|
|
--- |
|
|
task_categories: |
|
|
- text-classification |
|
|
language: |
|
|
- en |
|
|
--- |
|
|
# Movie Review Data |
|
|
|
|
|
* Original source: sentence polarity dataset v1.0 http://www.cs.cornell.edu/people/pabo/movie-review-data/ |
|
|
* Seems to same as https://huggingface.co/datasets/rotten_tomatoes, but different split. |
|
|
|
|
|
## Original README |
|
|
|
|
|
======= |
|
|
|
|
|
Introduction |
|
|
|
|
|
This README v1.0 (June, 2005) for the v1.0 sentence polarity dataset comes |
|
|
from the URL |
|
|
http://www.cs.cornell.edu/people/pabo/movie-review-data . |
|
|
|
|
|
======= |
|
|
|
|
|
Citation Info |
|
|
|
|
|
This data was first used in Bo Pang and Lillian Lee, |
|
|
``Seeing stars: Exploiting class relationships for sentiment categorization |
|
|
with respect to rating scales.'', Proceedings of the ACL, 2005. |
|
|
|
|
|
@InProceedings{Pang+Lee:05a, |
|
|
author = {Bo Pang and Lillian Lee}, |
|
|
title = {Seeing stars: Exploiting class relationships for sentiment |
|
|
categorization with respect to rating scales}, |
|
|
booktitle = {Proceedings of the ACL}, |
|
|
year = 2005 |
|
|
} |
|
|
|
|
|
======= |
|
|
|
|
|
Data Format Summary |
|
|
|
|
|
- rt-polaritydata.tar.gz: contains this readme and two data files that |
|
|
were used in the experiments described in Pang/Lee ACL 2005. |
|
|
|
|
|
Specifically: |
|
|
* rt-polarity.pos contains 5331 positive snippets |
|
|
* rt-polarity.neg contains 5331 negative snippets |
|
|
|
|
|
Each line in these two files corresponds to a single snippet (usually |
|
|
containing roughly one single sentence); all snippets are down-cased. |
|
|
The snippets were labeled automatically, as described below (see |
|
|
section "Label Decision"). |
|
|
|
|
|
Note: The original source files from which the data in |
|
|
rt-polaritydata.tar.gz was derived can be found in the subjective |
|
|
part (Rotten Tomatoes pages) of subjectivity_html.tar.gz (released |
|
|
with subjectivity dataset v1.0). |
|
|
|
|
|
|
|
|
======= |
|
|
|
|
|
Label Decision |
|
|
|
|
|
We assumed snippets (from Rotten Tomatoes webpages) for reviews marked with |
|
|
``fresh'' are positive, and those for reviews marked with ``rotten'' are |
|
|
negative. |
|
|
|
|
|
## Preprocessing |
|
|
|
|
|
To make csv with text and label field, we use the following script. |
|
|
|
|
|
```python3 |
|
|
import csv |
|
|
import random |
|
|
|
|
|
# NOTE: The encoding of original file is "latin_1". We will change it to "utf8". |
|
|
with open("rt-polarity.pos", encoding="latin_1") as f: |
|
|
texts_pos = [line.strip() for line in f] |
|
|
with open("rt-polarity.neg", encoding="latin_1") as f: |
|
|
texts_neg = [line.strip() for line in f] |
|
|
|
|
|
rows_pos = [{"text": text, "label": 1} for text in texts_pos] |
|
|
rows_neg = [{"text": text, "label": 0} for text in texts_pos] |
|
|
|
|
|
# NOTE: For fair validation, we split it into train and test. Also, for the research who wants to use different setting, we provide whole setting. |
|
|
# NOTE: We follow the split setting in LM-BFF paper. |
|
|
rows_whole = rows_pos + rows_neg |
|
|
random.Random(42).shuffle(rows_whole) |
|
|
rows_test, rows_train = rows_whole[:2000], rows_whole[2000:] |
|
|
|
|
|
with open("whole.csv", "w", encoding="utf8") as f: |
|
|
writer = csv.DictWriter(f, fieldnames=["text", "label"]) |
|
|
writer.writerows(rows_train) |
|
|
with open("train.csv", "w", encoding="utf8") as f: |
|
|
writer = csv.DictWriter(f, fieldnames=["text", "label"]) |
|
|
writer.writerows(rows_train) |
|
|
with open("test.csv", "w", encoding="utf8") as f: |
|
|
writer = csv.DictWriter(f, fieldnames=["text", "label"]) |
|
|
writer.writerows(rows_test) |
|
|
``` |
|
|
|
