For any marketer or business, two things are a must-do in their marketing campaigns: using recent technologies and meeting consumer needs. AI sentiment analysis is a perfect combination of both. It's got a new tech and keeps customers at the center of attention by catering to how they feel about your brand or product.
In a time where 19% of marketers are spending 40% of their marketing budgets on AI technologies, this duo of customer centricity and advanced tech adoption is even more important. That's not to say that sentiment analysis is an entirely new thing.
Brands have been using it for years now. However, existing tools often fail to understand human expressions like sarcasm and irony, as well as emojis on social media. They also tend to be a tad bit expensive and have limited functionality, often only supporting English and a few other languages.
Now that AI is in the picture, the capabilities of these tools have expanded. Let's discuss how you can use AI sentiment analysis in your campaigns and why it's such a hit.
- AI Sentiment Analysis 101: Understanding the Basics
- Why Is Sentiment Analysis Important?
- Unlocking AI Sentiment Analysis: Advanced Applications You Need to Know
- Cracking the Code: Solving the Biggest Challenges in Sentiment Analysis
- Trends and Innovations Shaping AI Sentiment Analysis
- Where AI Sentiment Analysis is Headed Next
- Frequently Asked Questions
AI Sentiment Analysis 101: Understanding the Basics
Sentiment analysis is quite simply a process in which a tool analyzes large volumes of text to identify the sentiment it expresses: positive, negative, or neutral. For businesses, this means getting a peek into the customers' feelings about their brand.
We all analyze sentiments in our daily lives. For example, when a coworker sends an email in which they tell you how fed up they are with the delays a certain team member causes, you can tell that they are frustrated. Similarly, a friend texting you that they can't want to see you this weekend expresses joy and excitement.
However, manual sentiment analysis only works for interpersonal situations. Businesses are talked about widely by thousands, if not millions, of people. We've all discussed a brand on social media or in online conversations.
So, brands have to analyze thousands of tweets or hundreds of reviews from third-party sites to get a gist of how customers feel. AI sentiment analysis facilitates this process through quick and accurate analysis.
But how does this work? There may be two types of analyses: rule-based or machine learning. Both approaches deliver similar results but differ in how they do it.
In rule-based sentiment analysis, an AI tool is trained on certain "rules." These are guidelines that dictate which words or phrases indicate positive, negative, or neutral sentiments.
For example, a rule may state that the words "happy" and "satisfied" indicate positive sentiment. The same may apply to words like "well-made," "efficient," "affordable," or "innovative." On the contrary, words like "disappointed," "slow," and "poor" reflect negative sentiment.
The tool will then classify the sentiment based on the presence of these words in a text. Then, it gives you a score based on the proportion of positive, negative, or neutral words present in the text.
Source: linkedin.com
Machine learning sentiment analysis algorithms not only analyze the word but also the text appearing before or after it. In this way, they can capture nuance, just like a human reader would. These tools also learn continuously from the data, so they keep getting better over time.
A common machine learning classification algorithm is Naive Bayes. It uses probability to classify text based on how probable it is for the words to appear in a positive, negative, or neutral text block.
Some AI sentiment monitoring tools also use deep learning models that employ multiple algorithms to analyze text like a human brain would, showing even more accurate results. These tools can also classify sentiments like anger, disgust, frustration, excitement, and indifference. They can also understand sarcasm and other forms of language that may be challenging for rule-based tools to spot.
Sentiment Analysis vs. Opinion Mining: What's the Difference?
Often, marketers may use the terms sentiment analysis and opinion mining interchangeably. While both these techniques complement each other, they're definitely distinct in their functionality.
Sentiment analysis is an overall sentiment classification of text. It simply answers the question, "Is this text positive, negative, or neutral?" It doesn't go deeper into why a particular sentiment is expressed.
Opinion mining does just that. It is the process of extracting insights and opinions from the text. It goes beyond sentiment labels and provides more context to the emotions expressed.
