How technology can capture human emotions
Alexa, Siri & Co. are the leading examples of voice assistants. The difference between them and simple applications is their use of sentiment analysis, which is used to determine linguistic and emotional contexts. The experts of the Reply Practice Voice Machine Interfaces make this technology available to clients so they can use it for deeper customer insights.
Intelligent voice recognition has existed since 1952. In this year, Bell Labs presented automatic digit recognition, which was able to recognise spoken numbers with great accuracy. Today voice recognition systems, such as chatbots, can do quite a good deal more and are used in numerous high-performance voice interfaces like Alexa and co., which are convenient and user-friendly. However, there are still issues at a certain point that are decisive for customer service or product reviews: It is referred to as “sentiment analysis” in voice technology communication. Voice assistants should be able to correctly recognise and interpret the mood or tone of what people say.
Sentiment analysis offers great added value for companies throughout every industry. Software can automatically evaluate written text or spoken content. Employees therefore no longer need to read through long, convoluted, or error-ridden text. Such applications save time and money, especially when the goal is to monitor social media or gather customer reviews and feedback on service.
Most applications deliver a relatively simple evaluation consisting of keywords and an appropriate probability calculation. It can be processed using algorithms, saved and used for other applications. For this purpose, both an emotional state on a polar scale (such as joy versus rage) and the respective probability are determined as a certain value between zero and one. For instance, the evaluation “Joy: 0.78456” indicates that the user has very probably made a happy, positive statement.
What is referred to as ontologies constitutes an additional level of complexity; ontologies recognise individual terms as a collection of properties that are conceptually connected with other terms. The statement “That was a total surprise!” illustrates such an ontology and can be easily understood: When used in relation to a film at the cinema, the statement would be positive. However, in the context of software application use, it would more likely be negative.
Using interfaces that are specially programmed for these contexts, such as those used for Amazon Alexa or Google Home, it is possible to convert these kinds of statements via speech-to-text and then evaluate them with sentiment analysis APIs. Applications like these are able to interpret the emotionality and polarity of statements.
However, one disadvantage is that voice assistants generally only “listen” for a few seconds when processing statements. That means they are not able to perform the kind of deeper analysis that would be possible with running text, for instance. Nevertheless, they are suitable for recording short recommendations or opinions.
Companies frequently use sentiment analysis for opinion mining, which is to say, opinion analysis. For online retailers or financial service providers, for instance, it is important to know what people are writing about performance, products or services in social media. Additionally, it is possible to gather opinions about what the target group wants or what mood consumers are in when they ring the call centre. The company can use the knowledge gained to improve the products or services or use the benefits of voice technology in marketing.