Mastering the Art of Unsupervised Learning: Techniques and Guidance
Unsupervised learning, a cornerstone of artificial intelligence, has emerged as a powerful technique that enables machines to uncover patterns and insights from unlabelled data.
Sentiment analysis applies Natural Language Processing to reveal the emotion of a statement – positive, negative, or neutral. It is instrumental in assessing public opinion, monitoring brand reputation and analysing customer experiences.
Sentiment Analysis tools help companies generate insights on how they can enhance customer experience and improve customer service. These tools are essential in understanding feelings towards a product, service, or brand.
Why Sentiment Analysis is Important
Sentiment analysis can help retain customers and improve loyalty with better service outcomes and customer experience.
When a customer raises a support ticket and sends an email, NLP can detect the emotional state of the message. If the message carries agitated and serious notes, business rules related to such an emotional state are set so the customer service agent is able to provide an appropriate response. For high-priority and urgent messages, NLP can trigger escalation and flag for immediate priority or handling by senior staff. With staff awareness of the client’s emotional state, he can be more empathetic leading to a more satisfying resolution of the issue and improvement of the customer relationship.
One sentiment analysis technique used by organisations is machine-learning-based sentiment analysis. This software examines speech and text for positive or negative sentiment. However, it’s not only customer interaction that can be a source of information but such software can also mine social media posts, online reviews and forums, satisfaction surveys and more.
When an organisation determines and understands its positive and negative feedback, it can improve its customer interaction. Exploring historical data from customer experiences and interactions, can predict future customer behavior and actions and can then create steps to make sure that the customers have only positive experiences.
There are different sentiment analysis algorithms available to analyse customer feedback and conduct sentiment analysis text classification.
One is Rule-Based Sentiment Analysis Algorithm. Rule-based algorithms require user to define rules which the system uses to classify unstructured text data based on pre-defined tools. Rule-based requires NLP techniques such as tokenization, Parsing, and Lexicon.
Automated Sentiment Analysis Model is another option which uses algorithms us as linear regression, Naïve Bayes and Deep Learning.
The third one is Hybrid Analysis Algorithms which involves combining the desirable elements of both rule-based and automated machine learning-based algorithms to classify unstructured text to positive, negative and neutral categories.