Introduction to AI-powered sentiment analysis
As we get deeper into sentiment analysis, check out our blog for more information on the related topics, such as AI analytics and text mining.
What is sentiment analysis?
Sentiment analysis (or opinion mining) is the process of identifying emotions expressed in words using artificial intelligence and its subtypes. It follows a predetermined metric to understand how positive, neutral, or negative a piece of text sounds. AI can analyze millions of comments posted on social media, review sites, and online surveys. It can even obtain data from videos. That’s how sentiment analysis enables companies to spot negative attitudes towards their products, empowering them to make a change and address those issues in real time.
Here is an example of different sentiments identified in customer feedback:
| Positive | Neutral | Negative |
|---|---|---|
|
This café is great! The staff is friendly, and the coffee is delicious. |
Avernum is a series of demoware role-playing video games by Jeff Vogel. It is available for Mac and Windows-based computers. |
I’ve had multiple conversations with your customer support, and they were worthless. No one could address my issues. |
Generative AI in sentiment analysis
Generative AI and large language models (LLMs) offer more accurate and nuanced sentiment analysis compared to traditional natural language processing (NLP) solutions. Large-scale pre-trained language models, such as GPT-4, can understand the broader context and give a more precise sentiment interpretation. Also, these tools are better at understanding sarcasm, ambiguous statements, and different writing styles due to their advanced knowledge of language patterns.
Just recently, a team of German researchers investigated the fitness of Gen AI for sentiment analysis. They compared the performance of three large language models against a well-established transfer learning model with an excellent track record in sentiment analysis. The LLMs performed equally well and even surpassed the traditional machine learning (ML) model in some cases. What was even more remarkable is that the LLMs could clearly justify their output. Llama 2 was the best performing model, exhibiting human-like reasoning when explaining its output, which marks a sharp contrast to regular AI models lacking an explainability layer.
Benefits of AI sentiment analysis
Sentiment analysis turns unstructured data into actionable information, which empowers businesses to achieve the following goals:
-
Retaining present customers and attracting new ones
-
Retaining talent and identifying issues that hinder employee productivity in real time
-
Improving marketing campaigns by understanding your customers’ reactions and gauging sentiment towards competition’s marketing efforts
-
Tracking how product modifications impact customers’ attitudes. For example, you can see how customers respond to changes in a product’s interface or adding new features.
-
Anticipating customer and employee churn. Real-time sentiment analysis enables companies to notice heated discussions on social media and interfere when an angry person is ready to move on.
How does AI-based sentiment analysis work?
Earlier, companies only used traditional methods, such as surveys and focus groups, to understand customers’ feelings about their products and services. Employing big data analytics empowers organizations to mine larger data volumes, like social media data, to get a more precise picture of clients’ opinions. In its current state, sentiment analysis is a sub-field of natural language processing (NLP).
Sentiment analysis is built on one (or a combination) of these two techniques:
-
Rule-based sentiment analysis
This method uses a dictionary that contains words labeled by sentiment. Here is an example of such a dictionary:
| Word | Sentiment |
|---|---|
|
Good |
0.5 |
|
Excellent |
0.9 |
|
Terrible |
– 0.9 |
The rules can use NLP techniques, such as tokenization and stemming, to identify words before looking them up in the dictionary. The final sentiment score is combined with additional rules to accommodate negations, dependencies, and other issues in rule-based methods.
Rule-based sentiment analysis is naïve in that it doesn’t consider how words are arranged in a sentence and can’t recognize sarcasm. You can always add new logical connections, but this might alter the existing ones. This approach requires frequent updates and maintenance.
-
AI-based sentiment analysis
Here, we teach an ML or Gen AI model to extract information using training datasets. After sufficient training, the algorithm will be able to infer sentiment from new texts. It doesn’t just follow predefined rules; it can also learn to detect sarcasm, synonyms, and other complex cases.
It is also possible to use a hybrid approach that combines rule-based and AI sentiment analysis into one system. Some sources claim that this technique often produces more accurate results.
Five creative ways AI sentiment analysis can help your business
Here are five key use cases of AI-powered sentiment analysis:
- Enhancing customer service
- Facilitating market research
- Crafting marketing campaigns
- Improving products
- Measuring employee engagement and satisfaction
1. Sentiment analysis for customer service improvement
Firms can enhance their customer service with AI-powered sentiment analysis. These tools can monitor social media interactions in real time, spot poor reviews, and alert managers to any issues with the company’s products and services.
Companies can also use sentiment-related information to prioritize support tickets and customer interactions based on the implied urgency and emotional tone. These analyses give sales and support personnel insights on how to treat customers and respond with empathy.
T-Mobile, a telecommunications company, analyzed customer feedback and reviews to identify the common problems customers have with T-Mobile’s services and resolve them. This initiative decreased client complaints by a whopping 73%.
