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 people’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 how people feel about their products. Sentiment analysis helps them plan what they need to do to improve how consumers perceive their brand.
Read More about “Sentiment Analysis”
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.
How Does Sentiment Analysis Work?
As discussed above, sentiment analysis uses NLP algorithms to develop software that can understand the different ways context can influence sentiment. So, how does sentiment analysis work?
First, developers create a text classifier algorithm that can detect if a post contains specific sentiment indicators. They then train these classifiers by introducing a range of positive, negative, and neutral words.
The process usually begins by crawling text and splitting it into basic components, such as sentences, phrases, tokens, and entities. Once that is done, topic and relationship (i.e., of words with one another) identification follows. The program then assigns a sentiment score to a post. That score can range between -1 (negative) and +4 (positive) comments. Neutral sentiments are often given a score of 0.
Since the human language is complex, it is also often necessary to train programs to detect and analyze grammatical nuances, slang words, cultural variations, and misspellings, making the process extra challenging.
Here’s a simple video that shows what sentiment analysis is and how it works:
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.