd capitalization as the ask_user_for_input function should convert it to lowercase.

This section gives some starter code you should use in your program. You may not alter the names of any function or the parameters the functions take (this includes adding or removing parameters). You may not import any libraries or modules not included in the template code and all code you add should be inside a function (adding code outside of a function may cause the Gradescope tests to fail). You may add additional helper functions as needed.

It is important to import the files in the correct order and from the correct files. Main.py should import emotions.py as shown in the template above and not the other way around.

7. Extra Example

In addition to the other tasks and specifications given in this document, your program must also fulfill the following requirements:

2. You may not use any modules or third-party libraries not described in this document. Standard built-in functions such as the String, file, and math functions are fine. You should not have to import anything other than your emotions.py and the os.path module. TAs may manually remove marks from your Gradescope test if you violate this rule.

3. You must document your code with brief comments. Each file should contain a comment at the top of the file with your name, western e-mail, student number, and a brief description of what is contained in that file. At least one comment should also be given for each function that describes its purpose, parameters, and values returned. You should also include any additional comments to document any lines that may be unclear to the reader.

4. Your program must be efficient and terminate within a reasonable time limit. All gradescope test cases (including any hidden cases) must terminate within the autograder’s time limit.

5. Assignments are to be done individually and must be your own original work. You may not show or otherwise share your code for this assignment with others or use tools to generate your code for you. Software will be used to detect academic dishonesty (cheating). If you have any questions about what is or is not academic dishonesty, please consult the document on academic dishonesty and ask any questions to your course instructor before submitting this assignment.

6. You must follow Python style and coding conventions and good programming techniques, for example:

7. All of your code should be contained in the files main.py and emotions.py. Only submit these files and no others and ensure the filenames match exactly. It is your responsibility to ensure you have submitted the correct files.

8. All function names and outputs should follow the specifications given in this document exactly. Not following the specifications may lead to test cases failing. It is your responsibility to ensure you have followed them correctly and your tests are passing.

10. Frequently backup your work remotely (e.g. using OneDrive) in a way that is secure and private. No extension will be given for lost or corrupted files or submitting incorrect files.

9. Marking and Submission

9.1 Submission

It is recommended that you create your own test cases to check that the code is working properly for a multitude of different scenarios (some example datasets have been provided for you with this document on OWL).

Assignments will not be accepted by email or by any other form other than a Gradescope submission.

9.2 Marking

The assignment will be marked as a combination of your auto-graded tests (both visible and hidden tests) and manual grading of your code logic, comments, formatting, style, etc.

Marks will be deducted for failing to follow any of the specifications in this document (both functional and nonfunctional), not documenting your code with comments, using poor formatting or style, hardcoding, or naming your files incorrectly.Marking Scheme:

Total: 28 points

Late assignments will only be accepted up to 3 days late and only if you have enough late coupons remaining (at least one for each day late). If you submit one day late, you will need to use 1 late coupon. 2 days late, 2 late coupons. And 3 days late, 3 late coupons. If you have insufficient late coupons remaining or submit more than 3 days late, you will receive a zero grade on this assignment.

It is your responsibility to track your late coupon use. Any values shown on OWL should be considered an estimate and may not be accurate or up to date.