NAVER Sentiment Movie Corpus
NAVER Sentiment Movie Corpus(NSMC) is a movie review dataset released by e9t@github. Data specification is as follows.
- author: e9t@github
- repository: https://github.com/e9t/nsmc
- references: www.lucypark.kr/docs/2015-pyconkr/#39
- size:
- train: 150,000 examples
- test: 50,000 examples
Data structure is as follows:
| Attributes | Properties |
|---|---|
| text | movie review comments |
| label | sentiment labels on the movie (positive 1, negative 0) |
1. Using in Python
You can download and load the corpus after executing your Python console.
Downloading the corpus
You can download NSMC into your local directory with the following Python codes.
from Korpora import Korpora
Korpora.fetch("nsmc")
By default, the corpus is downloaded to a Korpora directory within the user’s root directory (~/Korpora). If you wish to download the corpus to another directory,add root_dir=custom_path argument to the fetch method.
When the fetch method is executed with force_download=True argument, it ignores the existing corpus in the local directory and re-downloads the corpus. The default value of force_download is False.
Loading the corpus
You can load NSMC from your Python console with the following codes. If the corpus does not exist in the local directory, it is also downloaded as well.
from Korpora import Korpora
corpus = Korpora.load("nsmc")
You can also load the corpus as follows. The output of these codes is identical to that of previous codes.
from Korpora import NSMCKorpus
corpus = NSMCKorpus()
If you use either one of these previous examples, you can load the corpus into the variable corpus. train refers to the training dataset of NSMC, and you can check its first training instance as follows.
>>> corpus.train[0]
LabeledSentence(text='아 더빙.. 진짜 짜증나네요 목소리', label=0)
>>> corpus.train[0].text
아 더빙.. 진짜 짜증나네요 목소리
>>> corpus.train[0].label
0
By executing the get_all_texts method, you can access all texts (movie review comments) within NSMC.
>>> corpus.get_all_texts()
['아 더빙.. 진짜 짜증나네요 목소리', ... ]
By executing the get_all_labels method, you can access all labels (either positive or negative) within NSMC.
>>> corpus.get_all_labels()
[0, ... ]
2. Using in a terminal
You can directly download the corpus without executing Python console. To do so, use the following command.
korpora fetch --corpus nsmc
By default, the corpus is downloaded to a Korpora directory within the user’s root directory (~/Korpora). If you wish to download the corpus to another directory,add --root_dir custom_path argument to the fetch command.
If you add --force_download argument when executing the fetch command in the terminal, it ignores the existing corpus in the local directory and re-downloads the corpus.