Why Basic Sentiment Analysis Falls Short
Most sentiment tools give you a single score: positive, negative, or neutral. That's almost useless for product decisions. "The laptop screen is gorgeous but the battery dies in 3 hours" is a neutral-scored review that contains critical product intelligence about two different aspects.
Aspect-based sentiment analysis solves this. It identifies the entities mentioned (screen, battery) and the sentiment toward each one separately. Now you know customers love your display but hate your battery life. That's actionable.
Model Architecture
The system uses a fine-tuned BERT model (distilbert-base-uncased for speed, bert-base for accuracy) with a classification head for sentiment labels and an extraction head for aspect terms. The model is pre-trained on a large review corpus and can be fine-tuned on your domain-specific data with as few as 500 labeled examples.
For languages beyond English, the system supports multilingual BERT (mBERT) which covers 104 languages. Accuracy is highest for English, German, French, and Spanish, but works reasonably well for most Latin-script and CJK languages.
Processing Pipeline
Text goes through preprocessing (cleaning, tokenization), model inference (sentiment + aspect extraction), and post-processing (aggregation, scoring). The pipeline handles HTML tags, emojis, slang, and abbreviations. Batch mode processes CSV/JSON files with thousands of records. The API mode accepts individual text inputs and returns results in under 200ms.
Dashboard and Reporting
The React dashboard shows sentiment distribution over time, aspect-level breakdowns, word clouds for positive and negative mentions, and trend lines. Filter by date range, source, product, or sentiment label. Export raw results as CSV or formatted reports as PDF. The dashboard updates in real time when processing live social media feeds.
