Sentiment analysis is a technology that reads people's emotions and opinions from data such as text, audio, and facial expressions.
In recent years, sentiment analysis systems equipped with AI (Artificial Intelligence) have increased, making higher performance and speedier analysis possible than conventional methods.
In addition to basics such as the mechanism and methods of sentiment analysis, this article comprehensively explains the benefits of utilizing it in business, actual cases, and precautions for implementation.
The content is designed to help you understand the value of implementing sentiment analysis.
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Sentiment analysis is a technology that uses AI to analyze emotions and changes in feelings, such as joy, anger, sorrow, and pleasure, from data like text, audio, and facial expressions.
With the progress of AI technology, it has become possible to process vast amounts of data efficiently, leading to improved precision in sentiment analysis technology. As accuracy has improved, it is being applied across a wide range of fields, including marketing, customer support, healthcare, education, and entertainment.
Sentiment analysis methods are broadly divided into two categories: "Rule-based approaches" and "AI-based approaches."
A rule-based approach is a method of estimating emotions from specific words or expressions based on pre-defined rules. For example, text is evaluated using a list of positive and negative words.
Rule-based approaches are effective for detecting keywords representing specific emotions. However, they struggle with reading nuance and context. Thus, there are limits to understanding situations where emotions are complexly intertwined or when sarcasm is expressed.
On the other hand, in an AI-based approach, specialized AI in each field learns from various types of data as shown below. This method identifies patterns and regularities in data to estimate emotions.
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AI-based approaches improve in accuracy as the amount of data increases, making it possible to analyze a variety of emotions quickly.
Since both rule-based and AI-based approaches have their own benefits and challenges, it is necessary to choose the appropriate method according to the data used and the required accuracy.
The mechanism of sentiment analysis differs depending on the data being analyzed. Below, we introduce the mechanisms divided into audio, text data, facial expressions, and biometric information.
Sentiment analysis using audio data focuses on the characteristics of speech without depending on a specific language. It is a mechanism that recognizes emotions by analyzing features such as "vocal inflection" and "volume."
For example, even with the same words, a bright tone of voice is inferred as a positive emotion, while a low, slow tone is inferred as a negative emotion.
Sentiment analysis using audio data allows for the analysis of a wide range of emotional data across countries and cultures. Therefore, its utilization is particularly progressing in customer support and call centers.
Sentiment analysis using text data primarily uses natural language processing technology. It is a mechanism where AI reads words and phrases within input sentences (text) and judges emotions by analyzing the content.
For example, AI reads product reviews or social media posts and detects words like "like" or "complaint" to judge the posters' emotional tendencies.
It is widely used in customer feedback analysis and social media analysis, helping companies understand user needs and opinions.
Sentiment analysis using facial expressions works by reading emotions from the movement of facial expressions, much like humans do in daily communication. AI analyzes minute expression movements from image or video data, such as facial muscle movements, eye movements, and changes in the mouth.
Facial expression-based sentiment analysis can be performed in real-time by combining cameras and facial recognition software. It is particularly useful for analyzing customer satisfaction in the entertainment and service industries.
Sentiment analysis from biometric information is a mechanism that uses sensors to measure physiological reactions such as pulse, perspiration, and brain waves, and estimates emotions based on that data.
For example, many people experience an increased heart rate and sweaty palms when feeling nervous or stressed. By collecting this data with sensors, emotions such as "tension" or "anxiety" can be inferred. There is also technology that uses brainwave sensors to judge emotional states from brain activity patterns.
Biometric information allows for the identification of signs that are difficult to see from outward appearances like faces or voices. Therefore, it is attracting attention as a means for real-time monitoring of patient stress levels and mental states, particularly in the medical field.
Sentiment analysis AI brings significant benefits to corporate marketing activities and workplace environments. Below are the specific benefits that sentiment analysis AI brings to companies.
Sentiment analysis AI contributes to improving user satisfaction during inquiries.
