All

AI-Based Sentiment Analysis Explained: Importance, Benefits, Challenges, and Use Cases in Software

Wondering how your clients feel about your services and products? Knowing their emotions is key to your business's success. Each piece of feedback, be it good or negative, can either help your business grow or pose challenges.

AI sentiment analysis can help you measure customer satisfaction by interpreting emotions from text, audio, and visuals. The demand for sentiment analysis software development is rising fast, and the market is expected to reach $5.83 billion by 2029. Why? Because it's a real must-have for companies of all kind!

Read on to learn about all the capabilities of sentiment analysis with its types, challenges, and use cases. You'll get expert insights based on the experience of SapientPro's engineers.

Businesses can now access a wide range of customer data from emails, surveys, tweets, service chats, and reviews. This offers a great opportunity to understand customer emotions but also brings the challenge of analyzing such vast amounts of text. They have to analyze this information to gain insights and make the best possible decisions.

Sentiment analysis is the process of checking text to see if it shows positive, negative, or neutral feelings. For example, social media posts mentioning a company with phrases like “can't contact” show negative feelings, whereas “great support service” reflects positive ones. More neutral negative feedback could be “support was fine” or “not sure if I'll come back.” 

Sentiment analysis tools help companies dive deeper into what customers feel, think, and prefer or dislike about their products or services. This insight helps enhance customer experiences and strengthens the brand image.

With so many ways for people to share their thoughts online, organizations need tools to quickly monitor what is being said about them and their products. By using sentiment analysis, businesses can more easily identify where customers face challenges in their overall experience. Here are three key reasons why AI-based sentiment analysis is an important tool.

#1 Reduced Bias for Objective Reviews

AI sentiment analysis gives businesses unbiased insights from reviews. Unlike human analysts who might let their personal opinions influence the results, AI algorithms evaluate text consistently and without bias. This objectivity helps businesses trust their feedback data, which leads to better decisions.

For example, when a customer writes a review with both negative and positive comments, a human reviewer might pay attention to the negative parts first. In contrast, AI-powered sentiment analysis looks at the whole review, ensuring it captures and considers both negative and positive sentiments.

#2 Reduced Human Workload and Better Scalability

AI sentiment analysis can quickly go through and evaluate thousands of reviews, comments, or social media mentions in just moments, a task that would take much longer for a human team. This automation eases the load on customer service and marketing teams and lets them focus on strategic decisions and creative tasks.

As businesses grow, AI sentiment analysis can manage more data without requiring more staff, which makes it a cost-effective and flexible solution.

#3 Real-Time Brand Reputation Monitoring

In today's digitalized world, brand reputation can shift quickly. AI-powered sentiment analysis helps businesses stay on top of public opinion by constantly monitoring social media and forums. This allows them to identify changes in sentiment, like positive feedback from a successful campaign or negative reactions from a sudden issue, and act right away.

This approach helps companies avoid reputation issues, make the most of positive trends, and maintain a strong, consistent image across all channels.

Sentiment analysis involves using technologies like natural language processing and machine learning to help machines understand and interpret text as humans do. It can be achieved through a rule-based system, a machine learning system, or a hybrid approach that combines both.

For example, rule-based systems are quick and provide fast results, while machine learning systems are more flexible and handle complex tasks. Each method has its pros and cons. Let's take a look at these three approaches to help you understand them better.

Rule-Based Sentiment Analysis

Rule-based sentiment analysis is a method that helps identify the emotional tone in text using set rules. It looks for specific words and phrases associated with certain emotions to decide if the sentiment is positive, negative, or neutral. Unlike methods that rely on statistics or machine learning, this approach uses fixed guidelines.

For example, a positive list of words could include "affordable," "quick," and "reliable," while a negative list might have "costly," "lagging," and "fragmented." The software looks for these words, counts them, and then calculates an overall sentiment score based on whether they are positive or negative.

Machine Learning Sentiment Analysis

Software can use machine learning to figure out the sentiment in a text by analyzing words and their structure. Developers can then design algorithms to help software understand emotions in text, much like people do. 

Here are some commonly used classification algorithms:

• Linear regression: logistic regression in sentiment analysis predicts if a review is positive or negative by analyzing the words used in it.
• Naive Bayes: Naive Bayes predicts sentiment by analyzing specific words or features in the text. For sentiment analysis, it looks at how frequently positive and negative words appear in a review to decide if the sentiment is positive or negative.
• Support Vector Machines (SVM): SVM separates words into positive and negative sentiments by finding the best line (or boundary) that maximizes the distance between them.
• Deep Learning (DL): deep learning models like convolutional neural networks (CNNs) or recurrent neural networks (RNNs) can automatically identify complex language elements in the text. This allows them to detect subtleties like sarcasm or context that simpler models might overlook.