For example, a customer leaves a review for a restaurant that says,
"The food was delicious, but the service was slow."
Sentiment analysis will label this review as neutral, as it contains both positive and negative sentiments. However, opinion mining will further analyze the review to extract the specific opinions mentioned: good food but slow service.
If you've recently shopped from Amazon, you must have seen opinion mining in action. The eCommerce giant now shows AI-generated review summaries for every product. For example, on this page for a Shark vacuum, the AI-generated text shows the positive features customers have most commonly mentioned in their reviews, such as "strong suction power" and "lightweight design."
Amazon AI-generated review summaries
The 4.4-star rating indicates a positive sentiment, while this summary shows why customers feel positively about the product. AI plays a role in both sentiment analysis and opinion mining, first by identifying the overall sentiment and then by extracting insights from the text.
Why Is Sentiment Analysis Important?
The level of competitiveness in the business world makes it compulsory for brands to be as conscious of their consumers' feelings as possible. If you're not proactive, a competitor may swoop in and take over your market share.
Brands have been doing this since forever. If one of their competitors has negative PR, they will try to capitalize on the situation by highlighting their own positive features. For example, when Fable, a social app for book readers, came under fire for its AI generating offensive summaries with remarks on gender, sexuality, and race, Goodreads, their competitor, was quick to promote itself.
Put yourself in Fable’s shoes and ask yourself, could you have avoided this with proactive sentiment analysis? Most likely, yes. Here are some other benefits of sentiment analysis.
Enhancing Marketing Strategies
Nowadays, customers have become accustomed to brands meeting their individual needs. That's why 72% of consumers only claim to engage with personalized messages. However, you can't really personalize marketing messaging unless you know your customers' sentiments.
Sentiment analysis gives you a view into your audience's feelings. For AI-assisted personalization, you can further use tools that provide contextual information about the analyzed sentiments. These pain points or satisfaction drivers can guide your marketing strategy, giving you an idea of how to approach each customer segment.
The emotional intelligence obtained through sentiment analysis also helps you retain customers. Let's say that your sentiment analysis tool shows you that words like "limited" and "difficult" are recurring in your negative reviews.
As an apparel store, this could indicate that customers are unhappy with limited sizing options or have trouble working with the size chart on your website. You can then address these issues to improve the retention rate.
Understanding Social Media Trends
Sentiment analysis isn't just for customer reviews on your website but also for social media posts and comments. Public opinion typically breeds on social media these days, with people sharing their thoughts on X and TikTok quite freely.
You can use a blend of sentiment and AI trend analysis tools to learn about shifting customer expectations. It doesn't necessarily have to be about your brand alone. The general sentiment towards a topic or trend can also inform your marketing strategy.
In Fable’s case, if they had used sentiment analysis tools, they may have noticed the growing concern about AI bias in the publishing space. These tools would have also shown them that readers aren’t happy with AI-generated summaries. Many readers and authors have also expressed their disdain about the use of generative AI tools for writing books.
Keeping these sentiments in mind, Fable could have avoided using generative AI to "roast" its users’ reading list for the year. They would have also been aware of the conversations around AI bias and the loss of intellectualism in reading on the Booktok space.
Boosting SEO and Digital Strategies
When you have a better idea of your customers' sentiments, you can tailor your SEO strategy to engage with them closely. For example, if you conduct a sentiment analysis of the comment section of your website blog and notice a recurring theme of customer dissatisfaction, you can use that information to create content that resolves these issues.
Similarly, positive mentions become opportunities for link-building. For example, if an individual blogger mentions your brand in a positive light, you can reach out to them and ask for a backlink from their website.
You can also collect user-generated content (UGC) this way. Use a sentiment analysis tool to find positive or neutral mentions of your brand on social media. Then, repost this content on your social media platforms or curate it for your next newsletter.
