Is Sentiment Analysis Accurate?

The ability to carry out tasks that are typically attributed to a human is known as artificial intelligence. Computers and robots exhibiting near-human intelligence have revolutionized the way we operate. With robots and computers becoming powerful enough to mimic human intelligence, they can be tasked with tedious assignments that the human brain is capable of but for which it often lacks stamina.

Sentiment analysis, an extension of natural language processing, makes computers and robots familiarise with the human way of understanding language. Whereas sentiment analysis looks to find hidden emotions in any textual or spoken form. Thus, artificial intelligence sentiment analysis is the ability to decode human emotions from texts that generally could not be interpreted by computers or robots. Be it negative sentiments towards a maligned brand or positive opinions about a product indicating customer satisfaction.

How Does Sentiment Analysis Work?

Natural language processing, or NLP, is at the forefront of deciphering human sentiments for the machine. It is the process of dissecting language into easily understandable code for devices. Natural language processing is done via a 5-step method. It starts with lexical analysis, where the text is broken down into paragraphs, phrases and words. Then comes the grammatical analysis or Syntax. The third step looks for the meaning in the text, i.e., the semantics of the text.

The fourth step in this analysis involves finding the context behind a particular phrase or text. Lastly, the program analyzes the overall feel of the conversation or text. Natural language processing is the way a machine interprets a set of text. Dissecting the text into understandable components is the first step in figuring out what the text means. You can employ simple machine learning and artificial intelligence to increase the accuracy of results.

How Sentiment Analysis With AI Works

Artificial Intelligence mimics human intelligence and is employed by machines and robots to achieve near-human intelligent responses to texts and scenarios. Natural language processing is a fine example of artificial intelligence used for sentiment analysis. The online content is broken down into various components, after which the computer employs sentiment analysis algorithms to narrow its search to find meaning in the text.

Artificial intelligence sentiment analysis involves the use of various automation tools. Machine learning and data mining are the two pillars on which sentiment analysis is performed. The basics of using artificial intelligence for this purpose is gathering data through the internet and then subjecting this data to sentiment learning algorithms which decipher the negative and positive sentiments from the given content.

What are Sentiment Analysis Tools

Sentiment analysis tools are based on artificial intelligence, data mining and machine learning. Sentiment analysis algorithms are developed through tagging, classifying data into negative and positive sentiments, etc. Data is fed to these artificial intelligence models, which return a sentiment score based on their training in distinguishing negative sentiments and positive sentiments. This training is crucial in the subsequent accuracy of the sentiment analysis algorithms. Sentiment scores help brands determine which features to retain and which ones they can get away with. The ultimate goal of this exercise is to increase the brand’s viability and reach.

How is Sentiment Analysis Useful?

Since there are a lot of challenges involved when using brand-new technology for a specific purpose, sentiment analysis and its derivations are subject to trial and error. However, despite this nascent stage, the latest artificial intelligence sentiment analysis techniques have a 90% accuracy rate, thus making it a pretty reliable content analysis tool. For this very reason, many companies are using sentiment analysis to gain that competitive advantage over their rivals.

Sentiment analysis tools find their way into many environments, including HR and banking sectors. Measuring the performance of your employees is an integral part of making a company profitable. Motivated employees mean higher productivity and, thus, higher profits. Using sentiment analysis to gauge employee satisfaction and their problems at work can improve the work environment. Artificial intelligence sentiment analysis models result in greater work efficiency and less employee fatigue.

What the Voice of Customer Can Unlock for You

Using social media platforms to gather vast amounts of data and then feed it to sentiment analysis tools, companies can gauge the emotions of their customers. Such social media reviews provide actionable insights into the customer’s minds. Since the famous slogan “Customer is Always Right” is valid on most occasions, it is especially true in today’s dominant e-commerce industry. Following this slogan, delving deep into customer reviews and how they affect customer satisfaction can result in a product’s success or failure.

Customer, product, and online reviews are excellent sources of true customer sentiments, whether negative or positive. The voice of the customer can unlock many levels of success for a company or brand. These voices represent a large percentage of the population and thus provide people’s sentiments of a given product. Sentiment analysis is the perfect tool to gauge customer reviews, including negative or positive ones.

Analysis Helps Uncover Customer’s Needs

Artificial intelligence sentiment analysis has the basis to become a powerful tool to address customers’ needs. A customer’s needs are usually not evident in social media posts, but they’re hidden in the subtext of such posts. Emotion detection and intent analysis of social media posts and online content reveal an employee’s or customer’s genuine hidden emotions. Such emotions and insights are the accurate pictures a company or brand needs to excel in their product development phase.

Public relations are vital to any brand’s image and depict its sincerity towards its customers. Refining public relations through sentiment analysis is a way to increase brand viability and integrity. It also leads to better brand management practices. Continuous feedback from the customers will help the brand attain excellent QA/QC lifecycles. Using artificial intelligence and machine learning techniques to gauge customer intent and emotions helps a brand grow its audience and increase its visibility.

With the help of the latest artificial intelligence sentiment analysis techniques, VizRefra aims to offer you the best solutions for your brand. Our sentiment analysis will help your brand grow with top-notch industry tools at our disposal.