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While artificial intelligence (AI) cannot feel or express human emotion, it can detect emotion through sentiment analysis. This field of AI, also known as opinion mining or emotional artificial intelligence, has become an indispensable tool for businesses looking to gain valuable insights into how customers feel about their products and services. Sentiment analysis can be leveraged in virtually any industry where understanding human emotions are important. To date, it’s primarily being used to garner unspoken meaning or emotion from customer feedback, but sentiment analysis can be used in other ways.
Sentiment analysis uses natural language processing and text analysis techniques to identify and extract subjective information from source materials. At its core, it involves identifying the “polarity” of a given piece of text, or in other words, whether it expresses positive or negative sentiment. This information can include attitudes, opinions, and emotions of the author or speakers, as well as the overall tone and feeling of the text. It’s commonly used with text-based data like social media posts, reviews, surveys, and customer service tickets but can also be used for spoken word.
Sentiment analysis uses natural language processing (NLP) algorithms to process large amounts of text-based data and identify the attitudes or emotions behind them. The NLP algorithms are trained on datasets consisting of the labeled text so they can learn how to classify new data based on previously seen examples. Once the algorithm has been introduced, it can be applied to classify new texts into three categories: positive, negative, or neutral. Let’s put this in context.
Customer reviews are a common way to extract emotion. If, for example, a customer review for a restaurant said, “I had a terrible experience at the restaurant last night. The service was slow, and the food was cold. I will never go back again.” In this example, the sentiment analysis technology would extract the negative sentiment from the text by identifying words such as “terrible,” “slow,” “cold,” and “never,” which indicate that the customer had a negative experience. The analysis can also identify the emotions of anger and disappointment from the customer’s words and phrases, such as “I had a terrible experience” and “I will never go back again.” These emotions can give a more detailed understanding of the customer’s experience.
Sentiment analysis can also be applied to spoken words, such as speech or audio recordings. The process of extracting emotion from spoken words is similar to that of text, but it involves additional steps to convert the speech to text. This process is known as speech-to-text transcription. Once the speech has been transcribed to text, it can be analyzed using the same techniques used for text-based sentiment analysis. In addition to the text-based techniques, specific techniques can be used to extract emotion from spoken words, such as prosodic analysis, which examines the pitch, speed, and volume of speech, and phonetic analysis, which examines the way that words are pronounced.
Sentiment analysis enables companies to quickly analyze large amounts of text that would otherwise take hours or days for humans to read and interpret manually. For example, suppose you had hundreds of customer reviews about your product. In that case, sentiment analysis could help you determine which features people liked or disliked without manually reading each review. It could also allow you to identify areas where improvements can be made to better serve your customers. And the use cases don’t just apply to customer populations. Sentiment analysis has many applications, including social media monitoring and market research.
Companies have been using sentiment analysis for years to gain insights into consumer behavior and make better-informed decisions regarding product development, marketing campaigns, and customer service initiatives. For example, many companies use sentiment analysis to monitor online conversations about their brand to gauge public opinion on certain topics or products. This type of monitoring allows them to quickly address any negative feedback before it escalates into something bigger. Some other real-world use cases of the technology include the following:
As mentioned, sentiment analysis is not only limited to text-based data but also has a wide range of possibilities and potential in analyzing spoken words. At Quantilus, we constantly seek innovative ways to utilize technology to provide solutions to real-world problems. One such example is our work for B2 Ventures, where we were tasked to develop a multi-platform application called Mike that aims to assist individuals in overcoming their anxiety and challenges related to public speaking.
The core principle behind the app is that with consistent practice and feedback, users can improve their speaking skills in a virtual environment. By integrating AI, natural language processing, and sentiment analysis, the app is able to measure and score the user’s speaking performance. The sentiment analysis technology converts the user’s spoken words into text. Subsequently, it produces metrics such as pace, volume, intonation, and filler words, providing users with specific, constructive feedback. Moreover, users have the option to record their speeches in video format, allowing them to watch the playback in conjunction with the speech analysis.
In this use case, sentiment analysis technology provides access and opportunities for skill development to individuals from all walks of life. Whether the users have personal goals, are preparing for a job interview, or have a public speaking engagement, the technology is leveraged to enhance a human capability.
With its ability to quickly process vast volumes of text data from multiple sources and extract emotion, sentiment analysis is an invaluable tool for businesses looking to gain a competitive edge in today’s digital world. It also is a technology that can help individuals enhance their capabilities or better understand others, as is the case with assistive technology uses. Regardless of the application, it’s exciting to think of the possibilities with sentiment analysis.
Please contact us if you have a vision for a use case of sentiment analysis and need help bringing it to life. Quantilus can bring your idea to the next level and assist with a proof of concept or prototype.
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