Implementation of Review Analysis (Review Analysis) — Aspect-based Sentiment Analysis
Analyzing reviews at the "positive/negative" level is insufficient for product solutions. Aspect-based sentiment (ABSA) answers the question: what exactly does the customer like or dislike? "The room is clean, but Wi-Fi is terrible, and breakfast is mediocre" — three aspects, three ratings.
ABSA Tasks
ATE (Aspect Term Extraction): extracting aspects from text ("room", "Wi-Fi", "breakfast")
ASC (Aspect Sentiment Classification): sentiment for each aspect (positive/negative/neutral)
ATSC (Aspect Term Sentiment Classification): joint task — extraction and classification simultaneously
Opinion Target Expression: extracting opinions with their targets ("Wi-Fi" → "terrible")
Implementation with LLM
For non-standard domains, LLM-based approach with structured output is faster than fine-tuning:
class AspectSentiment(BaseModel):
aspect: str
sentiment: Literal["positive", "negative", "neutral", "mixed"]
opinion_phrase: str
score: float # -1.0 to 1.0
class ReviewAnalysis(BaseModel):
aspects: list[AspectSentiment]
overall_sentiment: Literal["positive", "negative", "neutral", "mixed"]
key_issues: list[str]
key_positives: list[str]
Aggregation Across Review Corpus
Aspect analysis of a single review has little value. Value lies in aggregation across thousands of reviews:
- Heat map of aspects over time: dynamics of "cleanliness" decline after housekeeping change
- Comparison with competitors by aspects: your "service" better than market average, "location" worse
- Automatic alert: sharp decline in "delivery speed" aspect → logistics problem
Visualization and Dashboard
Stack for review analytics: Pandas + Plotly for visualization, Superset or Metabase for business dashboards. Key widgets: aspect radar, sentiment trends over time, top-N negative themes per period.