Sentiment analysis
Sentiment analysis is the process of people’s opinions and attitudes towards a specific topic or product using various automated tools. Researchers’ business practitioners are brought together to determine the favorable and unfavorable views towards particular products and services using customer feedbacks (Sharda, Delen, & Turban, 2020).
Challenges of sentiment analysis
Accuracy- accuracy measurement is hard and much depends on the levels of the data you are analyzing, data sets found within the areas of business, and information required from these data sets.
Utilization of both machine and human learning is also a significant challenge since machine learning is isolated, and social learning is not separated, making it challenging to apply prior knowledge when using machine learning (Sharda et al., 2020).
Many clients apply sentiment analysis having set a specific hypothesis in their minds. Hence, they will give information that suits their reducing the wealth of information that needs to be covered.
Sentiment analysis is hard to be used in prediction since it’s mostly used for historical purposes based on prior data.
Areas of application for sentimental analysis
Social media is the most common area of application for sentimental analysis since it provides a goldmine of information and opinion regarding customer’s views towards a particular product giving a better insight into the product.
Sentimental analysis is also used in analyzing employee turnover to improve employee engagement and morale since unhappy employees create a terrible customer experience. It helps in boosting both the employee’s and customer’s loyalty (Sharda et al., 2020).
HealthCare and financial service companies also apply sentiment analysis in saving time and costs. They also try to solve all the complaints raised and challenges they encounter in day to day operations.
References
Lazarus, N. (2015). Challenges in sentiment analysis
Sharda, R., Delen, D., Turban, E. (2020). Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support