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Datasets:
tyqiangz
/
multilingual-sentiments

Tasks:
Text Classification
Modalities:
Text
Sub-tasks:
sentiment-analysis
sentiment-classification
Languages:
German
English
Spanish
Size:
100K - 1M
Libraries:
Datasets
License:
Dataset card Viewer Files Community
3
tyqiangz commited on
Commit
1a50b3c
•
2 Parent(s): 48d1186 e08b5e9

added data source column to all dataset splits

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Files changed (3) hide show
  1. README.md +30 -0
  2. dataset_infos.json +1 -1
  3. multilingual-sentiments.py +24 -19
README.md CHANGED
@@ -1,5 +1,31 @@
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  ---
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  license: apache-2.0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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  # Multilingual Sentiments Dataset
@@ -9,3 +35,7 @@ A collection of multilingual sentiments datasets grouped into 3 classes -- posit
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  Most multilingual sentiment datasets are either 2-class positive or negative, 5-class ratings of products reviews (e.g. Amazon multilingual dataset) or multiple classes of emotions. However, to an average person, sometimes positive, negative and neutral classes suffice and are more straightforward to perceive and annotate. Also, a positive/negative classification is too naive, most of the text in the world is actually neutral in sentiment. Furthermore, most multilingual sentiment datasets don't include Asian languages (e.g. Malay, Indonesian) and are dominated by Western languages (e.g. English, German).
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  Git repo: https://github.com/tyqiangz/multilingual-sentiment-datasets
 
 
 
 
 
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  ---
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  license: apache-2.0
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+ language:
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+ - de
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+ - en
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+ - es
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+ - fr
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+ - ja
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+ - zh
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+ - id
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+ - ar
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+ - hi
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+ - it
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+ - ms
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+ - pt
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+ multilinguality:
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+ - monolingual
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+ - multilingual
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+ size_categories:
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+ - 100K<n<1M
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+ - 1M<n<10M
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+ task_ids:
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+ - text-classification
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+ - sentiment-classification
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+ - sentiment-analysis
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+ task_categories:
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+ - text-classification
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+ - sentiment-analysis
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  ---
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  # Multilingual Sentiments Dataset
 
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  Most multilingual sentiment datasets are either 2-class positive or negative, 5-class ratings of products reviews (e.g. Amazon multilingual dataset) or multiple classes of emotions. However, to an average person, sometimes positive, negative and neutral classes suffice and are more straightforward to perceive and annotate. Also, a positive/negative classification is too naive, most of the text in the world is actually neutral in sentiment. Furthermore, most multilingual sentiment datasets don't include Asian languages (e.g. Malay, Indonesian) and are dominated by Western languages (e.g. English, German).
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  Git repo: https://github.com/tyqiangz/multilingual-sentiment-datasets
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+
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+ ## Dataset Description
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+
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+ - **Webpage:** https://github.com/tyqiangz/multilingual-sentiment-datasets
dataset_infos.json CHANGED
@@ -1 +1 @@
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- {"id": {"description": "This sentence-level sentiment analysis dataset (Purwarianti and Crisdayanti, 2019)\nis a collection of comments and reviews in Indonesian obtained from multiple online\nplatforms. The text was crawled and then annotated by several Indonesian linguists\nto construct this dataset. There are three possible sentiments on the SmSA\ndataset: positive, negative, and neutral.", "citation": "@inproceedings{purwarianti2019improving,\n title={Improving Bi-LSTM Performance for Indonesian Sentiment Analysis Using Paragraph Vector},\n author={Ayu Purwarianti and Ida Ayu Putu Ari Crisdayanti},\n booktitle={Proceedings of the 2019 International Conference of Advanced Informatics: Concepts, Theory and Applications (ICAICTA)},\n pages={1--5},\n year={2019},\n organization={IEEE}\n}", "homepage": "https://www.indobenchmark.com/", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 3, "names": ["positive", "neutral", "negative"], "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "multilingual-sentiments", "config_name": "id", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": 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multilingual-sentiments.py CHANGED
@@ -16,6 +16,7 @@
16
 
17
  import csv
18
  import textwrap
 
19
 
20
  import datasets
21
 
@@ -71,25 +72,25 @@ class MultilingualSentiments(datasets.GeneratorBasedBuilder):
71
 
72
  BUILDER_CONFIGS = []
73
 
74
- # BUILDER_CONFIGS.append(
75
- # MultilingualSentimentsConfig(
76
- # name="all",
77
- # description=textwrap.dedent(
78
- # f"""\
79
- # All datasets."""
80
- # ),
81
- # text_features={"text": "text", "source": "source", "language": "language"},
82
- # label_classes=["positive", "neutral", "negative"],
83
- # label_column="label",
84
- # train_url=f"https://raw.githubusercontent.com/tyqiangz/multilingual-sentiment-datasets/main/data/all/train.csv",
85
- # valid_url=f"https://raw.githubusercontent.com/tyqiangz/multilingual-sentiment-datasets/main/data/all/valid.csv",
86
- # test_url=f"https://raw.githubusercontent.com/tyqiangz/multilingual-sentiment-datasets/main/data/all/test.csv",
87
- # citation=textwrap.dedent(
88
- # f"""\
89
- # All citation"""
90
- # ),
91
- # ),
92
- # )
93
 
94
  for lang in LANGUAGES:
95
  BUILDER_CONFIGS.append(
@@ -143,6 +144,10 @@ class MultilingualSentiments(datasets.GeneratorBasedBuilder):
143
 
144
  df = pd.read_csv(filepath)
145
 
 
 
 
 
146
  for id_, row in df.iterrows():
147
  if self.config.name != "all":
148
  text = row["text"]
 
16
 
17
  import csv
18
  import textwrap
19
+ import pandas as pd
20
 
21
  import datasets
22
 
 
72
 
73
  BUILDER_CONFIGS = []
74
 
75
+ BUILDER_CONFIGS.append(
76
+ MultilingualSentimentsConfig(
77
+ name="all",
78
+ description=textwrap.dedent(
79
+ f"""\
80
+ All datasets."""
81
+ ),
82
+ text_features={"text": "text", "source": "source", "language": "language"},
83
+ label_classes=["positive", "neutral", "negative"],
84
+ label_column="label",
85
+ train_url=f"https://raw.githubusercontent.com/tyqiangz/multilingual-sentiment-datasets/main/data/all/train.csv",
86
+ valid_url=f"https://raw.githubusercontent.com/tyqiangz/multilingual-sentiment-datasets/main/data/all/valid.csv",
87
+ test_url=f"https://raw.githubusercontent.com/tyqiangz/multilingual-sentiment-datasets/main/data/all/test.csv",
88
+ citation=textwrap.dedent(
89
+ f"""\
90
+ All citation"""
91
+ ),
92
+ ),
93
+ )
94
 
95
  for lang in LANGUAGES:
96
  BUILDER_CONFIGS.append(
 
144
 
145
  df = pd.read_csv(filepath)
146
 
147
+ print('-'*100)
148
+ print(df.head())
149
+ print('-'*100)
150
+
151
  for id_, row in df.iterrows():
152
  if self.config.name != "all":
153
  text = row["text"]