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---
license: unknown
datasets:
- anilguven/turkish_tweet_emotion_dataset
language:
- tr
metrics:
- accuracy
- f1
- precision
- recall
tags:
- turkish
- tweet
- emotion
- sentiment
- bert
---
### Model Info
This model was developed/finetuned for tweet emotion detection task for the Turkish Language. This model was finetuned via tweet dataset. This dataset contains 5 classes: angry, happy, sad, surprised and afraid.
- LABEL_0: angry
- LABEL_1: afraid
- LABEL_2: happy
- LABEL_3: surprised
- LABEL_4: sad
### Model Sources
<!-- Provide the basic links for the model. -->
- **Dataset:** https://huggingface.co/datasets/anilguven/turkish_tweet_emotion_dataset
- **Paper:** https://ieeexplore.ieee.org/document/9559014
- **Demo-Coding [optional]:** https://github.com/anil1055/Turkish_tweet_emotion_analysis_with_language_models
- **Finetuned from model [optional]:** https://huggingface.co/dbmdz/distilbert-base-turkish-cased
#### Preprocessing
You must apply removing stopwords, stemming, or lemmatization process for Turkish.
### Results
- eval_loss = 0.05249839214870008
- mcc = 0.9828118433102754
- Accuracy: %98.63
## Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
*@INPROCEEDINGS{9559014,
author={Guven, Zekeriya Anil},
booktitle={2021 6th International Conference on Computer Science and Engineering (UBMK)},
title={Comparison of BERT Models and Machine Learning Methods for Sentiment Analysis on Turkish Tweets},
year={2021},
volume={},
number={},
pages={98-101},
keywords={Computer science;Sentiment analysis;Analytical models;Social networking (online);Computational modeling;Bit error rate;Random forests;Sentiment Analysis;BERT;Machine Learning;Text Classification;Tweet Analysis.},
doi={10.1109/UBMK52708.2021.9559014}}*
**APA:**
*Guven, Z. A. (2021, September). Comparison of BERT models and machine learning methods for sentiment analysis on Turkish tweets. In 2021 6th International Conference on Computer Science and Engineering (UBMK) (pp. 98-101). IEEE.* |