Hybrid Solutions

A hybrid approach to sentiment analysis uses both rule-based methods and machine learning models to understand clear and subtle emotions in a text. In such a system, the rule-based component identifies obvious sentiment signals and organizes the data. Then, the machine learning part analyzes complex elements that need context or subtleties beyond fixed rules.

For example, take a customer review: "The camera quality is overall fine, but the zoom ring is horrible." A rule-based analysis simply labels "fine" as positive and "horrible" as negative. In contrast, a machine learning analysis looks at the context and overall sentiment. It might decide, based on past data, that the customer finds the zoom ring issue more important than the overall camera quality, even though there's both positive and negative feedback.

As mentioned earlier, sentiment analysis uses natural language processing and machine learning to accurately identify emotions in user interactions. First, machine learning classifiers analyze the text by learning from large datasets with examples of positive, negative, and neutral sentiments. Then, the text is broken down into words, phrases, and sentences. Find out what's happening next in a typical machine learning classifier.

Preprocessing

In training, the feature extractor looks at the input text to find key features and turns them into feature vectors labeled as positive, negative, or neutral. With methods like word embeddings, it represents similar words alike. These vectors help create a classifier model using a machine learning algorithm.

Prediction Phase and Keyword Analysis

In this stage, new data is turned into feature vectors by a feature extractor, then analyzed by a classifier model to figure out its sentiment. Algorithms like Naïve Bayes, linear regression, support vector machines, and deep learning help in this text sentiment analysis stage.

The machine learning classifier gives a sentiment score to the new data, where 1 might be negative, 0 neutral, and +4 positive. For example, take the sentence "I really loved the Acer Swift Go 14 laptop. Even though my touchpad occasionally has issues, it’s uncommon to find a laptop of this quality at such an affordable price." Sentiment scores might go like this:

• "Acer Swift Go 14 laptop…loved" gets a score of +4
• "touchpad…issues" gets a score of -2
• "it is uncommon…laptop of this quality…affordable price" gets a score of +3

After adding these scores, the overall sentiment is positive, though there are some negative feelings about the touchpad.

Let's take a closer look at the four main types of sentiment analysis and see how modern businesses use them.

Fine-Grained Sentiment Analysis

A fine-grained analysis specifies the sentiment topic and intensity for each phrase or clause. This method tackles issues that simpler ones can't, like comparisons: "Samsung is better than LG", or mixed reviews: "The salad didn't taste good, but I liked how it was delivered."

Fine-grained analysis can be used in various business areas, such as:

• Product reviews: identifies which product features customers like or dislike.
• Political analysis: monitors political speeches to see their effects and identify where people agree or disagree.
• News and media: analyzes content to understand public sentiment on current events.

Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis looks at people's feelings about specific parts of a product or service. For example, in a restaurant review, someone might say the service was great but complain about a dirty table or high prices.

This method has three main steps:

1. Identifies the features mentioned in reviews.
2. Determines the sentiment for each feature.
3. Summarizes the overall sentiment for each aspect.

This type of analysis is used in different areas, like:

• Finance: helps track business elements that interest investors, like growth opportunities;
• E-commerce: reveals what customers like or dislike about product features, such as price;
• HR: examines cultural aspects within a company, like work-life balance.

Intent-Based Sentiment Analysis

Intent-based analysis does more than just identify if a text is positive, negative, or neutral. It uses machine learning to understand the text's purpose, such as whether it asks a question, makes a complaint, gives a command, or expresses a wish.

Intent-based sentiment analysis has many uses in business, such as:

• Legal: it helps identify intent in legal documents, ensuring accurate responses;
• Marketing: it evaluates marketing strategies by analyzing customer interactions;
• Healthcare: it examines patient feedback to understand their experiences better.

Emotional Detection

Emotion detection sentiment analysis tries to understand how someone feels when they write something, looking at their deeper thoughts and intentions. It's more than just tagging text as positive, negative, or neutral. Instead, it identifies specific emotions like frustration, indifference, or wonder.

This type of analysis has lots of practical uses, for example:

• Speech analysis: can detect emotions in real time, improving how humans interact with machines.
• Virtual reality: personalizes experiences by adjusting based on the user's emotions.
• Customer feedback analysis: helps identify trends in reviews, which leads to a better quality of products and services.

Companies often struggle to ensure accurate sentiment analysis. A key challenge is training machines to interpret emotions as intuitively as humans. The diversity of languages adds to this complexity. Despite these hurdles, advancements in data science help improve sentiment analysis software and provide solutions to each limitation. Let's take a look at the main challenges in sentiment analysis.

