Restaurant Reviews- Sentiment Analysis- Turning Feedback into Actionable Insights

In the food industry, customer feedback is everywhere—Google reviews, Yelp, social media, and survey responses. For restaurants, the challenge isn’t collecting reviews, but understanding them at scale. Manually reading through thousands of comments is not feasible, and this often leads to missed opportunities for improving service or capitalizing on what customers love most.

To tackle this challenge, I built a sentiment analysis pipeline that processes restaurant reviews and translates them into clear, actionable insights.

Here’s how I approached it:

1. Data SourceI used a Kaggle dataset of restaurant reviews containing both text feedback and associated ratings.

2. Data CleaningRaw text data often comes with noise—punctuation, emojis, inconsistent casing, and stopwords. I applied text preprocessing techniques (tokenization, lowercasing, removing special characters) to prepare the reviews for modeling.

3. Sentiment Classification with NLPFor the core sentiment analysis, I used the DistilBERT model (distilbert-base-uncased-finetuned-sst-2-english). This transformer-based model is lightweight yet powerful, trained specifically for sentiment classification tasks. Each review was labeled as positive or negative, giving restaurants a high-level view of customer satisfaction.

4. Visualization with PlotlyTo make insights business-friendly, I built interactive dashboards with Plotly. These charts allow stakeholders to quickly see patterns such as:

   • Percentage of positive vs. negative reviews

   • Most frequent keywords in negative reviews (e.g., “cold food,” “slow service”)

   • Sentiment trends over time

Business Impact: With this system, restaurants can move beyond anecdotal feedback and gain a data-driven understanding of customer experiences. Instead of relying solely on ratings, managers can see why customers leave good or bad reviews. This enables smarter decision-making—whether it’s improving delivery speed, training staff, or doubling down on popular menu items.

Technologies Used:

• Python (data preprocessing, NLP pipeline)

• Transformers (Hugging Face) – DistilBERT sentiment classification

• Pandas & NumPy – Data wrangling

• Plotly – Interactive visualizations

This project shows how AI + data visualization can turn unstructured customer feedback into a clear roadmap for operational improvement.