Sentiment Analysis in Reviews: How Hotels Increase Occupancy

Discover how automated online feedback analysis with NLP allows hotels to identify issues, adjust services, and improve pricing and marketing decisions in real time.

Introduction

Portuguese hotels face a constant challenge: hundreds of reviews appear weekly on platforms like Booking, TripAdvisor and Google. Manually reading each comment consumes hours and makes it difficult to spot recurring patterns. Sentiment analysis with natural language processing (NLP) transforms this noise into actionable information — enabling quick operational adjustments that directly impact occupancy and revenue.

This article explains how automated review analysis works, what tools you can use, and which metrics to monitor for concrete results.

What is sentiment analysis applied to hospitality

Sentiment analysis is an NLP technique that classifies text as positive, negative or neutral. In a hotel context, it goes beyond binary classification:

Aspect extraction: identifies what guests are talking about (cleanliness, breakfast, staff, location, Wi-Fi).
Emotional intensity: distinguishes between "the room was acceptable" and "the room was impeccable".
Trend detection: maps whether problems (e.g. slow check-in) occur repeatedly or are isolated incidents.
Competitive comparison: analyses competitors' reviews to identify differentiating advantages.

Imagine you manage an 80-room hotel in Lisbon. Instead of reading 150 reviews per month, an NLP system automatically extracts:

"23% mention nighttime noise (negative sentiment, aspect: soundproofing)."
"67% praise reception staff friendliness (positive sentiment, aspect: staff)."
"12% complain about repetitive breakfast (negative sentiment, aspect: F&B)."

These insights enable quick decisions: invest in double-glazed windows, maintain staff training, refresh the breakfast menu.

Typical implementation workflow

1. Data collection

The main platforms (Booking, TripAdvisor, Google) provide APIs or export tools. You can:

Use scrapers (respecting terms of service) to aggregate reviews.
Integrate with reputation management platforms like ReviewPro, TrustYou or Revinate, which consolidate sources.
Set up alerts for new reviews (most platforms send email notifications — automate capture with Zapier or n8n).

Recommended frequency: daily collection for hotels with >50 rooms, weekly for smaller properties.

2. Text pre-processing

Before analysing sentiment, the text needs cleaning:

Noise removal: eliminates excessive emojis, URLs, special characters.
Normalisation: converts spelling variations to a standard form, handles abbreviations.
Tokenisation: splits sentences into words or phrases.
Lemmatisation: reduces "rooms" and "room" to the same root.

Useful libraries: spaCy (supports European Portuguese), NLTK, or cloud services like AWS Comprehend.

3. Sentiment analysis

Two main approaches:

Pre-trained models:

Multilingual BERT (e.g. neuralmind/bert-base-portuguese-cased): trained on Portuguese texts, captures context.
XLM-RoBERTa: robust variant for multiple languages, including PT-PT.
Commercial APIs: Google Cloud Natural Language, Azure Text Analytics, AWS Comprehend (support Portuguese, but validate quality with real reviews before trusting).

Fine-tuning: If you have 500+ manually classified reviews, you can fine-tune a model for hotel-specific vocabulary (e.g. "hard bed" is negative, but "hard bread" at breakfast also is).

To get started quickly: use Hugging Face Transformers with the nlptown/bert-base-multilingual-uncased-sentiment model (classifies reviews as 1-5 stars).

4. Aspect extraction

Identifies what each comment is about. Methods:

Keyword matching: searches for key terms ("breakfast", "cleanliness", "reception").
Dependency-based models: analyses grammatical structure to link adjectives to nouns ("spacious room" → aspect: room, sentiment: positive).
Semantic clustering: groups similar phrases ("attentive staff", "friendly employees", "helpful team").

Tools: spaCy for dependency parsing, libraries like Gensim for topic modelling (LDA).

5. Visualisation and alerts

Essential dashboard should show:

Aggregated sentiment score (e.g. 7.8/10 this week vs. 7.5 last week).
Breakdown by aspect: bar chart with % positive/negative for each category.
Trending issues: list of emerging problems (e.g. "air conditioning" jumped from 2% to 15% of negative mentions).
Temporal comparison: monthly evolution.
Competitive benchmark: how your score compares with 3-5 direct competitors.

Automated alerts:

Email/SMS when sentiment falls below threshold (e.g. <6.5/10).
Notification when specific aspect receives 3+ negative mentions in a week.

