Data Acquisition:
Scraping huge amounts of data efficiently in real-time: Efficiently scraping and processing a large volume of real-time review data was a significant challenge. To overcome this, we distributed the processing tasks across cloud-based resources, allowing us to handle the computational demands and scale efficiently.
Data Storage and Retrieval:
Fast retrieval of data: Retrieving and analyzing large datasets quickly was another hurdle. We implemented a Redis caching solution to store frequently accessed data in memory, significantly improving retrieval speed and overall responsiveness.
Model Development and Training:
Fine-tuning data transformers for multilingual sentiment analysis: Performing sentiment analysis on reviews in multiple languages required us to adapt existing data transformers to our specific use case. We achieved this by fine-tuning the transformer models on multilingual labeled datasets, ensuring they could accurately capture sentiment nuances across different languages.
Discussion