File size: 5,009 Bytes
a150770
 
 
 
 
 
b4d48e9
a150770
b4d48e9
0ccca88
a150770
 
 
7a51f6a
a150770
 
 
1802429
 
a150770
1802429
a150770
4800560
0ee3024
e084004
 
 
 
 
 
 
 
 
eff382b
 
 
 
e084004
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
a150770
 
 
 
 
 
 
0814f9c
a150770
 
 
0814f9c
 
a150770
 
 
 
 
 
 
 
 
 
 
 
 
3b25dd6
a150770
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3b25dd6
 
 
e084004
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
---
annotations_creators:
- expert-generated
- machine-generated
language_creators:
- found
language:
- de
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
- text-classification
task_ids:
- text-scoring
- sentiment-scoring
- part-of-speech
pretty_name: SentiWS
dataset_info:
- config_name: pos-tagging
  features:
  - name: word
    dtype: string
  - name: pos-tag
    dtype:
      class_label:
        names:
          '0': NN
          '1': VVINF
          '2': ADJX
          '3': ADV
  splits:
  - name: train
    num_bytes: 75530
    num_examples: 3471
  download_size: 97748
  dataset_size: 75530
- config_name: sentiment-scoring
  features:
  - name: word
    dtype: string
  - name: sentiment-score
    dtype: float32
  splits:
  - name: train
    num_bytes: 61646
    num_examples: 3471
  download_size: 97748
  dataset_size: 61646
---

# Dataset Card for SentiWS

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Homepage:** https://wortschatz.uni-leipzig.de/en/download
- **Repository:** [Needs More Information]
- **Paper:** http://www.lrec-conf.org/proceedings/lrec2010/pdf/490_Paper.pdf
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]

### Dataset Summary

SentimentWortschatz, or SentiWS for short, is a publicly available German-language resource for sentiment analysis, opinion mining etc. It lists positive and negative polarity bearing words weighted within the interval of [-1; 1] plus their part of speech tag, and if applicable, their inflections. The current version of SentiWS contains around 1,650 positive and 1,800 negative words, which sum up to around 16,000 positive and 18,000 negative word forms incl. their inflections, respectively. It not only contains adjectives and adverbs explicitly expressing a sentiment, but also nouns and verbs implicitly containing one.

### Supported Tasks and Leaderboards

Sentiment-Scoring, Pos-Tagging

### Languages

German

## Dataset Structure

### Data Instances
For pos-tagging:
```
{ 
"word":"Abbau"
"pos_tag": 0
}
```
For sentiment-scoring:
```
{
"word":"Abbau"
"sentiment-score":-0.058
}
``` 

### Data Fields

SentiWS is UTF8-encoded text.
For pos-tagging:
- word: one word as a string,
- pos_tag: the part-of-speech tag of the word as an integer,
For sentiment-scoring:
- word: one word as a string,
- sentiment-score: the sentiment score of the word as a float between -1 and 1,

The POS tags are ["NN", "VVINF", "ADJX", "ADV"] -> ["noun", "verb", "adjective", "adverb"], and positive and negative polarity bearing words are weighted within the interval of [-1, 1].

### Data Splits

 train: 1,650 negative and 1,818 positive words

## Dataset Creation

### Curation Rationale

[Needs More Information]

### Source Data

#### Initial Data Collection and Normalization

[Needs More Information]

#### Who are the source language producers?

[Needs More Information]

### Annotations

#### Annotation process

[Needs More Information]

#### Who are the annotators?

[Needs More Information]

### Personal and Sensitive Information

[Needs More Information]

## Considerations for Using the Data

### Social Impact of Dataset

[Needs More Information]

### Discussion of Biases

[Needs More Information]

### Other Known Limitations

[Needs More Information]

## Additional Information

### Dataset Curators

[Needs More Information]

### Licensing Information

Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported License

### Citation Information
@INPROCEEDINGS{remquahey2010,
title = {SentiWS -- a Publicly Available German-language Resource for Sentiment Analysis},
booktitle = {Proceedings of the 7th International Language Resources and Evaluation (LREC'10)},
author = {Remus, R. and Quasthoff, U. and Heyer, G.},
year = {2010}
}
### Contributions

Thanks to [@harshalmittal4](https://github.com/harshalmittal4) for adding this dataset.