AI sentiment analysis, also known as opinion mining, uses artificial intelligence and its subtypes to detect emotions embedded in text, audio, or video formats. By applying a predetermined metric, it can detect whether a piece of text has a positive, neutral, or negative sentiment. This technology enables businesses to detect negative feedback about their products and services and respond in real time.
According to Research and Markets, the global sentiment analysis market was valued at $3.5 billion in 2022 and it’s predicted to almost triple and reach $10.1 billion by 2030, growing at a CAGR of 14.2%. And this trend will continue as companies refer to AI service providers to gauge customer sentiment.
Do you want to learn more about how AI-powered sentiment analysis can improve your business? Keep reading.
Which benefits can you gain from AI in sentiment analysis?
You can deploy this technology to spot patterns that would be hard to detect with a human eye. For instance, sentiment analysis can easily pinpoint hateful remarks and cyberbullying on your online platform, or identify a customer who is frustrated and is contemplating leaving for a competitor.
The major benefits of this technology include:
- Monitoring your brand’s image online. It can follow up on brand mentions and detect negative reviews as soon as they occur. By monitoring a brand’s reputation over time, businesses can develop usable statistics and information, anticipate outcomes, and stay proactive to gain a competitive edge.
- Analyzing competitors. The technology helps businesses spot positive mentions of their competitors’ marketing efforts, and adopt a similar approach.
- Gauging response to a product. AI-driven sentiment analysis allows managers to locate feedback on specific aspects of their product, such as interface and functionality, and make the required changes.
- Spotting dissatisfied employees. Artificial intelligence sentiment analysis can gather information on employees’ behavior by analyzing surveys and peer reviews. Managers then utilize this data to track demotivated staff members and launch initiatives to increase their productivity and reduce turnover.
AI sentiment analysis use cases in different sectors
Let’s take a look at the key sentiment analysis applications in various industries.
AI sentiment analysis in call centers
If you are managing a sales or support team, it’s tedious to monitor your personnel and keep track of what upsets the clients. AI-powered sentiment analysis can facilitate this task.
Take as an example DialPad. Headquartered in California, the firm offers a cloud-based business phone system that supports smarter calls and includes sentiment analysis and AI. This solution can analyze calls in real time, detect any negative or positive vibes, and notify the supervisor if needed. DialPad can also transcribe conversations.
In addition to detecting sentiment, this solution offers other interesting features. For instance, it allows call center agents to create real-time assist cards with tricky questions and trigger them by mentioning a keyword.
AI sentiment analysis in the governmental sector
The government can use this technology to mine citizens’ opinions on its services.
In one example, an Asian government ministry turned to a tech vendor to build a solution that would allow them to uncover the problems citizens face in passport offices, traffic jams, etc. The tech company built a tool that processed the government’s data, detected sentiments, and further segmented it into subclusters based on the comment’s nature, the citizen’s gender, and some other variables to get a more detailed view. This solution empowered the government to adjust faster in response to citizens’ complaints and to make better choices based on this analyzed historical data.
AI sentiment analysis in retail
A recent survey shows that 98% of people do read online reviews for their local businesses. So, it’s rather important for retailers to keep their hand on the pulse. Knowing what people think about your brand allows you not only to address any existing issues but also to base your business decisions on people’s needs.
Starbucks uses the technology to understand clients’ needs, address any poor experience they had with the firm, and formulate future strategies and marketing campaigns. Based on customer feedback, Starbucks can make business decisions, such as where to open its next store and what will this year’s Christmas cup in different cities be. This is even more impressive given that 10 new tweets are published each second regarding Starbucks on average.
AI sentiment analysis in education
Since recently, AI-powered sentiment analysis techniques are becoming popular in education. One of the key applications is reviewing comments students post on massive open online courses (MOOC). This enables instructors to reevaluate and enhance their content when needed.
One example of this endeavor comes from Beihang University in China, where researchers used deep learning algorithms to gauge learners’ sentiment regarding MOOC, identify any issues, and improve students’ outcome. In another experiment, scientists deployed ML to understand the impact of the COVID-19 pandemic on university students’ psychology.
How to implement AI-driven sentiment analysis?
If you are looking to enhance your business with AI sentiment analysis, there are three options:
- Pre-built cloud solutions. Ready-made and trained tools that you can access through an API without understanding how they operate.
- Automated machine learning tools. An ML consulting expert has their own algorithm, which is trained on the dataset that you provide.
- Tailored AI-based sentiment analysis. A solution that is custom-made for your needs and trained on your dataset.
It’s best to go for the third option when you plan on processing too many queries daily, which makes the cloud solution too pricy. Also, if you are worried about data privacy or have to implement a complex scoring system, then a custom AI tool is your best option.
And before you proceed with custom implementation, consider going through the following steps:
- Put forward a strategy, write down the results you want to achieve, and determine how you will evaluate success
- Aggregate, clean, and label the training dataset
- Define the requirements for your solution. This includes whether the tool can tolerate typos, how it can handle language-specific issues, like sarcasm and different ways of conveying the same information, etc.
- Determine how the algorithm will calculate and assign sentiment scores
To sum up
AI sentiment analysis can help you better understand both your clients and employees and anticipate their needs.
Clearly, you will need a reliable tech partner if you pick the custom sentiment analysis option, as they will build the algorithm, help you prepare the training dataset, and integrate the final solution into your workflows. However, it’s still recommended to contact a tech vendor even if you select the cloud-based option, as you might need assistance adapting your system to the cloud vendor’s API.