This project aims to extract and compile information about products on Amazon from their reviews. Aspect Extraction and Sentiment Analysis are employed in order to compute statistics and summaries of products given their URLs.
- Python 3.6+
- Google Chrome
- Tkinter (
sudo apt-get install python3-tk) - pipenv (
pip3 install pipenv)
Run pipenv install and pipenv run spacy in the root directory to install all necessary packages. The program can now be run using pipenv run main.
There is an issues with "Are You A Robot" pages from Amazon preventing the crawling of reviews. In this case, a ValueError exception will be thrown while the GUI does not close automatically.
To override this, launch the program using pipenv run debug.
In debug mode, the pages will be opened in the foreground and you get the opportunity to enter the image code of the "Are You A Robot" challenge. We generally recommend copying the product URLs from the Google Chrome browser so that the corresponding cookies ensure that no "Are You A Robot" challenge will be triggered.
Results of best sentiment model on custom Laptop dataset:
| Split \ Metric | Accuracy | Class balanced accuracy | F1 Score | Class Splits |
|---|---|---|---|---|
| LSTM Training | 81.58% | 85.25% | 0.8407 | - |
| LSTM Validation | 68.12% | 62.38% | 0.7713 | 28.12%/8.75%/63.13% |
| LSTM Test | 90.37% | 64.56% | 0.8787 | - |
| Random Forest Training | 62.22% | 72.24% | 0.5755 | - |
| Random Forest Validation | 57.63% | 54.22% | 0.4716 | 55.56%/9.03%/35.42% |
| Random Forest Test | 55.00% | 60.27% | 0.4912 | - |
| SVM Training | 72.50% | 65.63% | 0.6480 | - |
| SVM Validation | 62.50% | 36.59% | 0.3619 | 14.89%/4.17%/81.94% |
| SVM Test | 62.77% | 41.17% | 0.4066 | - |
| Validation Set | - | - | - | 37.97%/3.21%/58.82% |
- https://alt.qcri.org/semeval2015/task12/index.php?id=data-and-tools
- https://europe.naverlabs.com/research/natural-language-processing/aspect-based-sentiment-analysis-dataset/
- Sentiment LSTM: https://towardsdatascience.com/sentiment-analysis-using-lstm-step-by-step-50d074f09948
- Dynamic input sizes for LSTMs: https://towardsdatascience.com/taming-lstms-variable-sized-mini-batches-and-why-pytorch-is-good-for-your-health-61d35642972e
- see requirements.txt
- Google Chrome, available https://www.google.com/chrome/
- Christopher Brückner ([email protected])
- Julius Ernesti ([email protected])
- Raphael Kirchholtes ([email protected])
- Armand Rousselot ([email protected])