This is a repository for team project in CS372, 2020 Spring, KAIST.
Please refer to the course homepage and NLTK book.
These days, there are numerous online contents, such as movie or application reviews, and people react to them with comments. However, malicious comments are becoming a social problem. In this project, it starts with simple preference discriminator, and expands over different datasets. Preference discriminator refers to assessing how positive or negative the reviewers are about the content they watch or use. All works are done only with NLTK.
Please refer to the scheme here.
Refer to the following documents:
<type>(<scope>): <subject>
Should be one of the following:
- docs: Documentation only changes
- feat: A new feature
- fix: A bug fix
- refactor: A code change that neither fixes a bug nor adds a feature
- style: Changes that do not affect the meaning of the code (white-space, formatting, missing semi-colons, etc)
- misc: Adding miscellaneous items
Changed file name or none.
The subject contains a succinct description of the change:
- use the imperative, present tense: "change" not "changed" nor "changes"
- do capitalize the first letter
- no dot (.) at the end
Sorted in Hangeul (Korean alphabetical) order.