RapidOrc121/BERT_sentiment_analysis
Text Classification
•
Updated
•
21
•
1
Dataset Viewer
Full Screen Viewer
Full Screen
The viewer is disabled because this dataset repo requires arbitrary Python code execution. Please consider
removing the
loading script
and relying on
automated data support
(you can use
convert_to_parquet
from the datasets library). If this is not possible, please
open a discussion
for direct help.
YAML Metadata
Error:
"configs[0]" must be of type object
YAML Metadata
Warning:
The task_ids "feeling-classification" is not in the official list: acceptability-classification, entity-linking-classification, fact-checking, intent-classification, language-identification, multi-class-classification, multi-label-classification, multi-input-text-classification, natural-language-inference, semantic-similarity-classification, sentiment-classification, topic-classification, semantic-similarity-scoring, sentiment-scoring, sentiment-analysis, hate-speech-detection, text-scoring, named-entity-recognition, part-of-speech, parsing, lemmatization, word-sense-disambiguation, coreference-resolution, extractive-qa, open-domain-qa, closed-domain-qa, news-articles-summarization, news-articles-headline-generation, dialogue-modeling, dialogue-generation, conversational, language-modeling, text-simplification, explanation-generation, abstractive-qa, open-domain-abstractive-qa, closed-domain-qa, open-book-qa, closed-book-qa, slot-filling, masked-language-modeling, keyword-spotting, speaker-identification, audio-intent-classification, audio-emotion-recognition, audio-language-identification, multi-label-image-classification, multi-class-image-classification, face-detection, vehicle-detection, instance-segmentation, semantic-segmentation, panoptic-segmentation, image-captioning, image-inpainting, image-colorization, super-resolution, grasping, task-planning, tabular-multi-class-classification, tabular-multi-label-classification, tabular-single-column-regression, rdf-to-text, multiple-choice-qa, multiple-choice-coreference-resolution, document-retrieval, utterance-retrieval, entity-linking-retrieval, fact-checking-retrieval, univariate-time-series-forecasting, multivariate-time-series-forecasting, visual-question-answering, document-question-answering
Dataset Card for TSATC: Twitter Sentiment Analysis Training Corpus
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 for adding this transformed dataset from the original one.
- Downloads last month
- 486
Homepage:
TSATC
Repository:
TSATC
Paper:
TSATC: Twitter Sentiment Analysis Training Corpus
Point of Contact:
Carlos Blanco
