Source audit
We map every channel where your customers leave feedback: review platforms, social media, support tickets, NPS/CSAT surveys, web forms. We prioritise the sources with the highest volume and business value for your sector.
Voz del cliente
Sentiment Analysis
Your customers have already told you what they think: on Google, on TripAdvisor, in the support chat, in the survey you sent last month. AI sentiment analysis reads all that text and tells you what works, what fails and where to act — before the problem escalates.
Manually reading hundreds of reviews or open survey responses wastes hours and produces conclusions biased by whoever reads them. Natural language processing (NLP) models based on transformer architectures — BERT and its variants, or state-of-the-art language models — classify each piece of text as positive, negative or neutral, identify the specific aspects driving each emotion (service, price, delivery time, staff attitude) and detect week-by-week trends. The result: a dashboard your team can actually use, not a spreadsheet with 10,000 rows.
For an SME with a presence on Google My Business, TripAdvisor or Amazon, online reputation is as critical as product quality: a drop of half a star can translate into lost visibility and lost customers before the team has identified the cause. Sentiment analysis makes it possible to detect in real time which specific aspect triggered the change — a new supplier, a particular employee, a shift in opening hours — and make decisions backed by data, not gut feeling.
At Summum IA we integrate the full pipeline: data ingestion from your channels (Google, Trustpilot, email, CRM, forms), NLP processing with models calibrated for English, aspect-level and urgency classification, and delivery in the format you already use (dashboard, weekly PDF report, automated alert). We do not sell a generic tool; we configure the system for your sector and your business vocabulary. The implementation is backed by the cross-functional experience of the group: more than 2,000 digitalisation projects since 2007 across five offices in Castilla y León and the Canary Islands.
The Sentiment Analysis process.
The process · four stagesModel configuration
We select and tune the NLP model best suited to your case: from BERT-style transformers for aspect-level analysis of long reviews to lightweight pipelines for real-time social media monitoring. We train the vocabulary with terminology from your sector.
Integration and automation
We connect data sources via API or native connectors, automate ingestion and processing, and feed results into the dashboard or tool your team already uses (Power BI, Google Looker Studio, Notion, weekly email).
Ongoing monitoring and improvement
We periodically review model accuracy, add new sources as your business evolves and deliver sentiment-trend reports with actionable recommendations. The system learns over time and with your feedback.
What is included
What Sentiment Analysis includes.
The operational detail: what we deliver as part of the work and what we keep alive afterwards.
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Aspect-based sentiment analysis (ABSA)
Not just 'positive or negative': we detect which specific aspect (delivery, price, service, cleanliness, product quality) drives each emotion, so you can act at the exact point that matters.
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Online reputation monitoring
Continuous tracking of Google My Business, TripAdvisor, Trustpilot, social media and sector directories. Instant alert when sentiment drops below the defined threshold.
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Survey and NPS processing
Automatic classification of open-ended responses in your satisfaction surveys. We correlate textual comments with numeric scores to understand why NPS rises or falls.
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Ticket and support analysis
We process your CRM or helpdesk history to identify the most frequent contact reasons and those generating the most frustration, optimising your FAQs and team training.
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Dashboard and executive reports
We deliver results in the format that provides most value to your team: interactive dashboard, weekly PDF report or email alert. No spreadsheets with thousands of rows that nobody reads.
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AI Act compliance
Sentiment analysis aimed at reviews and product feedback is classified as minimal or limited risk under the European AI Regulation. We document the system in line with the AI Act transparency requirements in force since August 2026.
Summum cluster
How it connects with its sisters.
Sentiment analysis is the listening layer that powers Summum Marketing's marketing strategy and integrates with the CRM and ERP systems managed by Summum Sistemas.
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Summum ia
The customer service chatbot feeds on sentiment analysis to automatically escalate the most emotionally urgent contacts to the right human agent.
View service → iaSummum ia
Sentiment insights are integrated into the CRM to prioritise commercial follow-ups and personalise communications based on the detected emotional state of the customer.
View service → sistemasSummum sistemas
Summum Sistemas connects the sentiment analysis pipeline with your CRM so that customer opinion data enriches every account and opportunity record.
View service →Frequently asked questions about Sentiment Analysis.
What minimum volume of comments do I need for the analysis to be useful?
From just a few dozen texts per month the system already detects patterns. With low volumes we use pre-trained models without fine-tuning; with hundreds or thousands of monthly comments, fine-tuning delivers additional precision. We analyse your specific situation before proposing the architecture.
Does it work well in English, including industry jargon or regional expressions?
Yes. Multilingual transformer models (mBERT, XLM-RoBERTa) and English-specific models offer very good precision in standard English. For sectors with technical or highly local vocabulary — hospitality, agri-food, manufacturing — we run a calibration phase with real samples of your texts to tune performance.
Does the AI Act affect a customer review analysis system?
In the vast majority of cases, not significantly. Systems that analyse public reviews and product feedback are classified as minimal or limited risk under EU Regulation 2024/1689. The exception are systems that infer emotions from employees in the workplace, which are expressly prohibited under Article 5 of the Regulation unless used for medical or safety purposes. We document the risk level and applicable transparency obligations for your specific case.
How is sentiment analysis different from a simple star rating count?
A numeric score tells you how much; sentiment analysis tells you why and about what. Two four-star reviews can hide opposite messages: one praises the price but criticises the packaging; the other says the opposite. Aspect-level analysis breaks each text down into specific dimensions and tells you exactly where to focus your improvement effort.
How long does implementation take and when will I see results?
The basic integration — source connection, NLP model and first dashboard — is typically completed within four to six weeks. The first actionable insights appear in the first weekly report. The system progressively improves its accuracy as it accumulates data from your business.