
# Dataset Card for Vietnamese Students’ Feedback Corpus

## Table of Contents
- [Table of Contents](#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:** https://sites.google.com/uit.edu.vn/uit-nlp/datasets-projects#h.p_4Brw8L-cbfTe
- **Repository:**
- **Paper:** [UIT-VSFC: Vietnamese Students’ Feedback Corpus for Sentiment Analysis](https://www.researchgate.net/publication/329645066_UIT-VSFC_Vietnamese_Students'_Feedback_Corpus_for_Sentiment_Analysis)
- **Leaderboard:**
- **Point of Contact:**

### Dataset Summary

Students’ feedback is a vital resource for the interdisciplinary research involving the combining of two different
research fields between sentiment analysis and education.

Vietnamese Students’ Feedback Corpus (UIT-VSFC) is the resource consists of over 16,000 sentences which are
human-annotated with two different tasks: sentiment-based and topic-based classifications.

To assess the quality of our corpus, we measure the annotator agreements and classification evaluation on the
UIT-VSFC corpus. As a result, we obtained the inter-annotator agreement of sentiments and topics with more than over
91% and 71% respectively. In addition, we built the baseline model with the Maximum Entropy classifier and achieved
approximately 88% of the sentiment F1-score and over 84% of the topic F1-score.

### Supported Tasks and Leaderboards

[More Information Needed]

### Languages

The language of the dataset text sentence is Vietnamese (`vi`).

## Dataset Structure

### Data Instances

An instance example:
```
{
  'sentence': 'slide giáo trình đầy đủ .', 
  'sentiment': 2, 
  'topic': 1
}
```

### Data Fields

- `sentence` (str): Text sentence.
- `sentiment`: Sentiment class, with values 0 (negative), 1 (neutral) and 2 (positive).
- `topic`: Topic class, with values 0 (lecturer), 1 (training_program), 2 (facility) and 3 (others).

### Data Splits

The dataset is split in train, validation and test.

|                    |  Tain | Validation | Test |
|--------------------|------:|-----------:|-----:|
| Number of examples | 11426 |       1583 | 3166 |


## Dataset Creation

### Curation Rationale

[More Information Needed]

### Source Data

#### Initial Data Collection and Normalization

[More Information Needed]

#### Who are the source language producers?

[More Information Needed]

### 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]

### Licensing Information

Unknown.

### Citation Information

```
@InProceedings{8573337,
  author={Nguyen, Kiet Van and Nguyen, Vu Duc and Nguyen, Phu X. V. and Truong, Tham T. H. and Nguyen, Ngan Luu-Thuy},
  booktitle={2018 10th International Conference on Knowledge and Systems Engineering (KSE)},
  title={UIT-VSFC: Vietnamese Students’ Feedback Corpus for Sentiment Analysis},
  year={2018},
  volume={},
  number={},
  pages={19-24},
  doi={10.1109/KSE.2018.8573337}
}
```

### Contributions

Thanks to [@albertvillanova](https://github.com/albertvillanova) for adding this dataset.

task_categories:text-classification task_ids:sentiment-classification task_ids:topic-classification annotations_creators:no-annotation language_creators:found multilinguality:monolingual size_categories:10K<n<100K source_datasets:original language:vi license:unknown region:us
{"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["vi"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["sentiment-classification", "topic-classification"], "pretty_name": "Vietnamese Students\u2019 Feedback Corpus"}
TAGS
#task_categories-text-classification #task_ids-sentiment-classification #task_ids-topic-classification #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Vietnamese #license-unknown #region-us 

Dataset Card for Vietnamese Students’ Feedback Corpus
=====================================================


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


* 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: URL
* Repository:
* Paper: UIT-VSFC: Vietnamese Students’ Feedback Corpus for Sentiment Analysis
* Leaderboard:
* Point of Contact:


### Dataset Summary


Students’ feedback is a vital resource for the interdisciplinary research involving the combining of two different
research fields between sentiment analysis and education.


Vietnamese Students’ Feedback Corpus (UIT-VSFC) is the resource consists of over 16,000 sentences which are
human-annotated with two different tasks: sentiment-based and topic-based classifications.


To assess the quality of our corpus, we measure the annotator agreements and classification evaluation on the
UIT-VSFC corpus. As a result, we obtained the inter-annotator agreement of sentiments and topics with more than over
91% and 71% respectively. In addition, we built the baseline model with the Maximum Entropy classifier and achieved
approximately 88% of the sentiment F1-score and over 84% of the topic F1-score.


### Supported Tasks and Leaderboards


### Languages


The language of the dataset text sentence is Vietnamese ('vi').


