task_categories:text-classification task_categories:zero-shot-classification language:en language:tl license:cc-by-4.0 size_categories:10K<n<100K format:csv modality:text library:datasets library:pandas library:mlcroissant library:polars region:us code-switching sentiment analysis low-resource languages Taglish Tagalog Filipino
---
license: cc-by-4.0
task_categories:
- text-classification
- zero-shot-classification
language:
- en
- tl
tags:
- code-switching
- sentiment analysis
- low-resource languages
- Taglish
- Tagalog
- Filipino
size_categories:
- 10K<n<100K
---

# Dataset Card for Sentiment-Annotated Taglish Product and Service Reviews (SentiTaglish: Products and Services)

## Dataset Summary

Sentiment-Annotated Taglish Product and Service Reviews (SentiTaglish: Products and Services) is a gold standard, sentiment-annotated corpus for the Tagalog-English language pair. It contains 10,510 product and service reviews which were manually labeled by three human annotators according to four sentiment classes: Positive, Negative, Neutral, and Mixed.

## Supported Tasks and Leaderboards

Sentiment classification of multilingual text with code-switching / code-mixing.

## Languages

- Tagalog
- English

## Dataset Structure

### Data Fields

* `review`: a string containing the body of the review
* `sentiment`: an integer containing the label encoding of the gold-truth label provided by the human annotators

### Label encoding
* 1 - Negative
* 2 - Neutral
* 3 - Positive
* 4 - Mixed

## Dataset Creation and Annotation

The data set was created using publicly available online service and product reviews from Google Maps Reviews and Shopee Philippines. Only the rating and review fields were collected and stored.

Three annotators were tasked to manually label the data set. The first two annotators labeled the same full set of reviews. Any disagreements were sent to a third annotator.

The validity and reliability of the dataset is supported by a strong agreement among the human annotators. Fleiss' kappa was used to measure the agreement between the annotations of the first two taggers while Krippendorff’s alpha was used to measure the agreement among all three annotators. Both reported strong agreement at 0.82 and 0.83, respectively. In addition, a strong positive correlation between user ratings and manual annotations was noted.

## Personal and Sensitive Information

No personal information were collected and stored.

## Licensing Information

The SentiTaglish: Products and Services data set version 1.0 is released under the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) license.
  
## Citation Information

TBA