first readme dataset card

README.md

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+---
+pretty_name: "TSATC: Twitter Sentiment Analysis Training Corpus"
+annotations_creators:
+- expert-generated
+language_creators:
+- other
+languages:
+- en
+licenses:
+- other
+multilinguality:
+- monolingual
+size_categories:
+- 100K<n<1M
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids:
+- feeling-classification
+paperswithcode_id: other
+configs:
+- None
+---
+# Dataset Card for TSATC: Twitter Sentiment Analysis Training Corpus
+## 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:** [TSATC](https://github.com/cblancac/SentimentAnalysisBert/blob/main/data)
+- **Repository:** [TSATC](https://github.com/cblancac/SentimentAnalysisBert/blob/main/data)
+- **Paper:** [TSATC: Twitter Sentiment Analysis Training Corpus](http://thinknook.com/twitter-sentiment-analysis-training-corpus-dataset-2012-09-22/)
+- **Point of Contact:** [Carlos Blanco](carblacac7@gmail.com)
+### Dataset Summary
+TSATC: Twitter Sentiment Analysis Training Corpus
+The original Twitter Sentiment Analysis Dataset contains 1,578,627 classified tweets, each row is marked as 1 for positive sentiment and 0 for negative sentiment. It can be downloaded from http://thinknook.com/wp-content/uploads/2012/09/Sentiment-Analysis-Dataset.zip. 
+The dataset is based on data from the following two sources:
+
+University of Michigan Sentiment Analysis competition on Kaggle
+Twitter Sentiment Corpus by Niek Sanders
+
+This dataset has been transformed, selecting in a random way a subset of them, applying a cleaning process, and dividing them between the test and train subsets, keeping a balance between the number of positive and negative tweets within each of these subsets. These two files can be founded on https://github.com/cblancac/SentimentAnalysisBert/blob/main/data. 
+
+Finally, the train subset has been divided in two smallest datasets, train (80%) and validation (20%). The final dataset has been created with these two new subdatasets plus the previous test dataset. 
+### Supported Tasks and Leaderboards
+[More Information Needed]
+### Languages
+The text in the dataset is in English.
+## Dataset Structure
+### Data Instances
+Below are two examples from the dataset:
+
+
+
+
+|     | Text                              | Feeling |
+| :-- | :----------------------------     | :------ |
+| (1) | blaaah. I don't feel good aagain. | 0       |
+| (2) | My birthday is coming June 3.     | 1       |
+
+### Data Fields
+In the final dataset, all files are in the JSON format with f columns:
+
+| Column Name   | Data                        |
+| :------------ | :-------------------------- |
+| text          | A sentence (or tweet)       |
+| feeling       | The feeling of the sentence |
+
+Each feeling has two possible values: `0` indicates the sentence has a negative sentiment, while `1` indicates a positive feeling.
+### Data Splits
+The number of examples and the proportion sentiments are shown below:
+
+| Data                | Train   | Validation    | Test  |
+| :------------------ | ------: | ------------: | ----: |
+| Size                | 119.988 | 29.997      |  61.998 |
+| Labeled positive    | 60.019  | 14.947      | 31029   |
+| Labeled negative    | 59.969  | 15.050      | 30969   |
+## Dataset Creation
+### Curation Rationale
+Existing paraphrase identification datasets lack sentence pairs that have high lexical overlap without being paraphrases. Models trained on such data fail to distinguish pairs like *flights from New York to Florida* and *flights from Florida to New York*.
+### Source Data
+#### Initial Data Collection and Normalization
+[More Information Needed]
+#### Who are the source language producers?
+Mentioned above.
+### Annotations
+#### Annotation process
+[More Information Needed]
+#### Who are the annotators?
+[More Information Needed]
+### Personal and Sensitive Information
+[More Information Needed]
+## Considerations for Using the Data
+### Social Impact of Dataset
+[More Information Needed]
+### Discussion of Biases
+[More Information Needed]
+### Other Known Limitations
+[More Information Needed]
+## Additional Information
+### Dataset Curators
+[More Information Needed]
+### Citation Information
+```
+@InProceedings{paws2019naacl,
+  title = {{TSATC: Twitter Sentiment Analysis Training Corpus}},
+  author = {Ibrahim Naji},
+  booktitle = {thinknook},
+  year = {2012}
+}
+```
+### Contributions
+Thanks to myself [@carblacac](https://github.com/cblancac/) for adding this transformed dataset from the original one.