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


### Data Instances


An instance example:


### Data Fields


* 'sentence' (str): Text sentence.
* 'sentiment': Sentiment class, with values 0 (negative), 1 (neutral) and 2 (positive).
* 'topic': Topic class, with values 0 (lecturer), 1 (training\_program), 2 (facility) and 3 (others).


### Data Splits


The dataset is split in train, validation and test.



Dataset Creation
----------------


### Curation Rationale


### Source Data


#### Initial Data Collection and Normalization


#### Who are the source language producers?


### 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


### Licensing Information


Unknown.


### Contributions


Thanks to @albertvillanova for adding this dataset.



### Dataset Summary


Students’ feedback is a vital resource for the interdisciplinary research involving the combining of two different
research fields between sentiment analysis and education.


Vietnamese Students’ Feedback Corpus (UIT-VSFC) is the resource consists of over 16,000 sentences which are
human-annotated with two different tasks: sentiment-based and topic-based classifications.


To assess the quality of our corpus, we measure the annotator agreements and classification evaluation on the
UIT-VSFC corpus. As a result, we obtained the inter-annotator agreement of sentiments and topics with more than over
91% and 71% respectively. In addition, we built the baseline model with the Maximum Entropy classifier and achieved
approximately 88% of the sentiment F1-score and over 84% of the topic F1-score. ### Supported Tasks and Leaderboards ### Languages


The language of the dataset text sentence is Vietnamese ('vi').


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


An instance example: ### Data Fields


* 'sentence' (str): Text sentence.
* 'sentiment': Sentiment class, with values 0 (negative), 1 (neutral) and 2 (positive).
* 'topic': Topic class, with values 0 (lecturer), 1 (training\_program), 2 (facility) and 3 (others). ### Data Splits


The dataset is split in train, validation and test.



Dataset Creation
---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### 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 ### Licensing Information


Unknown. ### Contributions


Thanks to @albertvillanova for adding this dataset.
TAGS
#task_categories-text-classification #task_ids-sentiment-classification #task_ids-topic-classification #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Vietnamese #license-unknown #region-us 
 ### Dataset Summary


Students’ feedback is a vital resource for the interdisciplinary research involving the combining of two different
research fields between sentiment analysis and education.


Vietnamese Students’ Feedback Corpus (UIT-VSFC) is the resource consists of over 16,000 sentences which are
human-annotated with two different tasks: sentiment-based and topic-based classifications.


To assess the quality of our corpus, we measure the annotator agreements and classification evaluation on the
UIT-VSFC corpus. As a result, we obtained the inter-annotator agreement of sentiments and topics with more than over
91% and 71% respectively. In addition, we built the baseline model with the Maximum Entropy classifier and achieved
approximately 88% of the sentiment F1-score and over 84% of the topic F1-score. ### Supported Tasks and Leaderboards ### Languages


The language of the dataset text sentence is Vietnamese ('vi').


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


An instance example: ### Data Fields


* 'sentence' (str): Text sentence.
* 'sentiment': Sentiment class, with values 0 (negative), 1 (neutral) and 2 (positive).
* 'topic': Topic class, with values 0 (lecturer), 1 (training\_program), 2 (facility) and 3 (others). ### Data Splits


The dataset is split in train, validation and test.



Dataset Creation
---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### 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 ### Licensing Information


Unknown. ### Contributions


Thanks to @albertvillanova for adding this dataset.
passage: TAGS
#task_categories-text-classification #task_ids-sentiment-classification #task_ids-topic-classification #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Vietnamese #license-unknown #region-us 
### Dataset Summary


Students’ feedback is a vital resource for the interdisciplinary research involving the combining of two different
research fields between sentiment analysis and education.


Vietnamese Students’ Feedback Corpus (UIT-VSFC) is the resource consists of over 16,000 sentences which are
human-annotated with two different tasks: sentiment-based and topic-based classifications.


To assess the quality of our corpus, we measure the annotator agreements and classification evaluation on the
UIT-VSFC corpus. As a result, we obtained the inter-annotator agreement of sentiments and topics with more than over
91% and 71% respectively. In addition, we built the baseline model with the Maximum Entropy classifier and achieved
approximately 88% of the sentiment F1-score and over 84% of the topic F1-score.### Supported Tasks and Leaderboards### Languages


The language of the dataset text sentence is Vietnamese ('vi').


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


An instance example:### Data Fields


* 'sentence' (str): Text sentence.
* 'sentiment': Sentiment class, with values 0 (negative), 1 (neutral) and 2 (positive).
* 'topic': Topic class, with values 0 (lecturer), 1 (training\_program), 2 (facility) and 3 (others).### Data Splits


The dataset is split in train, validation and test.



Dataset Creation
----------------### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information


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