ACM Research Coding Challenge (Fall 2021)

No Collaboration Policy

You may not collaborate with anyone on this challenge. You are allowed to use Internet documentation. If you do use existing code (either from Github, Stack Overflow, or other sources), please cite your sources in the README.

Submission Procedure

Please follow the below instructions on how to submit your answers.

1. Create a public fork of this repo and name it ACM-Research-Coding-Challenge-F21. To fork this repo, click the button on the top right and click the "Fork" button.

2. Clone the fork of the repo to your computer using git clone [the URL of your clone]. You may need to install Git for this (Google it).

3. Complete the Challenge based on the instructions below.

4. Submit your solution by filling out this form.

Assessment Criteria

Submissions will be evaluated holistically and based on a combination of effort, validity of approach, analysis, adherence to the prompt, use of outside resources (encouraged), promptness of your submission, and other factors. Your approach and explanation (detailed below) is the most weighted criteria, and partial solutions are accepted.

Question One

Sentiment analysis is a natural language processing technique that computes a sentiment score for a body of text. This sentiment score can quantify how positive, negative, or neutral the text is. The following dataset in input.txt contains a relatively large body of text.

Determine an overall sentiment score of the text in this file, explain what this score means, and contrast this score with what you expected. If your solution also provides different metrics about the text (magnitude, individual sentence score, etc.), feel free to add it to your explanation.

You may use any programming language you feel most comfortable. We recommend Python because it is the easiest to implement. You're allowed to use any library/API you want to implement this, just document which ones you used in this README file. Try to complete this as soon as possible as submissions are evaluated on a rolling basis.

Regardless if you can or cannot answer the question, provide a short explanation of how you got your solution or how you think it can be solved in your README.md file. However, we highly recommend giving the challenge a try, you just might learn something new!