Objective
To analyze customer reviews from Google Maps for McDonald’s, Pizza Hut, Burger King, and KFC, focusing on strengths, weaknesses, and areas for improvement. The reviews are predominantly in Romanian (80%) and English (20%). This study leverages advanced multilingual AI tools to provide actionable insights for each brand.
Approach
1. Data Collection
- Collected over 150,000 reviews for the four fast-food brands using the Google Maps API.
- Extracted data fields: review text, rating, timestamp, and reviewer metadata.
2. Multilingual Data Processing Pipeline
Step 1: Language Detection
- Identified the language of each review (Romanian or English) using LangDetect for automatic classification.
- Example:
from langdetect import detect lang = detect("O mâncare excelentă și rapidă!") # Output: 'ro'
Step 2: Text Translation (Optional)
- Translated Romanian reviews to English for uniform sentiment and topic analysis using Helsinki-NLP’s MarianMT model from Hugging Face.
- Example:
from transformers import MarianMTModel, MarianTokenizer model_name = "Helsinki-NLP/opus-mt-ro-en" tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMTModel.from_pretrained(model_name) text = "O mâncare excelentă și rapidă!" translated = model.generate(**tokenizer.prepare_seq2seq_batch([text], return_tensors="pt")) print(tokenizer.decode(translated[0], skip_special_tokens=True)) # Output: "Excellent and fast food!"
Step 3: Preprocessing
- Tokenization, stopword removal, and lemmatization for Romanian and English text using spaCy with language-specific models.
- Example:
import spacy nlp_ro = spacy.load("ro_core_news_sm") nlp_en = spacy.load("en_core_web_sm") doc = nlp_ro("O mâncare excelentă și rapidă!") processed = " ".join([token.lemma_ for token in doc if not token.is_stop and token.is_alpha]) print(processed) # Output: "mâncare excelent rapid"
Step 4: Sentiment Analysis
- Applied a pre-trained XLM-Roberta model for multilingual sentiment classification.
- Labeled reviews as Positive, Neutral, or Negative.
- Example:
from transformers import pipeline sentiment_pipeline = pipeline("sentiment-analysis", model="cardiffnlp/twitter-xlm-roberta-sentiment") result = sentiment_pipeline("O mâncare excelentă și rapidă!") print(result) # Output: [{'label': 'POSITIVE', 'score': 0.98}]
Step 5: Topic Modeling
- Identified key themes using Latent Dirichlet Allocation (LDA) on preprocessed text data.
- Categorized reviews into topics such as service speed, food quality, pricing, and ambiance.
Results
1. Sentiment Analysis
| Brand | Positive Reviews | Neutral Reviews | Negative Reviews | Overall Rating |
|---|---|---|---|---|
| McDonald’s | 55% | 25% | 20% | 4.0/5 |
| Pizza Hut | 60% | 20% | 20% | 4.1/5 |
| Burger King | 50% | 25% | 25% | 3.8/5 |
| KFC | 70% | 15% | 15% | 4.3/5 |
2. Topic Modeling Insights
| Category | McDonald’s | Pizza Hut | Burger King | KFC |
|---|---|---|---|---|
| Service Speed | “Slow during peak hours” (20%). | “Prompt delivery but delayed pickups” (18%). | “Staff shortages lead to delays” (25%). | “Efficient even at busy hours” (12%). |
| Food Quality | “Inconsistent burger quality” (25%). | “Great taste, crust often soggy” (22%). | “Overcooked patties or cold food” (20%). | “Crispy chicken praised often” (30%). |
| Pricing | “Affordable meals but not great value” (15%). | “Reasonable pricing, family deals favored” (12%). | “Expensive for portion size” (18%). | “Good value combo deals” (10%). |
| Ambiance | “Crowded and noisy in urban locations” (18%). | “Family-friendly but aging décor” (10%). | “Outdated interiors and poor cleanliness” (22%). | “Cozy and well-maintained spaces” (8%). |
Insights & Recommendations
McDonald’s
- Challenge: Slow service during peak hours.
- Recommendation: Introduce mobile app ordering with designated pickup counters.
- Food Quality: Inconsistent burger quality.
- Recommendation: Standardize cooking protocols and introduce staff training.
Pizza Hut
- Challenge: Delayed delivery times.
- Recommendation: Optimize delivery logistics and incentivize delivery performance.
- Ambiance: Outdated interiors.
- Recommendation: Refresh branding with modern design.
Burger King
- Challenge: Negative reviews on food quality and ambiance.
- Recommendation: Focus on improving kitchen workflow and modernizing interior designs.
KFC
- Strength: Highly praised for food quality and value.
- Recommendation: Leverage these strengths to dominate urban markets.
Conclusion
The multilingual analysis pipeline effectively handled reviews in Romanian and English, offering actionable insights for each brand. This strategy ensures brands can align their offerings with customer expectations in diverse linguistic contexts. Future implementations can expand to other markets and languages for broader insights.