Sentimental analytics in today’s market

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With the world diving deeper into the space of technology with newer advancements such as the natural language processing by the Artificial Intelligence technology has given us a whole new perspective over AI-based models. NLP (natural language processing) is the capability of a computer program to understand the human language well and good as it is spoken, which is a component of AI. This subtype of Artificial Intelligence takes a decision based on the information provided through the meaning extracted from human language, it focuses on the interaction between the human language and the computers.

There are many benefits of NLP and fields that could be used for. Thus one of the best features of NLP is sentiment analysis. Getting to know another side has always been important in every industry and business, thus identifying feelings, opinions, or a statement from very positive ones to neutral ones to very negative ones. The primary goal is to get the information from them whether be it texts, opinions, judgement, or emotional states.

“If you want to understand people especially your customer…then you have to be able to possess a strong capability to analyze text.”- Paul Hofmann

This technology is also called as emotional AI or opinion mining. The ability to find, interpret, process, and simulate human effects is featured in this technology. In sentiment analysis, there are two types; supervised and unsupervised sentiment analysis. Sentiment analysis could be automated, decisions could be based on a sizeable relevant amount of data instead of basing the decisions on pure intuitions and assumptions which may not always be correct and would end up being wrong resulting errors in decisions.

The three main techniques for sentiment analysis are;

The method based on a rule:

In a sentence to measure the sentiment, some words are labelled as affect words thus categorizing them helps in finding out what sentiment that sentence holds. These emotions could be sadness, rudeness, sarcasm, happiness, appreciations, and other phrases like I hate you or I love you.!

Statistical method:

The ML model is used to detect sentiment based on the labelled words using their sentiment-labelled training set. This model would find out the person with the opinion and the products or service i.e. between the holder of the sentiment and the target.

Hybrid method:

Using the above said two methods in a combo, along with the advanced understanding of language to spot semantics that is expressed tenuously or subtly.

Advantages of sentiment analysis:

  • Deriving a more efficient, insightful, and data-based marketing strategy.
  • It helps in understanding customers in a much better way.
  • Measuring marketing campaigns could be done effectively.
  • Looking into brand perception also made easier.
  • Boosting up of customer service using the insights from the analysis.

Thus, using sentiment analysis could bring up the business from scratch and make it good. Cheers to that!!

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