Analytics Blog

In some circumstances, sentiment analysis may fail to capture the true feelings of the customer. The technique of discovering and interpreting the underlying emotions portrayed in textual data is known as emotion analysis. Emotion analytics  may gather text data from a variety of sources in order to examine subjective information and comprehend the emotions underlying it. Emotion analysis is the technique of finding and interpreting the emotions conveyed in textual material. Emotion detection and classification are straightforward tasks that can be completed depending on the types of emotions portrayed in the text, such as fear, rage, happiness, sadness, love, inspiration, or neutrality. The core intent is to analyze human language by extracting views, ideas, and thoughts by the assignment of polarities either negative, positive, or neutral. These customers’ reviews contain information that encodes their feelings about their purchases. Reviews and ratings for certain businesses are cri

  Entity extraction is a natural language processing (NLP) technique for extracting mentions of entities (people, places, or objects) from a document. This can be done for a variety of reasons, including understanding the context of the content, providing a summary of the document, or building a knowledge base of entities mentioned in the document. It enables teams to identify relevant information in massive amounts of unstructured text data. However, owing to automated entity extraction, you may have the information you require in a matter of seconds. Sifting through hundreds of surveys, emails, customer support requests, or product reviews would necessitate the deployment of a technology sorter that would automatically sort the data, saving many hours of work. Applications of entity extraction Online customer reviews can be a fantastic source of information for targeted development. With entity extraction, you may examine consumer feedback to determine what they like and dislike. Ent

  The financial services industry is a complex ecosystem, with many different functions, players, and data. The traditional model of financial services is rapidly evolving. Traditional banking, insurance, and investment firms are now being challenged by new entrants like fintech startups, who are disrupting the financial services industry by using innovative technology and data-driven strategies to create new value for customers. The Financial Services industry is evolv i ng at a rapid pace, and as such, there is a need for data-driven strategies to creating a competitive advantage. Traditional financial services firms are now turning to text analysis to gain a competitive edge. Day-to-day operations in finance entail producing and consuming large amounts of unstructured text data from various sources. However, the manual approaches to data processing have over time been reduced in use and importance. Because of this text analysis, the demand has increased significantly in recent years

  Because much of the data generated practically every day is not organized in a particular fashion, analyzing and processing it is incredibly challenging. Keyword extraction  is a text analysis method that aids in the extraction of the most frequently used and relevant words and phrases from any unstructured text. It assists you in summarising the written material and identifying essential subjects presented. It collects keywords from a variety of text sources, including social network interactions, government documents, news stories, business reports, forums and reviews, and more. You can easily find the most essential terms and phrases in massive datasets and disclose valuable information about the issues your consumers are talking about, such as - How many of them are demanding a price reduction? How much of the consumer feedback is connected to quality? What kind of feedback do your consumers give you regarding your customer service? Such insights can assist you in developing a mo