What is sentiment analysis: principles, benefits and tools

Sentiment analysis is a process of identifying and specifying the emotional tone of different kinds of texts. Traditionally sentiment analysis is used to consider customer feedback, employee engagement, survey responses, and product reviews. Monitoring of social media, brand reputation management, and customer experience are the main, but not the only areas for successful usage and implementation of sentiment analysis.

What is Sentiment Analysis?

Sentiment analysis is used to determine whether a given text contains a negative, positive, or neutral mood. It is a form of text analysis using natural language processing (NLP) and machine learning (ML). It’s also known as opinion mining or emotion artificial intelligence (AI).

Sentiment Score

An important aspect of accurate sentiment analysis is polarity classification. Polarity refers to the overall mood conveyed by a particular text, phrase, or word. The range of polarity can be expressed as a numerical score called a "sentiment score". For example, this can be a number between -100 and 100, where 0 represents a neutral sentiment. This score can be calculated for the entire text or just for a single phrase.

Fine-Grained Sentiment Analysis

For every specific use case sentiment scoring can be defined in a different way. For example, to range the mood and attitude of customers’ reviews you can use five categories and consider 1-star ratings as "Very Negative" and 5-star ratings as "Very Positive".  You can also further refine positive and negative sentiments into specific emotions. For example, positive words’ emotions can be further refined into happy, excited, impressed, trusting, etc.

Aspect-Based Sentiment Analysis

Sentiment analysis is most useful when associated with specific attributes or characteristics described in a text. The process of discovering these attributes and their sentiments is called Aspect-Based Sentiment Analysis, or ABSA. Aspect-based customer sentiment analysis is especially useful for real-time monitoring. Businesses can instantly spot issues reported by a customer on social media or in comments. This helps reduce response times and improve customer experience. Let us have a look at a sentiment analysis example.

Sentiment Analysis Example for Real-Time Monitoring

When looking at cell phones, you might be interested in the best camera quality. In terms of mobile cam quality, aspect-based algorithms can be used to determine whether a sentence has a positive, negative, or neutral connotation.

This makes it possible to measure sentiment by camera quality, even if people use slightly different words in online reviews. For example, "reliable photo processing" or "great-looking telephoto images", can both lead to positive sentiment about the "mobile camera quality."

The results of the analysis can then be examined in data visualization to identify areas for improvement. These visualizations can include overall sentiment, sentiment over time, and sentiment by rating specific datasets. 

Why Sentiment Analysis?

So, why is sentiment analysis important? Increasing sales and retaining customers are core business goals. Sentiment analysis helps you understand how people feel about your brand or product. This is usually impossible to do manually because there is too much data. Professional SaaS tools make it easier for companies to gain insight into their textual data. This can include anything from customer reviews to employee surveys, social media posts, or news articles. Sentiment data from these sources can be used to make important business decisions.

What is Sentiment Analysis Used For?

Businesses use different sentiment analysis features across social media platforms, employee resources, online review sites, online communities, and internal customer communication channels. Let’s have a more thorough look at these cases and the main benefits of sentiment analysis are.

1. Sentiment Analysis in Social Media

Social media is an effective way to reach new customers and connect with existing ones. Good customer feedback and positive comments or posts encourage other customers to buy more from your business. But the opposite is also true. Negative posts on social media platforms can be very expensive for your business. While traditional social media monitoring was focused on the number of likes or comments, sentiment analysis has given it a completely new dimension showing companies how a customer feels about their products and services.

An investigation by Convergys Corp. has indicated that one negative review on YouTube, Twitter, Instagram, or Facebook may cost the company about 30 customers. Negative comments or posts about a company can also cause huge financial damage.

What is the fundamental purpose of sentiment analysis on social media? Sentiment analysis can help identify these types of issues in real time before they escalate. Businesses can then act quickly to reduce brand reputation and limit financial costs.

2. Sentiment Analysis for Crisis Prevention

Sentiment analysis tools can collect real-time mentions of predefined keywords from websites, news sites, and forums. PR specialists can receive real-time notifications of all negative content appearing online. When it comes to negative customer sentiment, companies can react quickly to nip the problem in the bud before it escalates into a social media crisis.

3. Sentiment Analysis for Market Survey

Sentiment analysis is useful for market research and obtaining market intelligence. It can help companies spot emerging trends, analyze competitors, and explore new markets. By performing sentiment analysis, it is possible to get deeper insights and determine what customers like and dislike about competitors' products. These insights can show how you can gain a competitive advantage.

Sentiment analysis can also be applied to market reports and business journals to reveal new opportunities. For example, analyzing industry data on the real estate market may show that a particular area is receiving more and more positive mentions. This information may indicate that industry insiders see the field as a good investment opportunity.