Irony and Sarcasm

People often use irony and sarcasm in social media chats and memes. When someone uses backhanded compliments to express negative feelings, it can be tricky for sentiment analysis tools to catch the real meaning. This might cause feedback to be marked as "positive" when it's actually negative. For example, saying "Oh great, another day" may sound positive because of the word "great," but the true sentiment is negative.

This issue can be addressed with advanced machine learning models like transformers (BERT). They help by understanding context and recognizing subtle text cues. Also, sentiment analysis models should be trained with datasets specifically labeled for sarcasm to better identify sarcastic language.

Negation and Polarity

Negations like not, never, and cannot may confuse machine learning models. One sentiment analysis example could be the phrase “I can't not go to this meeting”. This one actually means the person intends to attend, and the algorithm has to understand that.

To cope with this challenge, developers can create parsers that understand sentence grammar to detect negation and its impact on nearby words. Also, machine learning techniques can be used to consider relevant word sequences in context, rather than just single words.

Idiomatic Language

Machine learning programs often have trouble understanding figures of speech. For example, an idiom like “bite the bullet” can confuse these programs because they take language literally. This can lead to misinterpreting or missing key parts of comments or reviews.

To fix this, it's useful to build dictionaries of idioms and their sentiments. Using rule-based methods to detect idioms combined with machine learning models that learn from context can also help improve understanding.

Multilingual and Cultural Complexity

Direct translations often miss the true meaning because each language has its own subtleties. You can't always translate expressions into English or any other language and expect to keep the original intent. For example, translating the idiom “beat around the bush” into Italian would cause it to lose its original meaning.

To address this, developers should use multilingual models that understand different languages and their unique features. These models need training on diverse datasets with various languages and cultures to increase their adaptability and accuracy.

These strategies can help your sentiment analyzer become more detailed, even despite the complexity and diversity of human language.

There are hundreds of use cases of AI in business, and sentiment analysis is one of the leading solutions. It improves customer experiences, business outcomes, and supports marketing strategies. Let's look at some real-world examples to see how sentiment analysis enhances customer services.

Social Media Monitoring

AI-powered sentiment analysis makes social media marketing feel more genuine and personal. It helps businesses make sense of the vast amount of social media data to create content that truly connects with their audience. Artificial intelligence tools can recommend content that engages people more effectively by analyzing users' posts, comments, and messages.

For example, Marriott uses AI to look at guest data and provide personalized suggestions based on their previous stays, feedback, and preferences. This helps them recommend the best room upgrades, spa treatments, dining options, and more, which increases guest satisfaction and encourages them to come back.

Customer Feedback Analysis

Most sentiment analysis tools classify feedback as positive, negative, or neutral. However, more advanced AI tools can also identify emotions and the urgency of customer comments. In fact, 28% of customer service experts use AI for this purpose, making it a leading application of AI in customer service.

For example, if 50 customers reviewed your product in a week, AI could sort these reviews by sentiment and pinpoint which ones need immediate attention.  This helps companies quickly address critical feedback and prevent small issues from growing.

Campaign Performance Tracking

Traditional marketing tools are great for tracking content performance, usually looking at page views or how long someone stays on a page. However, they don't explain why things happen. For example, if you see a high bounce rate, these tools won't help you figure out whether it's due to the content or the page design.

AI-powered sentiment analysis tools offer a solution by analyzing large amounts of data to find insights that people might miss. Using machine learning, they can identify patterns and trends, which improve content strategies.

Sentiment analysis also lets businesses see customer reactions to their marketing efforts in real time. Companies can see which parts of their campaigns are popular with their audience and make changes to improve results.

Market Research

AI sentiment analysis helps businesses grasp customer opinions on their products and services. Review, survey and social media data are examined to ensure messaging aligns with customer feelings.

Amazon is a great example of using AI in marketing. It uses deep learning and machine learning to guess what customers might want to buy. Amazon finds patterns in past purchases and browsing history to recommend products you might like. This personalized system is really effective and makes up about 35% of Amazon’s sales.

The cost of creating AI sentiment analysis software can differ depending on the project's complexity, the technology chosen, the team's experience and location, and specific project needs.

Let's take a look at the possible costs for a mid-sized sentiment analysis solution. Keep in mind that these are only preliminary estimates without looking into your project's specifics. You can get a detailed evaluation of your project by contacting SapientPro's team.

Planning to integrate or even develop your own AI sentiment analysis solution? Contact SapientPro today to get an experienced AI development partner who will provide you with a detailed breakdown of all the costs and timelines for your project. You've got the idea, and we've got the solution. Let's team up and empower your business!

SapientPro is a reliable tech partner for developing and maintaining AI-drive solutions for your business. We have 8+ years of experience in building, improving, and integrating products of all kinds in 200+ projects. Our goal is to build a long-term partnership where both our clients and we grow together. Interested in brining your idea to life? Then let's team up!