Sentiment Analysis

About

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

Extreme data diversification

Tone, polarity, emojis, idioms, sarcasm, and negations are all linguistic components of customer communication. Tackling them for proper feedback analysis and improvising the system accordingly is also manageable with AI only.

Exorbitantly raw data

Textual analysis is a significant challenge for getting a grip on the customers’ sentiments. Neurodiversity in modeling is the sole solution to mitigate the risks associated with anomalies of textual data. AI models are trained to visualize the textual data from the affective aspect of customers to arrive at a helpful conclusion.

Customer churn

1. Customer churn The concept of long-term custom engagement for a single product by the same customer is distorted with time. The primary cause of this shift by clients is disengagement from the end of solution providers. Unless the products’ values are updated based on customers’ affective requisites, commitment for the long term will be an outdated phrase for key players. AI models with a deep emphasis on NLP frameworks serve as a reasonable interpretation of customer semantics.

Polysemy

Polysemy is understood to be incorporated along with multimodality-approached-based interactions. Therefore, detecting this element by AI models is also an enabling factor for better sentiment analysis.

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.