sentiment-analyzer

Aspect-based sentiment analysis (ABSA) identifies sentiment toward explicit and implicit aspects within text. It analyzes multi-entity inputs like reviews to generate separate, actionable insights for contrasting aspects such as product quality versus price, outputting per-aspect polarity.

Sarcasm detection in sentiment analysis surfaces non-literal sentiments by identifying valence shifters and sarcasm signals. It adjusts the overall polarity accordingly, ensuring that ironic statements in social media or reviews do not skew the document-level summary.

Yes, domain calibration applies domain-specific valence adjustments for industries like product, medical, and finance. This ensures more accurate sentiment interpretation by calibrating confidence scores based on the specific context of the technical documents or reviews.

To extract per-aspect sentiment, apply the analyzer to arbitrary text to identify sentiment expressions and calculate a 7-point polarity scale. It then aggregates these aspect details to produce a document-level summary with calibrated confidence scores.

Yes, the tool outputs a structured report featuring per-aspect polarity, identified sentiment expressions, and a document-level summary. All findings include calibrated confidence scoring to indicate the reliability of the detected sentiment and valence shifters.