Visualisation tools: Tableau, Power BI, Looker Studio (free), or custom dashboards with Streamlit/Plotly.

Concrete actions based on insights

Operations

Cleaning: if 10%+ reviews mention dust/stains, schedule an audit with housekeeping.
Maintenance: patterns like "weak shower" or "stiff lock" indicate need for repairs.
Training: drop in staff sentiment may signal burnout or need for training.

Revenue management

Hotels with sentiment score >8.5/10 can justify prices 10-15% above competitors.
If breakfast sentiment is very positive (>85% praise), consider raising the add-on price or including it in base package.
Identify periods with low sentiment (e.g. August with heat complaints) and adjust expectations or invest in air conditioning.

Marketing

Positive reviews about specific aspects ("stunning view", "peaceful pool") should appear in ad copy and website.
If competitors have negative sentiment about parking (and you don't), highlight this in campaigns.
Respond publicly to negative reviews based on analysis: mention concrete corrective actions.

Product

If 20%+ reviews request "vegetarian options" at breakfast, add them to the menu.
Recurring complaints about lack of USB sockets? Invest in adapters/renovated rooms.
Frequent praise for the terrace? Promote it more actively, consider private events.

Metrics to monitor

Net Sentiment Score (NSS): (% positive - % negative). Target: >70%.
Aspect Coverage: % of reviews where you successfully identify specific aspects (validates model quality). Target: >80%.
Response Time: average time between negative review and hotel response. Target: <24h.
Sentiment Impact on Booking: correlate sentiment variation with occupancy rate (2-4 week lag is normal).
Issue Resolution Rate: of problems identified via sentiment, how many were fixed within 30 days?

Recommended tools

All-in-one platforms (paid)

ReviewPro: integrates 100+ sources, sentiment analysis in 40+ languages, competitive benchmarking. Hotel-focused.
TrustYou: deep semantic analysis, intuitive dashboard, configurable alerts.
Revinate: combines reviews with CRM to segment guests by sentiment.

Open-source / custom

spaCy + Transformers: complete NLP pipeline. Requires Python skills.
Hugging Face Models: free pre-trained sentiment models (e.g. cardiffnlp/twitter-xlm-roberta-base-sentiment).
Apache Airflow: orchestrates collection → analysis → storage pipeline.
PostgreSQL + pgvector: stores review embeddings for semantic search.

Cloud APIs

Google Cloud Natural Language: supports PT-PT, extracts entities and sentiment. Pricing per 1k requests.
Azure Text Analytics: sentiment analysis and key phrase detection.
OpenAI GPT-4: via API, send reviews and request structured classification (pricier, but very flexible).

Common pitfalls

Blindly trusting aggregate scores: an 8/10 overall can hide a critical problem in one aspect (e.g. safety).
Ignoring cultural context: guests from different markets have different expectations (Nordics value quiet, Brazilians may prioritise friendliness).
Analysis without action: generating monthly reports nobody reads. Link insights to team KPIs (housekeeping, F&B, reception).
Not validating model quality: manually test 50-100 reviews classified by the system vs. human classification. Accuracy <75% indicates unsuitable model.
Forgetting internal reviews: employees on Glassdoor or Indeed reveal operational issues affecting guest experience.

Practical implementation in 4 weeks

Week 1: Define scope (how many properties, which platforms) and collect historical reviews (6-12 months).

Week 2: Set up automated collection pipeline and test sentiment model with 100 reviews (validate manually).

Week 3: Implement aspect extraction and create initial dashboard with 3-5 essential metrics.

Week 4: Share first report with operations teams, gather feedback, adjust aspect categories.

Estimated cost (50-100 room hotel, custom solution): 40-60h development time + €100-200/month in APIs/hosting.

Conclusion

Sentiment analysis transforms reviews from qualitative noise into actionable data. For hotels, the impact goes beyond improving online scores: it enables faster decisions about operations, pricing and marketing.

Start simple: aggregate reviews from 2-3 main platforms, use a pre-trained model to classify sentiment, create a dashboard with the 5 most mentioned aspects. Monitor for 4 weeks and adjust strategies based on real patterns. The key isn't technical sophistication, but the discipline of linking insights to concrete actions — and measuring the result.

If you manage a hotel property and want to learn how to implement NLP solutions without relying on external vendors, NeuroLearn offers practical AI courses applied to business, with real examples from the tourism sector.