

# Sentiment Analysis model based on SlovakBERT

This is a sentiment analysis classifier based on [SlovakBERT](https://huggingface.co/gerulata/slovakbert). The model can distinguish three level of sentiment:

- `-1` - Negative sentiment
- `0` - Neutral sentiment
- `1` - Positive setiment

The model was fine-tuned using Slovak part of [Multilingual Twitter Sentiment Analysis Dataset](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0155036) [Mozetič et al 2016] containing 50k manually annotated Slovak tweets. As such, it is fine-tuned for tweets and it is not advised to use the model for general-purpose sentiment analysis.

## Results

The model was evaluated in [our paper](https://arxiv.org/abs/2109.15254) [Pikuliak et al 2021, Section 4.4]. It achieves \\(0.67\\) F1-score on the original dataset and \\(0.58\\) F1-score on general reviews dataset.

## Cite

```
@inproceedings{pikuliak-etal-2022-slovakbert,
    title = "{S}lovak{BERT}: {S}lovak Masked Language Model",
    author = "Pikuliak, Mat{\'u}{\v{s}}  and
      Grivalsk{\'y}, {\v{S}}tefan  and
      Kon{\^o}pka, Martin  and
      Bl{\v{s}}t{\'a}k, Miroslav  and
      Tamajka, Martin  and
      Bachrat{\'y}, Viktor  and
      Simko, Marian  and
      Bal{\'a}{\v{z}}ik, Pavol  and
      Trnka, Michal  and
      Uhl{\'a}rik, Filip",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.findings-emnlp.530",
    pages = "7156--7168",
    abstract = "We introduce a new Slovak masked language model called \textit{SlovakBERT}. This is to our best knowledge the first paper discussing Slovak transformers-based language models. We evaluate our model on several NLP tasks and achieve state-of-the-art results. This evaluation is likewise the first attempt to establish a benchmark for Slovak language models. We publish the masked language model, as well as the fine-tuned models for part-of-speech tagging, sentiment analysis and semantic textual similarity.",
}

```
{"language": ["sk"], "license": "cc", "tags": ["twitter", "sentiment-analysis"], "metrics": ["f1"], "widget": [{"text": "Najkraj\u0161ia viano\u010dn\u00e1 reklama: Toto mil\u00e9 video v\u00e1m vyk\u00fazli \u010darovn\u00fa atmosf\u00e9ru: Vianoce sa nezadr\u017eate\u013ene bl\u00ed\u017eia."}, {"text": "A op\u00e4\u0165 sa objavili nebezpe\u010dn\u00e9 v\u00fdrobky. Pozrite sa, \u010di ich nem\u00e1te doma"}]}
transformers pytorch roberta text-classification twitter sentiment-analysis sk arxiv:2109.15254 license:cc autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #twitter #sentiment-analysis #sk #arxiv-2109.15254 #license-cc #autotrain_compatible #endpoints_compatible #region-us 



# Sentiment Analysis model based on SlovakBERT

This is a sentiment analysis classifier based on SlovakBERT. The model can distinguish three level of sentiment:

- '-1' - Negative sentiment
- '0' - Neutral sentiment
- '1' - Positive setiment

The model was fine-tuned using Slovak part of Multilingual Twitter Sentiment Analysis Dataset [Mozetič et al 2016] containing 50k manually annotated Slovak tweets. As such, it is fine-tuned for tweets and it is not advised to use the model for general-purpose sentiment analysis.

## Results

The model was evaluated in our paper [Pikuliak et al 2021, Section 4.4]. It achieves \\(0.67\\) F1-score on the original dataset and \\(0.58\\) F1-score on general reviews dataset.

## Cite


# Sentiment Analysis model based on SlovakBERT

This is a sentiment analysis classifier based on SlovakBERT. The model can distinguish three level of sentiment:

- '-1' - Negative sentiment
- '0' - Neutral sentiment
- '1' - Positive setiment

The model was fine-tuned using Slovak part of Multilingual Twitter Sentiment Analysis Dataset [Mozetič et al 2016] containing 50k manually annotated Slovak tweets. As such, it is fine-tuned for tweets and it is not advised to use the model for general-purpose sentiment analysis. ## Results

The model was evaluated in our paper [Pikuliak et al 2021, Section 4.4]. It achieves \\(0.67\\) F1-score on the original dataset and \\(0.58\\) F1-score on general reviews dataset. ## Cite
TAGS
#transformers #pytorch #roberta #text-classification #twitter #sentiment-analysis #sk #arxiv-2109.15254 #license-cc #autotrain_compatible #endpoints_compatible #region-us 
 # Sentiment Analysis model based on SlovakBERT

This is a sentiment analysis classifier based on SlovakBERT. The model can distinguish three level of sentiment:

- '-1' - Negative sentiment
- '0' - Neutral sentiment
- '1' - Positive setiment

The model was fine-tuned using Slovak part of Multilingual Twitter Sentiment Analysis Dataset [Mozetič et al 2016] containing 50k manually annotated Slovak tweets. As such, it is fine-tuned for tweets and it is not advised to use the model for general-purpose sentiment analysis. ## Results

The model was evaluated in our paper [Pikuliak et al 2021, Section 4.4]. It achieves \\(0.67\\) F1-score on the original dataset and \\(0.58\\) F1-score on general reviews dataset. ## Cite
passage: TAGS
#transformers #pytorch #roberta #text-classification #twitter #sentiment-analysis #sk #arxiv-2109.15254 #license-cc #autotrain_compatible #endpoints_compatible #region-us 
# Sentiment Analysis model based on SlovakBERT

This is a sentiment analysis classifier based on SlovakBERT. The model can distinguish three level of sentiment:

- '-1' - Negative sentiment
- '0' - Neutral sentiment
- '1' - Positive setiment

The model was fine-tuned using Slovak part of Multilingual Twitter Sentiment Analysis Dataset [Mozetič et al 2016] containing 50k manually annotated Slovak tweets. As such, it is fine-tuned for tweets and it is not advised to use the model for general-purpose sentiment analysis.## Results

The model was evaluated in our paper [Pikuliak et al 2021, Section 4.4]. It achieves \\(0.67\\) F1-score on the original dataset and \\(0.58\\) F1-score on general reviews dataset.## Cite