Sentiment Analysis


Identification, extraction, and quantification of customers’ subjective insights, i.e., sentiments, let stakeholders monitor their product journey endlessly. Only via artificial intelligence: automated interpretation of customers’ reviews, opinions, feedback, and subjectivity is obtainable as a knockdown factor for authenticating sentiment analysis. AI rule-based models classify sentiments into three categories mainly: positive, negative, and neutral. An exaggerated grading system can be further deployed an inflated grading system to provide more detailed analytics on customers' subjective opinions and influential positions. AI models for sentiment analysis gather data insights to make relevant autonomous emotion recognition and affective computation. To intuitively infer from one’s emotions, natural language processing, text recognition, speech recognition, computer vision, and other visionary models are amalgamated.

Challenges Faced by the Customers

In a nutshell:

Computational extraction of sentiments, views, reviews and textual information leads to an astronomical transition compared with traditional human language interpretation that is not concerned with emotions. This is an emerging approach to improve the quality control mechanism of intensively customer-focused entities.