Model Card for Sentiment Analysis Model

Model Details

Model Name: Sentiment Analysis Model(News) Model Version: 1.01.1 Model Type: Natural Language Processing (NLP) Model Architecture: Bidirectional LSTM with Embedding layer Model Input: Preprocessed text data Model Output: Predicted sentiment category (positive, negative, or neutral)

Model Performance

Accuracy: 0.95 Loss: 0.35 Dataset: News articles dataset Dataset Size: 8.5m samples Performance Metrics: Categorical cross-entropy loss and accuracy

Model Intended Use

The model is intended to be used for sentiment analysis of news article headlines. The model is not intended to be used for sentiment analysis of other types of text data, such as social media posts or product reviews. The model is intended for use in research and development projects related to sentiment analysis.

Ethical Considerations

The model was trained on a dataset of news articles, which may contain biased or subjective language. The model may produce biased results for certain types of news articles or for certain demographics of readers. The model does not take into account the context or background of the news article, which may impact the accuracy of the sentiment analysis. The model should be used in conjunction with human review and interpretation to ensure that the sentiment analysis is accurate and appropriate.

Model Limitations

The model is limited by the quality and representativeness of the dataset on which it was trained. The model may not perform well on news articles that contain highly complex or nuanced language. The model does not take into account the tone or emotion of the news article, which may impact the sentiment analysis.

Model Training Details

Training Algorithm: Adam optimizer Number of Epochs: 10 Training Time: 32 hours Training Hardware: NVIDIA Tesla P100 GPU Data Preprocessing: Tokenization, stopword removal, lemmatization, and padding Data Augmentation: None Validation Split: 20% of the dataset Hyperparameters: Vocabulary size of 200,000, embedding dimension of 300, and sequence length of 100

License and Intellectual Property

The model and associated code are licensed under the Apache License 2.0. The model was developed by [Pavan krishna Narne]. The model is open source and free to use for non-commercial purposes. Commercial use of the model requires permission from [Pavan krishna Narne].