What is sentiment analysis in the chatbot
The module of sentiment analysis in the chatbot identifies emotional signals in user messages to support service decisions. Sentiment reading helps prioritize critical conversations, adjust the language of responses, and reduce friction throughout digital service. In high-volume operations, the feature also standardises screening criteria and improves consistency in the experience delivered.
How the module works
The analysis is performed by models of NLP (Natural Language Processing) combined with techniques of machine learning. The system evaluates text, context, and linguistic patterns to estimate the predominant sentiment in the interaction. Based on this classification, the chatbot can follow service rules, trigger specific flows, and record data for later analysis.
Main capabilities of the module
Detection and classification of feelings and emotions
The module classifies the content of the messages into categories such as positive, neutral, and negative, in addition to allowing clippings based on emotions when configured for this level of granularity. This diagnosis is useful for reducing the time until human intervention in conversations at risk of escalation.
More personalized and context-appropriate answers
Sentiment information can guide the tone and form of the virtual assistant's response, while remaining consistent with brand guidelines. In dissatisfaction scenarios, logic can direct the chatbot to objective messages, with clear resolution steps and forwarding options.
Real-time monitoring and operational alerts
Real-time monitoring allows you to identify peaks of dissatisfaction associated with product, delivery, billing, or service instability. Alerts can be configured by volume of negative conversations, keywords, and topics, making it easier for the CX team to act and operations.
Detailed analyses for continuous service improvement
Consolidated data helps identify recurring reasons for contact, journey bottlenecks, and points with low resolution. The team can use the reports to update the knowledge base, adjust intents, improve automation flows, and calibrate transshipment rules for humans.
Practical use cases
- SAC and support: automatic prioritization of critical services and reduction of response time for complaints.
- Sales and Retention: identification of hesitation and objections to trigger support content or offers that are relevant to the user's time.
- Operation and quality: continuous satisfaction assessment by topic, channel, time and stage of the journey.
- Crisis management: rapid detection of negative variations associated with operational incidents and increased demand.
Metrics to monitor performance
To assess impact, follow indicators related to experience, efficiency, and quality:
- Sentiment distribution (by period, topic, and channel)
- Human transhipment rate And the reason for the transshipment
- Average time to resolution and average service time
- CSAT/NPS by Topic When there is a linked search
- Recontact fee after negative interactions
- Classification accuracy of the model (sampling with human validation)
Learn more about Plusoft AI
O Plusoft AI It is a platform of intelligent virtual assistants aimed at optimizing customer service on digital channels. The use of chatbots makes it possible to meet frequent demands with agility, maintain continuous availability and reduce queues in human service.
The platform can operate integrated with Plusoft Omni CRM, supporting a unified view of the relationship and productivity gains in service operations.
Plusoft AI can also contribute to:
- automation and standardization of interactions at scale;
- 24/7 service;
- accuracy evolution over the weeks based on history and operational adjustments;
- reduction of operating costs by increasing automatic resolution;
- efficiency improvement through configurable flows and integration with systems.
Do you want to map how sentiment analysis can reduce recontact, improve resolution, and prioritize critical care? Talk to our experts and schedule a conversation.
