Create README.md

README.md

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+---
+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/
+
+## 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)
+```