These insights can then be used to gain an early advantage by investing ahead of other markets.

4. Sentiment Analysis for Customer Service Experience

A great customer service experience can make or break a business. A customer wants to know that their requests will be handled quickly, efficiently, and professionally. Sentiment analysis can help companies evaluate customer satisfaction and simplify and improve their customers' impressions.

Machine learning algorithms can automatically rank conversations by urgency and topic. A sentiment analysis system can find posts where people are particularly upset. These requests can be prioritized by internal experts. Other community members can answer general questions.

Applying sentiment analysis can reduce turnaround time and increase efficiency by routing queries to the right people. Ultimately, a customer has a better support experience and you can reduce churn.

5. Sentiment Analysis for Product Experience

Sentiment analysis can determine how customers feel about product features and benefits. This helps uncover potentially unknown areas of improvement.

For example, you can search for online product reviews to get feedback on that particular product category from all competitors in that market. You can use sentiment analysis to discover topics your customers don't like. This may indicate an opportunity or a general problem.

Product managers can use sentiment analysis to monitor whether customers see improvements in check deposit functionality and reliability.

6. Sentiment Analysis for Employee Engagement

Employee engagement is a challenge no matter the size of your organization, but it's especially difficult and important when you're a large company. With employee turnover on the rise, annual performance reviews and surveys don't give companies enough information to truly understand how employees feel about them.

This is where employee sentiment analysis comes into play. Employee sentiment analysis provides many important features, including:

• When the organization finds that employees are dissatisfied with the way the organization addresses key issues such as employee performance, productivity, and customer service, it helps the organization take the necessary steps to make changes.

• Promote transparency and more open communication. When companies take corrective action to reassure employees that their opinions matter, it leads to transparency, better communication, and higher employee engagement.

• Provide accurate pictures of employee opinions.

People learn from former and current employees which companies they dislike the most. Employee sentiment analysis can help organizations assess whether these opinions are based on inaccurate perceptions. When wrong opinions damage a company's image, HR can take action to encourage a more positive impression.

How Does Sentiment Analysis Work?

The science behind the process relies on natural language processing (NLP) algorithms and machine learning to classify content as positive, neutral, or negative. Usually, the text being analyzed is divided into chunks, and each chunk is given a score based on a previously defined scale. Basically, sentiment analysis works with the help of a machine learning model

Machine learning (ML) is a type of artificial intelligence that teaches computers to perform tasks by looking at data. A machine learning algorithm is programmed to find patterns in data. ML can be trained to analyze any new text with high accuracy. Deep learning is a popular subtype of ML that tries to simulate the way the human brain works. Since deep learning can work with unstructured data such as images, video, speech, and natural language texts it makes it one of the most powerful types of ML when it comes to sentiment analysis.

Types of Sentiment Analysis Algorithms

Depending on the specific needs and the amount of data to be processed, data scientists can usually choose one of the three different algorithms most commonly used: automated algorithm, rule-based and hybrid algorithms. Let's dig deeper into each sentiment analysis algorithm.

Automated algorithm

Relies solely on machine learning techniques and studies from the data it receives. Begins by creating a dataset containing a set of texts classified as positive, negative, or neutral. This is where learning begins and continues as a semi-automated process.

The algorithm learns from the data until the system reaches a level of independence sufficient to correctly assess the sentiment of a new, unfamiliar text. Then, what data is provided to the algorithm is critical. If the algorithm hasn't encountered a concrete example before, it won't do an accurate analysis.

One of the algorithm's greatest strengths is the amount of data it can analyze - far more than rule-based algorithms.

As for the downside, the algorithm has difficulty explaining the decision behind text analysis. It is unclear why it classified specific texts as positive or negative.

Rule-based algorithm

The algorithm is based on a manually created dictionary that defines sequences of positive and negative words. It analyzes the number of positive and negative words and phrases to see which ones dominate. Rules can be set around other aspects of the text, such as part of speech, syntax, etc. This approach is easy to implement and transparent when it comes to the rules behind the analysis.

Hybrid algorithm

The algorithm combines the two algorithms above and seems to be the most efficient solution. This is because it combines the high precision of ML with the stability of rule-based, dictionary-based methods.

Sentiment Analysis Challenges

It's sometimes challenging even for human beings to analyze sentiments and it often happens that two people would differently categorize a sentence as having positive or negative sentiment. If we know this, it's not surprising that machines also struggle with this and that sentiment analysis is one of the most difficult tasks in natural language processing (NLP). With all the language inconsistencies such as grammar-related issues, context-dependent meanings, or subjectivity issues, it is clear that data scientists have a tough task when designing algorithms for sentiment analysis.

