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{
"name": "22_Sentiment_Analysis_LSTM_IMDb_DL",
"query": "Could you help me set up a sentiment analysis project using an LSTM model and the IMDb dataset? Please implement data cleaning in `src/data_loader.py`, including the removal of stop words and punctuation. Use word embeddings to convert the text to a numerical format and save these embeddings under `models/saved_models/`. Then use these embeddings as input of an LSTM model, which should be implemented in `src/model.py`. Save the classification report to `results/metrics/classification_report.txt`. Create a Jupyter Notebook saved as `results/report.ipynb` with the model architecture and training process visualized. Also, save the training loss and accuracy curves to `results/figures/training_curves.png`. Pre-trained embeddings (e.g., Word2Vec or GloVe) are preferred to enhance model performance.",
"tags": [
"Natural Language Processing",
"Supervised Learning"
],
"requirements": [
{
"requirement_id": 0,
"prerequisites": [],
"criteria": "The \"IMDb\" movie reviews dataset is used.",
"category": "Dataset or Environment",
"satisfied": null
},
{
"requirement_id": 1,
"prerequisites": [
0
],
"criteria": "Data cleaning is implemented in `src/data_loader.py`, including the removal of stop words and punctuation.",
"category": "Data preprocessing and postprocessing",
"satisfied": null
},
{
"requirement_id": 2,
"prerequisites": [
0,
1
],
"criteria": "Word embeddings are used to convert text to numerical format and saved under `models/saved_models/`.",
"category": "Data preprocessing and postprocessing",
"satisfied": null
},
{
"requirement_id": 3,
"prerequisites": [],
"criteria": "An \"LSTM\" model is used for sentiment analysis and should be implemented in `src/model.py`.",
"category": "Machine Learning Method",
"satisfied": null
},
{
"requirement_id": 4,
"prerequisites": [
2,
3
],
"criteria": "A classification report is saved as `results/metrics/classification_report.txt`.",
"category": "Performance Metrics",
"satisfied": null
},
{
"requirement_id": 5,
"prerequisites": [
2,
3
],
"criteria": "A Jupyter Notebook containing the model architecture and training process visualization is generated and saved as `results/report.ipynb`.",
"category": "Visualization",
"satisfied": null
},
{
"requirement_id": 6,
"prerequisites": [
2,
3
],
"criteria": "Training loss and accuracy curves are generated and saved as `results/figures/training_curves.png`.",
"category": "Visualization",
"satisfied": null
}
],
"preferences": [
{
"preference_id": 0,
"criteria": "The word embeddings should be pre-trained (e.g., Word2Vec or GloVe) to leverage existing semantic knowledge.",
"satisfied": null
},
{
"preference_id": 1,
"criteria": "The Jupyter Notebook should be well-documented, making it easy for others to understand the model architecture and training process.",
"satisfied": null
}
],
"is_kaggle_api_needed": false,
"is_training_needed": true,
"is_web_navigation_needed": false
} |