# 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.
task_categories:text-classification annotations_creators:expert-generated language_creators:other multilinguality:monolingual size_categories:100K<n<1M source_datasets:original language:en license:apache-2.0 region:us
{"annotations_creators": ["expert-generated"], "language_creators": ["other"], "language": ["en"], "license": ["apache-2.0"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["feeling-classification"], "paperswithcode_id": "other", "pretty_name": "TSATC: Twitter Sentiment Analysis Training Corpus", "configs": ["None"]}
TAGS
#task_categories-text-classification #annotations_creators-expert-generated #language_creators-other #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-English #license-apache-2.0 #region-us 

Dataset Card for TSATC: Twitter Sentiment Analysis Training Corpus
==================================================================


Table of Contents
-----------------


* Dataset Description
	+ Dataset Summary
	+ Supported Tasks and Leaderboards
	+ Languages
* Dataset Structure
	+ Data Instances
	+ Data Fields
	+ Data Splits
* Dataset Creation
	+ Curation Rationale
	+ Source Data
	+ Annotations
	+ Personal and Sensitive Information
* Considerations for Using the Data
	+ Social Impact of Dataset
	+ Discussion of Biases
	+ Other Known Limitations
* Additional Information
	+ Dataset Curators
	+ Licensing Information
	+ Citation Information
	+ Contributions


Dataset Description
-------------------


* Homepage: TSATC
* Repository: TSATC
* Paper: TSATC: Twitter Sentiment Analysis Training Corpus
* Point of Contact: Carlos Blanco


### 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 URL
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 URL


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


### Languages


The text in the dataset is in English.


Dataset Structure
-----------------


### Data Instances


Below are two examples from the dataset:



### Data Fields


In the final dataset, all files are in the JSON format with f columns:



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:



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


#### Who are the source language producers?


Mentioned above.


### Annotations


#### Annotation process


#### Who are the annotators?


### Personal and Sensitive Information


Considerations for Using the Data
---------------------------------


### Social Impact of Dataset


### Discussion of Biases


### Other Known Limitations


Additional Information
----------------------


### Dataset Curators


### Contributions


Thanks to myself @carblacac for adding this transformed dataset from the original one.



### 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 URL
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 URL


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 ### Languages


The text in the dataset is in English.


Dataset Structure
----------------- ### Data Instances


Below are two examples from the dataset: ### Data Fields


In the final dataset, all files are in the JSON format with f columns:



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:



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 #### Who are the source language producers?


Mentioned above. ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations


Additional Information
---------------------- ### Dataset Curators ### Contributions


Thanks to myself @carblacac for adding this transformed dataset from the original one.
TAGS
#task_categories-text-classification #annotations_creators-expert-generated #language_creators-other #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-English #license-apache-2.0 #region-us 
 ### 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 URL
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 URL


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 ### Languages


The text in the dataset is in English.


Dataset Structure
----------------- ### Data Instances


Below are two examples from the dataset: ### Data Fields


In the final dataset, all files are in the JSON format with f columns:



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:



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 #### Who are the source language producers?


Mentioned above. ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations


Additional Information
---------------------- ### Dataset Curators ### Contributions


Thanks to myself @carblacac for adding this transformed dataset from the original one.