Sentiment Analysis (SA) is a field of computing for studying opinions, feelings, and emotions expressed in texts. Besides computing, other areas are included, such as statistics, natural language processing (NLP), and linguistics. The junction of these areas can be used to detect different emotional states and reveal various behavioral and attitudinal patterns. Other techniques are used to detect conditions and patterns from text, such as process mining or discourse analysis. The polarity classification is a technique for classifier text written in natural language, considering their semantic polarity and distinguishing positive and negative forms. In general, linguistics, machine learning (ml), and hybrid (linguistics with ml) are approaches used in NLP for SA.

The SA is applied in different segments:

• Social media monitoring;
• Customer support ticket analysis;
• Brand monitoring;
• Reputation management;
• Listen to the voice of the employee;
• Product Analysis;
• Market research and competitive research;
• and other applications.

How about we apply SA in the educational segment?

In an educational segment, the use of SA allows the processing of student feedback, aiming at monitoring the teaching effectiveness of instructors and enhancing the learning experience.