Let's have a look at some of the most common challenges sentiment analysis faces.

Context

Even for human beings, it's sometimes quite difficult to analyze and properly understand a sentence or a piece of text without a broader context. Sentiment classification hugely depends on the context because the same text can be interpreted as having positive sentiment in one context or negative sentiment in some other. Let's consider a survey question:

What are the main advantages of our product?

For example, the answer might be:

Speed and price.

In this case, these two words would have a positive sentiment. But if the question was:

What are the main disadvantages of our product?

These two words would obviously have negative sentiments. Here we can see how context can influence polarity. This matter can be improved by thorough data preprocessing and post-processing. Unfortunately, the right solution on how to include context parameters in these processes is still to be found.

Irony and Sarcasm

People sometimes use positive words and phrases to express a negative sentiment. And again, even for people who lack a sense of humor, it might be difficult to recognize irony and sarcasm and properly understand it. If it's difficult for people then sure it's not easy for machines to detect it. For example, if we have a look at customer feedback on an e-commerce website:

The filter works great. It took me only half an hour to find what I was looking for.

This is obviously feedback coming from an unsatisfied customer expressing their negative sentiment using irony. However, for the words “works great” and “took only” the sentiment is positive so this review could be wrongly interpreted by a sentiment analysis system as bearing positive sentiment. This is why sarcasm and irony present big challenges for machine learning techniques.

Subjectivity

If we divide all texts into subjective and objective, then subjective would be the ones that contain and express certain sentiments, whereas objective express only some facts without containing any sentiment. If we look at the following sentences as an example:

The car is reliable.

The car is black.

The first sentence contains the word “reliable” which definitely has positive sentiment, while the second sentence simply states the fact about the color of the car which gives it a neutral sentiment.

Idioms, Colloquialisms, and Slang

Although some of these might be included in a machine learning algorithm and consequently recognized and interpreted properly, these words and phrases usually pose a huge obstacle to perform sentiment analysis. For example, idioms such as “break a leg” or “bite the bullet” might be quite confusing for a sentiment analysis system. Also a colloquialism such as “lemon” meaning “purchase that is unreliable and has many problems” can be a very challenging one to be processed by an algorithm. Since sentiment analysis tools usually work with formal language, slang can also be very challenging to interpret. For example, it's unlikely that an algorithm would properly interpret 'salty' as describing “someone who gets upset or angry over something minor“. Gen Z slang words really pose a huge challenge for sentiment analysis.

Emojis

Texts published on social media often contain emojis and these can quite significantly influence the final sentiment of the text. That's why a lot of preprocessing should be done related to emojis in order to whitelist and transform them into tokens. There are two basic types of emojis - Western emojis containing only one or two characters and a bit longer combination of characters Eastern emojis are made of. Lists of emojis and their corresponding unicode can be found online and can be very helpful in the preprocessing phase.

Comparison

Another challenge of sentiment classification is posed by comparison often used in texts, especially in customer feedback. It might be difficult to determine how exactly customers feel if you come across a review like this:

The new one is faster than the ones they had before.

If we don't know whether the “ones before” were fast enough, we can't really judge if the “new one” can be labeled with a positive, negative, or neutral sentiment.

Negation

In order to properly classify a sentence, understanding negation is crucial for any sentiment analysis model. For people, it's quite obvious that “good” and “not good” are completely opposite, but for machines, it is necessary to be trained on which words can be combined with negations so that they can classify them accurately.

Neutrality

Besides tagging textual data as positive and negative, a sentiment analysis model needs to be trained to recognize neutral sentiment as well. Usually, objective texts that we've mentioned above are classified as neutral. Also, irrelevant information can be tagged as neutral but with the risk of making noise that can harm the sentiment analysis performance.

Human Annotator Accuracy

In order for perform sentiment analysis properly, tools need data annotated correctly. The performance of sentiment analysis tools depends on the training data they used. If the data isn't accurate, a sentiment analysis system won't provide reliable results. As we've already mentioned, it quite often happens that two human annotators make different decisions and label data differently. Inter-annotator agreement measures this consistency by determining if two or more human annotators make the same decisions. As for now, the results are relatively low when it comes to sentiment analysis. So in order to achieve accurate sentiment analysis it's necessary first to improve human annotator accuracy.

EPAM Sentiment Analysis Tools

• InfoNgen is an AI text analytics software. This tool relies on natural language processing to help small, medium, and large businesses to search, collect and analyze information and make effective marketing decisions. It helps to do text and opinion mining, market research, analytics, and sentiment analysis.

• iSwarm is a sentiment analysis tool used to enhance online consumer & patient engagement and activation. Best choice for enterprises seeking to increase the effectiveness of their marketing campaigns.

*This article was updated in December 2022