lakshmansaimuthu/business-reputation-insights-analyzer-using-google-maps-reviews-llms

Purpose and entry point

This repository is a compact business-reputation analysis application for turning Google Maps review exports into sentiment, theme, summary, and recommendation insights. The executable entry point is app.py; the exploratory/prototyping workflow is captured in business reputation.ipynb.

Application boundary

The application appears to operate on repository-local review datasets rather than exposing a separate service API. Its primary input boundary is CSV data, and its primary output boundary is an interactive dashboard rendered by the application:

• Reviews.csv

• ReviewsDetails.csv

• app.py

Review ingestion and normalization

Raw Google Maps reviews are expected to have been collected through SerpAPI or Google Maps APIs and persisted as structured CSV files. The collection/API integration is described in the README but has no dedicated source module in the provided tree. Reviews.csv and ReviewsDetails.csv are therefore the durable handoff from external review acquisition into analysis.

NLP analysis pipeline

The core analysis stages are text cleaning, sentiment classification, theme extraction, review summarization, and recommendation generation. These stages transform unstructured review text into structured business signals: positive/neutral/negative sentiment, themes such as service or pricing, condensed summaries, and suggested improvements. Implementation is concentrated in:

• app.py

• business reputation.ipynb

LLM and prompt boundary

LLMs are an external intelligence dependency for extracting themes, generating summaries, and producing actionable recommendations. Prompt engineering is part of this boundary: review data is prepared locally, sent to an LLM-oriented pipeline, and returned as business-readable findings. No provider-specific client, credentials handling, or model configuration files are present in the supplied tree.

Interactive presentation layer

app.py is the production-facing orchestration layer and likely owns data loading, analysis invocation, and dashboard rendering. The README identifies Streamlit and/or Gradio as the intended UI runtimes, though only one executable application file is present:

• app.py

Development and validation artifact

business reputation.ipynb is the likely experimentation environment for validating data preprocessing, NLP/LLM prompts, and dashboard-oriented metrics before or alongside the app implementation. It shares the same CSV-backed data boundary:

• business reputation.ipynb

• Reviews.csv

• ReviewsDetails.csv