1 Learning Outcomes

2 The Story...

In Assignment 1, we studied basic principals in text cleansing. In this assignment, we will write code to analyse cleansed text. In particular, we are interested in a type of analysis called sentiment analysis.

Sentiment analysis is a process to identify opinions in a given piece of text and to determine whether the writer’s attitude towards a particular topic (or product, etc.) is positive, negative, or neutral. For example, writer’s attitude towards a particular topic (or product, etc.) is positive, negative, or neutral. For example, Algorithms are fun!" expresses a positive attitude; I hate raining!" expresses a negative attitude; and \I am going to an FOA lecture." is often neutral. Sentiment analysis is widely applied to customer reviews, polls, and online social network data (tweets) for various applications such as customer analysis and product recommendations.

There are various sentiment analysis algorithms. The core of those algorithms are linguistic rules (such as double negatives means ”positive") and statistics (such as how positive is the attitude when ”fun" is used).

Here, the numbers represent the sentiment scores (polarity) of the words. For example, ”fun" expresses a strong positive attitude and has a sentiment score of 4; \dislikes" and \rainy" express negative attitudes with sentiment sores of -2 and -1, respectively; all other words are neutral and have sentiment scores of 0. Overall, the sentence has a sentiment score of 4 + (-2) + (-1) = 1.

3 Your Task

The input starts with a list of words sorted alphabetically. Each word occupies exactly three lines. Line 1 starts with a ‘#’, which is followed by the word itself (e.g., \break"). There are at least 1 and at most 20 lowercase English letters in each word. There are no uppercase letters, digits, or any special characters.

Line 2 contains the sentiment score of the word. For example,\break" and \dislike" have sentiment scores of -1 and -2, respectively. You do not need to worry about how these scores are obtained.2 The sentiment score always exists for each word in the dictionary, and it is within the ranges of [-5; -1] and [1; 5].

Line 3 starts with a ‘$’, which is followed by the variation forms of the word that are separated by asterisks (‘*’). If a word is a verb (e.g., \break"), it will have 4 variation forms in the following order, where each form contains at least 1 and at most 23 lowercase English letters: (i) past tense form (e.g., ”broke"); (ii) past participle form (e.g, ”broken"); (iii) present participle form (e.g., ”breaking"); and (iv) third-person present form (e.g., \breaks"). If a word is a non-verb (e.g., \fun" and \rainy"), its variation forms will be empty. You may assume that any two words in the dictionary will not share the same variation forms (e.g., the variation forms of \break" and \dislike" are all different). You may also assume that any word will not be a variation form of another word in the dictionary (e.g., ”break" is not a variation form of any other word). (Hint: You do not need to understand what these variation forms mean to complete this assignment; for up to Stage 4, you may simply store all the variation forms of a word in a single string; for Stage 5, you will need to separate the variation forms and store them in multiple strings.)

The line \%%%%%%%%%%" indicates the end of the dictionary and the start of the given sentence.

The sentence given will occupy one line, where the words are separated by a single space. The sentence will contain at least one word, but there is no upper limit on the number of words or the number of characters in the sentence. Each word may contain at least 1 and at most 23 lowercase English letters. There will be no punctuation marks (such as ‘,’ or ‘.’), digits, or other special characters.

You will label the words of the sentence with their sentiment scores by consulting the sentiment dictionary.

3.1 Stage 1 - Reading One Dictionary Word (Up to 4 Marks)

3.2 Stage 2 - Reading the Whole Dictionary (Up to 8 Marks)

=========================Stage 2=========================
Number of words: 6
Average number of characters: 4.83
Average sentiment score: 0.17

3.3 Stage 3 - Reading the Sentence (Up to 12 Marks)

If you are not confident with linked data structures, you may use an array of strings to store the words, assuming a maximum of 20 words. If you do so, the full mark of this stage will be reduced from 4 to 2.

3.4 Stage 4 - Labelling and Calculating the Sentiment Score (Up to 15 Marks)

See the next page for a sample output of this stage given the input example above.

=========================Stage 4=========================
algorithms       0
are                 0
fun                4
although         0
she                0
dislikes           0
going             0
to                  0
school            0
on                 0
a                   0
rainy              -1
day                0
Overall score:   3

Note that if sequential search is used instead of binary search, the full mark of this stage will be reduced from 3 to 2. Note also that in this stage, we are only matching with the words in the dictionary but not their variation forms. Thus, \dislikes" in the sentence has not found a match in the dictionary. The variation forms will be considered in the next stage. 

3.5 Stage 5 - Handling the Variation Forms (Up to 15 Marks)

4 Submission and Assessment

You need to submit all your code (including any external code you used such as the functions in"listops.c") in one file named assmt2.c for assessment. The submission process is similar to that of Assignment 1. You will need to log in to a Unix server (dimefox.eng.unimelb.edu.au or nutmeg.eng.unimelb.edu.au) and submit your files using the following command:
submit comp10002 a2 assmt2.c
You can (and should) use submit both early and often - to get used to the way it works, and also to check that your program compiles correctly on our test system, which has some different characteristics to the lab machines. Only the last submission made before the deadline will be marked. You should verify your submission using the following commands: 

verify comp10002 a2 > receipt.txt /* Here ‘>’ writes the output into receipt.txt */ more receipt.txt 

You will be given a sample test file test0.txt and the sample output test0-output.txt. You can test your code on your own machine with the following command and compare the output with test0 -output.txt:

mac: ./assmt2 < test0.txt /* Here ‘<’ feeds the data from test0.txt into assmt2 */ 

Note that we are using the following command to compile your code on the submission server. 

gcc -Wall -std=c99 -o assmt2 assmt2.c 

The flag "-std=c99" enables the compiler to use a more modern standard of the C language { C99. To ensure that your submission works properly on the submission server, you should use this command to compile your code on your local machine as well
You may discuss your work with others, but what gets typed into your program must be individual work, not copied from anyone else. Do not give hard copy or soft copy of your work to anyone else; do not "lend" your memory stick to others; and do not ask others to give you their programs "just so that I can take a look and get some ideas, I won’t copy, honest". The best way to help your friends in this regard is to say a very firm "no" when they ask for a copy of, or to see, your program, pointing out that your "no", and their acceptance of that decision, is the only thing that will preserve your friendship. A sophisticated program that undertakes deep structural analysis of C code identifying regions of similarity will be run over all submissions in "compare every pair" mode. See https://academichonesty.unimelb.edu.au for more information.