Sentiment Roberta Large English

🚀 SiEBERT - English-Language Sentiment Classification

SiEBERT enables reliable binary sentiment analysis for various types of English-language text.

🚀 Quick Start

This model ("SiEBERT", prefix for "Sentiment in English") is a fine - tuned checkpoint of RoBERTa - large (Liu et al. 2019). It can perform reliable binary sentiment analysis on various English - language texts. For each text instance, it predicts either positive (1) or negative (0) sentiment. The model was fine - tuned and evaluated on 15 data sets from diverse text sources to enhance generalization across different text types (reviews, tweets, etc.). As a result, it outperforms models trained on only one type of text (e.g., movie reviews from the popular SST - 2 benchmark) when used on new data.

✨ Features

• Generalization: Fine - tuned on 15 diverse data sets, enhancing its performance across different text types.
• High Performance: Outperforms a DistilBERT - based model by more than 15 percentage points on average.
• Easy to Use: Can be used in Hugging Face pipelines with just a few lines of code.
• Fine - Tunable: Can serve as a starting point for further fine - tuning on specific data.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

The easiest way to use the model for single predictions is Hugging Face's sentiment analysis pipeline.

from transformers import pipeline
sentiment_analysis = pipeline("sentiment-analysis",model="siebert/sentiment-roberta-large-english")
print(sentiment_analysis("I love this!"))

Advanced Usage

If you want to predict sentiment for your own data, we provide an example script via Google Colab. You can load your data to a Google Drive and run the script for free on a Colab GPU. It's recommended to manually label a subset of your data to evaluate performance for your use case.

The model can also be used as a starting point for further fine - tuning of RoBERTa on your specific data. Please refer to Hugging Face's documentation for further details and example code.

📚 Documentation

For performance benchmark values across various sentiment analysis contexts, please refer to our paper (Hartmann et al. 2023).

🔧 Technical Details

To evaluate the performance of our general - purpose sentiment analysis model, we set aside an evaluation set from each data set, which was not used for training. On average, our model outperforms a DistilBERT - based model (which is solely fine - tuned on the popular SST - 2 data set) by more than 15 percentage points (78.1 vs. 93.2 percent). As a robustness check, we evaluate the model in a leave - one - out manner (training on 14 data sets, evaluating on the one left out), which decreases model performance by only about 3 percentage points on average and underscores its generalizability. Model performance is given as evaluation set accuracy in percent.

Fine - tuning hyperparameters

• learning_rate = 2e - 5
• num_train_epochs = 3.0
• warmump_steps = 500
• weight_decay = 0.01

Other values were left at their defaults as listed here.

📄 License

No license information is provided in the original document.

📖 Citation

Please cite this paper (Published in the IJRM) when you use our model.

@article{hartmann2023,
title = {More than a Feeling: Accuracy and Application of Sentiment Analysis},
journal = {International Journal of Research in Marketing},
volume = {40},
number = {1},
pages = {75-87},
year = {2023},
doi = {https://doi.org/10.1016/j.ijresmar.2022.05.005},
url = {https://www.sciencedirect.com/science/article/pii/S0167811622000477},
author = {Jochen Hartmann and Mark Heitmann and Christian Siebert and Christina Schamp},
}

If you have any questions or feedback, feel free to reach out to [christian.siebert@uni - hamburg.de](mailto:christian.siebert@uni - hamburg.de).