First commit

README.md

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+---
+language:
+- de
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: "train.parquet"
+  - split: test
+    path: "test.parquet"
+---
+
+## Information
+This dataset shows 1785 manually annotated tweets from German politicians during the election year 2021 (01.01.2021 - 31.12.2021).
+The tweets were annotated by 6 academics which were separated into two different groups. So every group of 3 people annotated the sentiment of ~900 tweets. For every tweet, the majority label was built. The annotation result had a moderate Kappa agreement. 
+
+## Preprocessing
+The source for this version of the dataset is located [here](https://github.com/NilsHellwig/Twitter_German_Federal_Election_Perception_2021/tree/main/Datasets/Schmidt2022).
+For better processing the line breaks of the texts are removed.
+The numbers for answers, retweets and favorites are removed from the text and also the phrase "Diesen Thread anzeigen" (Show this thread). Both aren't part of the tweet and were most likely added by the crawling tool.
+The preprocessing steps can be reproduced with the `cleaner.py` script.
+
+## Annotation
+The tweets were annotated as follows:
+- 1 if the sentiment of the tweet is positive
+- 2 if the sentiment of the tweet is negative
+- 3 if the sentiment of the tweet is neutral
+
+## Citation
+@inproceedings{schmidt-etal-2022-sentiment,
+    title = "Sentiment Analysis on {T}witter for the Major {G}erman Parties during the 2021 {G}erman Federal Election",
+    author = "Schmidt, Thomas  and
+      Fehle, Jakob  and
+      Weissenbacher, Maximilian  and
+      Richter, Jonathan  and
+      Gottschalk, Philipp  and
+      Wolff, Christian",
+    editor = "Schaefer, Robin  and
+      Bai, Xiaoyu  and
+      Stede, Manfred  and
+      Zesch, Torsten",
+    booktitle = "Proceedings of the 18th Conference on Natural Language Processing (KONVENS 2022)",
+    month = "12--15 " # sep,
+    year = "2022",
+    address = "Potsdam, Germany",
+    publisher = "KONVENS 2022 Organizers",
+    url = "https://aclanthology.org/2022.konvens-1.9",
+    pages = "74--87",
+}

cleaner.py

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+from datasets import load_dataset
+import re
+ds = load_dataset("test.csv")
+
+def prepro(datapoint):
+    pattern = "\s([0-9]\.){0,1}[0-9]{0,3}(?=\Z)"
+    text = datapoint["Embedded_text"].replace("\n", " ")
+    for i in range(3):
+        text = re.sub(pattern, "", text)
+    text = text.replace("Diesen Thread anzeigen", "").replace("  ", " ").replace("  ", " ")
+    datapoint["text"] = text
+    del datapoint["Embedded_text"]
+    return datapoint
+ds_f = ds.map(prepro)
+ds_f.to_parquet("test.parquet")

test.parquet

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+oid sha256:23a020bcbe3c949bbebbafff63c893d8c7e6ca3d2a1a8a9938018738d96a7e22
+size 81332

train.parquet

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+oid sha256:0c233718d6d36a69dbd992df0f0e4c30b67fd35e6b4404970dce9be1c296ec77
+size 326319