One tool that can facilitate this process is Semrush Brand Monitoring. It has a separate feature for sentiment analysis in which all of your brand's mentions are labeled as positive, negative, or neutral. You can also see overall statistics (percentage of each sentiment type) for all brand mentions.
Brand monitoring
If you feel that a mention has been labeled incorrectly, you can fix it manually. The tool will then learn from your correction and improve its sentiment analysis abilities. You can also use Brand Monitoring to track your competitors' mentions. If you notice a negative sentiment about them, you can position yourself as a better alternative through your SEO and marketing campaigns.
The tool's filtering capabilities let you spot high-impact negative or positive mentions and focus on them promptly. For the negative ones, you can take appropriate measures to rectify the situation. The positive mentions can become backlinking, guest posting, and UGC opportunities.
Brand monitoring
Positive mentions can also serve as social proof. For example, if you launch a new product, you can analyze all brand mentions that include the product name on Instagram and TikTok.
Show the positive mentions as social proof on social media or your website. As for the negative ones, use the feedback to improve your product or service. It's best to set up email alerts for negative mentions so that you can give them immediate attention before they become bigger issues.
Unlocking AI Sentiment Analysis: Advanced Applications You Need to Know
Traditional sentiment analysis is now supplemented with AI, which has led to its advanced use cases. Now, businesses can use AI sentiment analysis to keep an eye on their media mentions and customer satisfaction rates.
A good example of this comes from Marriott. The hotel chain's Bonvoy program is for customers who prefer personalized services. Marriott uses AI to peek into its customers' preferences and travel patterns. The AI-run system analyzes how customers feel about different brand offerings and services. Then, their chatbots provide personalized recommendations to customers to improve retention.
Since sentiment analysis can be limited to certain keywords (brand names, events, etc), it's possible to get niche insights for your business. For example, a trading app can use sentiment analysis to hear what people are saying about a new coin. It can then use these insights to create marketing messaging for customer segments that feel positively about the coin.
Newer sentiment analysis tools now come with multimodal analysis capabilities. They can also analyze images, videos, and audio to determine the sentiment behind them. These tools can help you gauge customer feelings across channels like podcasts, TikTok videos, and customer service calls.
Besides business, other sectors can also benefit from sentiment analysis. For example, analysis of what people are saying on social media can help predict voter behavior and election results.
Cracking the Code: Solving the Biggest Challenges in Sentiment Analysis
Humans speak in a nuanced language, often using sarcasm and irony to express their true opinions, presenting a challenge for sentiment analysis systems that rely heavily on text analysis. Let's say your brand prides itself on being a budget-friendly option for customers. Someone leaves a negative review about one of your products, saying ''so much for being a budget-friendly option''.
A sentiment analysis system may only pick up ''budget-friendly'' and interpret the review as positive. However, the reviewer is being sarcastic, and the sentiment behind their words is actually negative.
Similarly, consumers may use different languages and dialects to talk about your business. Most sentiment analysis tools are only English-optimized, so they can't gauge sentiment from non-English sources. Even in English, they might not be able to pick up slang or colloquial phrases that customers use to express their opinions.
The slang issue is even bigger with Gen Z customers. Consumer sentiment analysis algorithms don't know that ''fire'' or ''lit'' is slang for ''good'', so they might just miss positive mentions.
The best way to deal with these challenges is to choose tools that use deep learning algorithms to make sense of human language. Tools like Brand Monitoring continuously improve themselves as you flag false results, so you get more accurate results over time.
You can further use AI data analysis tools to compare sentiment data with real-time sales and marketing insights to see how consumers' opinions are impacting your business.
Trends and Innovations Shaping AI Sentiment Analysis
AI sentiment analysis has seen a lot of enhancement with GPT-based models that can better understand the text and predict sentiment. Open AI's ChatGPT is an example most people are familiar with. You can feed it text, and it will detect the emotion behind the words.
It goes beyond just positive or negative sentiment. AI-assisted tools can even pinpoint the exact sentiment, such as anger or disappointment. This way, you get a better understanding of what your customers are feeling and why they might be feeling that way.