2. AI-driven sentiment analysis for market research
Natural language processing-based solutions can monitor social media platforms to detect shifts in consumer preferences and analyze news articles, looking for emerging trends and topics. For instance, JP Morgan Chase uses IBM Watson to detect sentiment in online interactions and sense market movements. And another example from the academic world—a Korean research team deployed sentiment analysis to predict stock prices by analyzing 8-K financial reports divided by sector.
Another application of AI in sentiment analysis in market research is to gauge the prevailing sentiment towards your main competitors and learn from them. If they release a new product that is not successful, you can see what customers dislike and avoid making the same mistakes. This feedback can also help you strategize and position your products effectively.
3. AI sentiment analysis for crafting marketing campaigns
Companies can identify the prevailing sentiment in user groups and utilize it to craft marketing campaigns that resonate with people on a personal level. AI sentiment analysis can serve as a tool that monitors existing campaigns for negative sentiment in real time and alerts marketers to adapt or terminate the campaigns that upset the audience.
Through artificial intelligence sentiment analysis, companies can identify satisfied customers to target with loyalty programs and ask for positive reviews.
Coca-Cola relied on AI to understand its fans’ sentiments. The company could see how people envision the future through flavors, colors, and emotions and used this knowledge to market its Y3000 Zero Sugar futuristic product.
4. AI sentiment analysis for product improvement
With AI-powered sentiment analysis, companies can understand what customers like and dislike and adjust their existing products accordingly. These tools can also help identify frequent complaints about competitors’ offerings and engineer new products with the desired features.
The online educational platform, Coursera, deploys artificial intelligence sentiment analysis tools to process student survey responses, discussion forums, and course feedback to understand how they feel about different courses and update the material accordingly.
Also, McDonald’s relies on AI sentiment analysis to monitor social media and identify food items that receive negative reviews. The organization can either improve these items or remove them from the menu.
5. AI-powered sentiment analysis for measuring employee engagement and satisfaction
Understanding how employees feel is crucial to retaining talent and maximizing productivity. However, the traditional methods of measuring employee satisfaction are not reliable. The old-fashioned approach requires the HR department to analyze survey responses and produce actionable recommendations. But the answers to open-ended survey questions can be lengthy and confusing, not to mention that HR bias can skew the results.
Instead of manual intervention, businesses can use machine learning-powered sentiment analysis to process survey responses and social platforms. These modern tools can not only accurately detect sentiments but also pinpoint the issues that make employees feel that way.
There are several ready-made HR tools that can decipher employee sentiment. For example, the German company Leapsome offers AI sentiment analysis as a part of its HR platform. This tool helps management understand which problems employees are facing and who is getting ready to look for new opportunities. Similarly, the California-based ThriveSparrow can gauge employee sentiment and build heatmaps displaying engagement levels across departments.
How do you approach AI sentiment analysis?
Ready to incorporate AI-powered sentiment analysis into your business? Our machine learning expert, Sergey Leyko, highlights three options available for consideration:
-
Cloud-based ready-made solutions. Cloud vendors offer tools built and trained on datasets of their choice. You can submit your sentiment extraction requests through an API and receive a response, but the vendor will not explain how the tool works under the hood.
-
Auto ML solutions. You partner with an AI software development company that trains algorithms on your datasets. You don’t participate in algorithm development.
-
Custom AI-enabled sentiment analysis. You present your requirements to a custom AI development company. They build a model corresponding to your business demands and train it with your dataset.
The table below compares the three options based on different aspects arranged in a prioritized list.
