Sentiment analysis is a crucial field that employs computational methods to evaluate and interpret peoples’ feelings and emotions as they are conveyed in a selection of text. This process is also known as emotion artificial intelligence and opinion mining.

Technical approaches used by sentiment analysis tools  

Sentiment analysis tools use powerful techniques to train computer software to extract, interpret and categorize text messages into negative, positive and neutral ones. 

There are three sentiment analysis algorithmic approaches that are used to categorize text:

• Rule-based (Lexicon) 
• Machine learning (Automatic) 
• Hybrid approach  

Rule-based (Lexicon) 

With the rule-based approach, the system conducts sentiment analysis based on a set of manually crafted rules. Within the text block, specific keywords are classified as either positive or negative. For instance, the negative lexicon may include words such as 'slow' and 'expensive', while the positive lexicon includes 'fast' and 'affordable'. The results are tallied up to determine a sentiment score based on the volume of each type of keyword.

Machine learning (Automatic) 

The machine learning approach requires that the AI be trained beforehand to determine sentiment based on the words that appear and the order in which they appear in a block of text. Machine learning sentiment analysis algorithms continue to learn from the data they are fed, allowing the tool to evolve with time and perform more effectively. Commonly used classification algorithms include linear regression, support vector machines, deep learning and Naive Bayes. 

Hybrid approach 

The hybrid approach combines machine learning with manually generated rules to optimize speed and accuracy in a single system. This approach requires more time and technical resources than the other two but features improved accuracy. 

Why is sentiment analysis important? 

Understanding the sentiment analysis process is essential in order to achieve the best results. It has the potential to revolutionize the way businesses interact with customers and understand their behavior and needs. Some of the benefits of sentiment analysis include:

• Allows organizations to monitor and understand how people feel about the company’s services or products in real-time. 
• Analyzes interactions and identifies customer friction points at every stage.
• Delivers more objective insights based on customer feedback, allowing greater scalability of business intelligence programs. 
• Supports rapid responses to any potential problems and helps protect the brand’s reputation.

Types of sentiment analysis 

There are several types of sentiment analysis that are used to meet different business needs. Some of the most popular types of AI-enhanced sentiment analysis tools include: 

• Fine-grained (Graded)
• Aspect-based sentiment analysis (ABSA)
• Emotional detection
• Intent-based sentiment analysis (IBSA) 

Fine-grained (Graded) 

Fine-grained sentiment analysis groups text according to the emotion being expressed and grades it on a scale from zero to 100. A lexicon approach is used to evaluate the sentiments expressed in a given text. This type of sentiment analysis is often used when evaluating political statements, product reviews, customer feedback, news articles and media content. 

Aspect-based sentiment analysis (ABSA) 

ABSA focuses on particular aspects of a product, service or customer experience element, and provides a more detailed analysis. It is used in a wide range of industries, including finance, e-commerce, healthcare and human resources.  

Emotional detection 

Emotional detection attempts to detect the psychological state behind a given text, including the writer’s intentions and state of mind. Rather than classifying text as positive, negative or neutral, this type of analysis helps to identify specific emotions, such as frustration, indifference and shock. 

Intent-based sentimental analysis (IBSA)

IBSA identifies the text's tone and classifies its purpose. It is used to determine customer intent and interest level, such as whether the customer is interested in making a purchase, upgrade or downgrade, or whether they are looking to cancel or unsubscribe.  

Sentiment analysis is an evolving artificial intelligence field that enables businesses to quickly analyze customer feedback, product or service reviews and survey responses. A sentiment analysis guide for newbies can be a handy tool for those who want to save time and act faster in real-time scenarios.