gowthamr1225/google_reviews-analyzing

Purpose

This repository is a business-review analytics prototype that turns Google Maps-style customer reviews into sentiment, themes, trends, reputation signals, and operational recommendations. It is oriented around an interactive dashboard and an exploratory notebook workflow.

Entry Points

• app.py is the likely application/dashboard entry point for serving the analyzer experience.

• BUSINESS REPUTATION & INSIGHTS ANALYZER.ipynb is the primary exploratory or development workflow for data preparation, analysis, and model experimentation.

• README.md defines the intended six-stage review-analysis product behavior.

Review Data Layer

The system works from locally stored review datasets rather than a live ingestion service in this repository:

• reviews.xlsx

• reviews.pkl

• reviews_clean.parquet

The parallel cleaned exports (reviews_clean.parquet, reviews_clean.xlsx) form the likely boundary between raw/extracted reviews and downstream NLP/dashboard consumption.

NLP and Insight Pipeline

The intended analysis flow is: review text → preprocessing → sentiment classification → thematic categorization → trend/reputation aggregation → LLM-generated recommendations. The notebook is the most likely location for implementing or validating these stages:

• BUSINESS REPUTATION & INSIGHTS ANALYZER.ipynb

• reviews_clean.parquet

Outputs are business-facing insights rather than only per-review labels: recurring complaints, strengths, service/pricing/staff themes, temporal trends, and reputation scoring.

LLM Recommendation Boundary

Recommendation generation is designed to be separated from deterministic review processing. The README identifies Langflow, Groq API, Hugging Face models, prompt engineering, and transformer-based NLP as the AI stack. This creates an external-service boundary: cleaned/aggregated review evidence is passed into model workflows, which return summaries and tailored business actions.

• README.md

• app.py

Dashboard and Presentation

The presentation layer is intended to provide interactive decision support, with Streamlit or Gradio named as the frontend runtime. It should consume analyzed review data and generated insights to expose sentiment, themes, trends, reputation scores, and recommendations.

• app.py

• reviews_clean.xlsx

External Data and Infrastructure

SerpAPI is the stated upstream source for Google Maps review extraction, but no dedicated ingestion module is present in the repository tree. The current checked-in datasets therefore act as the operational data source. Groq and Hugging Face represent external model dependencies; Langflow is the stated workflow/orchestration environment.