What is the most efficient way to process millions of customer reviews into a usable dataset?

All answers to this question.

Traditional "Bag of Words" approaches fail miserably with sarcasm. You need to move toward Transformer-based embeddings. In 2024, we started using "Sentence-BERT" to convert entire reviews into vectors. This captures the "Context" of the sentence rather than just the keywords. For the scale issue, we use Spark-NLP to distribute the processing across a cluster. This allows us to process millions of rows in hours rather than days. The key is to first "Pre-process" the slang using a custom dictionary before feeding it into the deep learning model.

   Answered 2025-11-13 by Deborah Higgins

Are you planning to use a pre-trained model for this, or do you have enough labeled data to fine-tune a model on your specific industry's slang?

   Answered 2025-11-14 by Wayne Roberts

• Fine-tuning is usually the way to go if you want accuracy above 85%. In , a "Generic" sentiment model often misses industry-specific nuances (e.g., "This laptop is sick" is positive in gaming but negative in healthcare). We usually take a pre-trained model and fine-tune it on just 5,000 manually labeled examples from our specific domain. It’s a small investment of time that pays off massively in the "Recall" of our final reports. It makes the "Insights" much more believable for the product team.

     Commented 2025-11-15 by Philip Gardner

Try Zero-Shot classification if you don't have labels. It's surprisingly good at categorizing text into broad buckets without any training at all.

   Answered 2025-11-16 by Bryan Scott

• Zero-shot is a life-saver for quick prototypes. It gets you results today while your team works on the longer labeling process for the permanent model.

     Commented 2025-11-17 by Susan Montgomery