Artificial Intelligence (AI) helps software determine the feelings people have in social media posts and blogs. By analyzing the words used, AI software can tell and record what the person’s sentiments are, as well as why they felt that way based on the subject matter or context.

This activity is called sentiment analysis. It is now used by many companies that are eager to find out what people feel about their products. Sentiment analysis helps them plan what they need to do to improve how consumers perceive their brand.

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Sentiment analysis relies on several natural language processing (NLP) algorithms, which we’ve listed down below.

NLP Algorithms Used in Sentiment Analysis

1. Rule-Based Systems

Sentiment analysis relies on rules. Since a system does not consider the sequence of words, it requires additional processing of expressions to determine a person’s sentiment correctly. An example of rule-based sentiment analysis would follow this basic process:

  • Define two polarized words or opposites (e.g., a negative term such as “bad” and a positive one such as “good”).
  • Count the positive and negative words that appear in a given text.
  • If the positive word appears more than the negative term, the system returns a positive sentiment and vice versa. If the numbers are equal, the system will return a neutral feeling.

2. Automatic Systems

These systems rely on machine learning (ML) techniques. They determine sentiments based on the inputs fed to them. In this approach, the system is fed data to classify a view correctly.

3. Hybrid Systems

Hybrid systems use a combination of rules and ML approaches to ensure a high level of accuracy.

What are the Real-World Applications of Sentiment Analysis?

Getting meaningful insights from people are critical to the success of any organization. As such, it has become common for them to use sentiment analysis for:

1. Social Media Monitoring

With the growing use and reach of social media, platforms have become a means for consumers to air their experiences. Sentiment analysis can help organizations gauge how they fare compared to competitors among social media users. Somehow, listening to social media chatter can help marketing departments address challenges that crop up in negative sentiments or emulate best practices indicated in positive views.

2. Brand Monitoring

Brands are considered assets by any organization. That is precisely why almost all companies monitor brand mentions later on classified as either positive or negative. Sentiment analysis can also help a business assess brand reputation changes over time. These uses allow companies to address crises better and maintain their good standing.

3. Customer Feedback Monitoring

Organizations can also rely on sentiment analysis to better understand customer needs. They can then lessen negative sentiments by considering feedback. They can also use the data to see how departments are interacting with clients. And if issues crop up, these can be prioritized as areas for improvement.

Sentiment analysis can immensely help organizations improve their operations by addressing relevant issues that may arise from bringing their products and services online. It helps them monitor social media, brand, and customer feedback better.