Why Data Scientists Must Master Sentiment Analysis

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Why Data Scientists Must Master Sentiment Analysis Standing at a time when consumers can communicate their feelings as well as sentiments about the business more transparently than ever before, it has become fundamental for all businesses to understand the feelings of their clients. By automatically analyzing the feedback that is provided by the customer, organizations can understand their feelings and tailor their products and services in a way that is a lot more satisfactory to the client. Brands can also try to listen mindfully to the clients through many other means such as studying customer reactions, reading the discussions that revolve around them on social media platforms, and so on. Sentiment analysis is basically a method made possible by machine learning where polarity can be recognized right within the text, such as a positive or a negative feeling of the customer. The text can be in the form of a clause, sentence, paragraph, or whole document. So gear yourself up, as in this article we will explain everything that you need to know about sentiment analysis and how is it useful! 

The Role of Data Scientist Specific to Customer Relationship – The field of marketing has ended up being of the areas that are the most affected by this age of digital revolution. A lot of aversions have been caused to the proponents of customer marketing, and how this field has become a lot about codifying whatever knowledge is available. This includes collecting an abundance of digital data, sorting it out, using various algorithms to process whatever is useful, and taking care of decisions that are noteworthy for various different functions. All of this is done in real-time, provides an end-to-end automatic solution, that too at a lightning-quick speed. It is true that there is still a space for innovativeness, however, at this point, it does not work as a unique and competitive differentiator. Sentiment analysis is a way of processing natural language with the help of artificial intelligence. If an organization goes through several different social media platforms or the overall web, or even through the excess of client support


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