| Aspect priority | Cloud-based sentiment analysis solutions | Auto ML sentiment analysis solutions | Custom sentiment analysis solutions | |
|---|---|---|---|---|
| Time to deploy |
High |
Fast. If you buy a ready-made solution, you can use it immediately. |
Slower. It takes days and even weeks. |
Even slower. It can take several weeks or even months to build a custom AI sentiment analysis tool. |
| Costs |
High |
Low initial investment. But you might pay more in licensing fees as you submit more queries. |
Similar to cloud-based solutions |
Higher initial investment, but costs will decline later. For more insights, you can refer to our AI costs article. |
| Scalability |
Medium to high |
It can be scaled |
Similar to cloud-based tools |
Highly scalable. You can ask your vendor to build the solution with scalability in mind, but it will require additional resources. |
| Data privacy |
Medium to high |
Your data is given to a third party |
Your data is given to a third party |
Your data remains on premises |
| Maintenance and support |
Medium |
Likely to be limited. Vendors can even decide to stop supporting this version of the product and leave you on your own. However, you can arrange for support as a part of your deal for additional fees. |
Similar to off-the-shelf solutions |
Custom software vendors can allocate full-time dedicated teams to support you |
| Fitting in with your existing applications and APIs |
Medium |
Reasonable |
Reasonable |
It is designed to fit into your applications |
| Ability to handle complex cases |
Medium |
Depends on the solution. Some complex cases might be included, some will not be there. |
Similar to cloud-based tools |
A custom AI-based sentiment analysis solution vendor can deliver a tool tailored to your business cases, whether it’s typos, sarcasm, or any other demands |
| Competitive advantage |
Low to medium |
Limited, as you are using the same algorithms as some of your competitors |
It depends. You can’t influence it. |
It gives you a competitive advantage as your software is unique. It is possible to develop a robust model with the desired quality-to-speed balance. |
| Incorporating updates |
Low |
You wait for the vendor to release updates at their convenience |
Similar to cloud-based solutions |
You can incorporate updates whenever needed, as you own the code |
| Data requirements |
Low |
None. The provider uses their own datasets for training. |
You supply training data |
You supply training data |
As you can see, there are many benefits to using custom AI-powered sentiment analysis. You can build a unique and tailored system to satisfy your complex inquiries and give you a competitive edge over businesses that use standard cloud-based solutions. A custom tool with on-premise or private cloud-based processing will offer better data protection and allow you to update the system whenever needed. It will require a higher initial investment, but you will spend less along the way, balancing your expenses and profit.
Who needs to consider custom development options?
We advise businesses with the following characteristics to opt for custom machine learning sentiment analysis solutions:
-
When data privacy is of the essence, like in the healthcare sector
-
When you would have to submit too many queries and cloud-based solutions would be too expensive
-
When you want to process rather complex cases with a custom scoring system, as you will see below
Before you start implementing AI-driven sentiment analysis
Here are three steps that will help you prepare for AI sentiment analysis implementation.
Step 1: Formulate your strategy
-
Define how you will use the results of the analysis. What business problems do you want to solve, and how do you expect this tool to help?
-
Develop metrics to measure the solution’s success
-
Think of the languages you wish to include in your analysis
Step 2: Prepare your training dataset (only for auto ML and custom AI-driven sentiment analysis solutions)
-
Gather as much data as you can
-
Clean the data. If you use any special symbols, eliminate them.
-
Annotate your training set, if possible (except for Gen AI algorithms)
For more information, refer to our recent guide on how to prepare data for machine learning models.
Step 3: Identify the desired capabilities for your algorithm
-
The level of detail
Is it enough for you to know the general sentiment (positive, neutral, or negative), or do you want to dig deeper and understand what exactly your customers enjoy or despise about your product? For example, does it suffice to see that someone wasn’t happy with their stay at a hotel? Or do you want to know what exactly went wrong, like “the receptionist was rude” and “there was no coffee at breakfast?”
-
Different ways of saying the same thing
People naturally use different words to refer to the same thing. Coming back to the hotel example, to express the fact that the hotel room was dirty, one customer can say “the room was filthy,” while another would use different terminology, saying, “the room looked like a dump.” So, there are not only synonyms for a particular word but also fully different ways of expressing the same sentiment.
-
Typos
Many people make typos in their writing. How do you want your algorithm to handle that?
-
Sarcasm
Sarcastic sentences express negative sentiment using positive words. In the example below, a positive tone is used, but the sentiment towards the laptop bag is clearly negative:
“This is the best laptop bag ever. It is so good that within two months of use, it is worthy of being used as a grocery bag.”
-
The way you assign scores
How do you want to calculate the final sentiment score? Take the following restaurant review as an example:
“Tuna salad was fresh and delicious, but the dining room was dark and tiny.”
The sentiment towards the salad is positive, but it’s negative towards the dining space. What is the final score here? Is it neutral (0), as the negative cancels out the positive? Or do you want your algorithm to break this sentence down into two categories, food and space, and score it (+1) for food and (-1) for space separately?
On a final note
AI sentiment analysis tools have many benefits. They can help you gauge what your customers are feeling towards your product, thereby allowing you to improve your offering. You can also analyze the sentiment towards your competition to repeat their success and avoid their mistakes.
However, incorporating AI-based sentiment analysis into your business is a challenge. We at ITRex can assist you in various ways. If you opt for cloud-based sentiment analysis, we can help you adapt your system to the cloud vendor’s API. If you choose the auto ML option, we can assist you with dataset preparation and training and with the vendor’s API integration.
We will be most helpful if you decide to build a custom AI sentiment analysis tool. Our team will allocate ML engineers and work with you on data collection, cleaning, and annotation (if needed). We will build and train a model based on your own datasets and corresponding to your specific needs. Furthermore, we will develop an API and integrate it into your back end seamlessly. Finally, you can count on our support in case of malfunctioning, the need to scale, or incorporating updates.
