license: unknown
datasets:
- anilguven/turkish_tweet_emotion_dataset
language:
- tr
metrics:
- accuracy
- f1
- precision
- recall
tags:
- bert
- turkish
- emotion
- sentiment
- tweet
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
- 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/bert-base-turkish-uncased
Preprocessing
You must apply removing stopwords, stemming, or lemmatization process for Turkish.
Results
- eval_loss = 0.06813859832385788
- mcc = 0.9843707754295762
- Accuracy: %98.75
Citation
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.