Going forward, relying on text alone won't be enough. You'll have to use visuals and speech in your analysis, too. The good news is that AI-backed sentiment analysis tools can analyze all these formats to give you a comprehensive view of consumer emotions across channels that were previously not possible.
Where AI Sentiment Analysis is Headed Next
From social media content creation to digital marketing strategies and customer service, there are quite a lot of applications for AI sentiment analysis. Consumers are more vocal than ever about their feelings about brands and their products. So, there's a lot of data available on which to base your campaigns.
AI-powered sentiment analysis tools give you a glimpse into your customers' minds. How do they feel about your business? Why do they feel this way? These tools can even tell you the opinions expressed about your brand in every mention. The cumulative insights from text, visual, and audio-based mentions can help you create personalized campaigns to target specific emotions.
Frequently Asked Questions
What is sentiment analysis, and how does AI enhance it?
Sentiment analysis is a process in which a software analyzes large text volumes to determine if its sentiment is positive, negative, or neutral. AI takes it up a notch by spotting subtler emotions (like sarcasm or frustration) and analyzing large volumes of data quickly.
Which industries benefit most from AI-driven sentiment analysis?
Industries like travel, eCommerce, and retail can benefit largely from AI sentiment analysis because it helps understand customer vibes in real time. Healthcare can use it for patient feedback, while finance can track market sentiment. Similarly, the entertainment industry benefits by gauging audience reactions to content.
What are the challenges in detecting sarcasm in sentiment analysis?
Sarcasm is tricky because it flips the meaning of words, like saying, "Oh, great!" when things are bad. AI struggles with this because it usually just relies on text-based rules. Without enough training data or multimodal inputs (like voice or facial cues), sarcasm often flies under the radar.
How do free sentiment analysis tools compare with paid solutions?
Free tools are great for basic sentiment analysis, but they often lack depth and accuracy. Paid solutions offer advanced features like emotion profiling, real-time analysis, email alerts, and integration options. So they tend to deliver more reliable insights. Most paid tools, such as Semrush Brand Monitoring also have advanced filtering options. You can also flag false results; the tool then learns from your corrections over time to improve its analysis capabilities.
What role does human oversight play in AI sentiment analysis?
AI can misinterpret sarcasm, irony, cultural nuances, or complex emotions. Humans step in to refine algorithms and handle edge cases for higher accuracy. It's a teamwork thing: AI handles scale and speed, while humans provide the understanding and judgment AI can't fully replicate.
How can businesses leverage real-time sentiment analysis?
Businesses can use real-time sentiment analysis to respond quickly to customer feedback and manage PR crises. For example, monitoring social media during a product launch helps adjust strategies if sentiment shifts. Real-time sentiment analysis also helps spot opportunities for backlinks and user-generated content.
Which AI models are most effective for sentiment analysis?
Models like GPT and BERT are considered the gold standard for sentiment analysis. They're pre-trained on tons of text, so they understand context and nuance well. They can be further fine-tuned to detect emotions and complex sentiments in various industries.
How is sentiment analysis used in political campaigns?
In political campaigns, sentiment analysis helps track public opinion by analyzing social media, articles, speeches, and news. It can reveal how voters feel about a candidate and their policy, letting campaigns adjust messaging in real time.
Can AI detect sentiment in non-verbal communication?
AI can detect sentiment in text and images. However, it struggles with understanding non-verbal communication, such as facial expressions and tone of voice. Advanced technologies like emotion recognition software can help bridge this gap, but human involvement will still be needed.
How accurate are current AI sentiment analysis tools?
AI sentiment analysis tools are pretty accurate but not perfect. They're great at picking up on clear emotions like happiness or anger but can struggle with things like sarcasm or non-English language. Tools trained on smaller datasets may also not be as accurate compared to those trained on larger, more diverse data sets.