For example, by analyzing call content and customer facial expressions in a call center in real-time, operators can grasp the customer's emotional state and respond more appropriately. If the AI detects that a customer's tone has lowered or that words indicating dissatisfaction have increased in a chat, prompt follow-up can be performed.
In this way, sentiment analysis AI not only increases the speed of troubleshooting but also leads to building good relationships with customers.
By using sentiment analysis AI to analyze consumer emotions and opinions from social media and review site posts, product development and marketing based on customer needs and requests become possible.
Additionally, sentiment analysis by AI is gaining attention as a means to visualize the relationship between specific emotions and consumer behavior. If companies can grasp what kind of emotional shifts lead consumers to take actions like purchasing, it can be used to improve marketing and sales activities.
Sentiment analysis AI also contributes greatly to improving the work environment. By monitoring employee emotions, signs of stress or dissatisfaction in daily operations can be detected early, allowing for quick countermeasures.
For example, by analyzing emotional tendencies from regular surveys or chat contents, departments or tasks where fatigue and dissatisfaction are accumulating can be identified. This makes it easier for managers and HR to provide accurate support and improvement plans while protecting privacy.
Utilizing sentiment analysis AI makes it easier to improve employee satisfaction and provide psychological care, leading to reduced turnover rates and improved workplace motivation.
Sentiment analysis systems are actually being utilized for corporate marketing activities and service improvements, and their effectiveness has been confirmed. Below are some use cases for sentiment analysis systems.
Asahi Quality & Innovation, a part of the Asahi Group, conducted empirical research regarding the selection behavior of beer consumers using sentiment analysis tools.
Conventionally, beer product development was carried out based on preferences and opinions voiced by tasters or written in surveys. However, there are many cases where these do not match what consumers actually think or the reactions they show subconsciously.
Therefore, an attempt was made to measure the facial expressions of consumers in real-time during beer tasting using a sentiment analysis tool to grasp reactions to products.
The empirical research suggested that the "lip-pressing" expression made by consumers after tasting is an effective indicator for predicting beer selection.
In the future, by analyzing more expressions with sentiment analysis tools, it is expected that the subconscious and essential reactions of consumers toward products can be understood.
Reference: https://www.affectiva.jp/affectiva-topics/aqi
KDDI developed "facial expression recognition AI technology" that can recognize facial expressions even while wearing a mask. When a mask is worn, about 70% of the face is covered, making it difficult to grasp the movement of the lower half of the face, such as the lips, corners of the mouth, and cheeks, which are important for conventional facial recognition technology.
To overcome this challenge, KDDI prepared more than 50 types of masks and incorporated hundreds of images of people wearing masks into the AI training data. At the beginning of development, the recognition accuracy of this technology was about 60%, but by expanding the data, it ultimately achieved an accuracy of over 90%.
In the future, it is expected to be used as a tool to check the reactions of visitors at zoos, aquariums, amusement parks, etc. It is also expected to be useful for visualizing the reactions of participants wearing masks in online meetings.
Reference: https://time-space.kddi.com/au-kddi/20210611/3122.html
Benesse Corporation introduced a system utilizing facial expression and emotion recognition AI for "Online Speaking," an English option material for its correspondence education program "Shinken Seminar."
This material is aimed at children from preschool to high school, many of whom are having English conversations with foreigners for the first time. Therefore, the tutor's atmosphere and manner of interaction significantly affect learning effectiveness and satisfaction.
Thus, with the goal of improving tutors' instructional techniques, Benesse incorporated facial expression and emotion recognition AI that can analyze tutors' expressions and gestures in real-time.
In this system, elements such as "smiling," which are particularly important indicators for lessons for children, are standardized, and AI analyzes all lesson footage in real-time. Once the lesson is over, a report is sent showing the frequency of the tutor's own expressions and gestures.
Through facial expression and emotion recognition AI, the goal is to improve lesson quality by allowing tutors to check their own teaching style as appropriate and improve their interactions with children.
Reference: https://www.affectiva.jp/affectiva-topics/bennese_ks4c
When utilizing sentiment analysis AI, there are several precautions regarding data and model development. Below are the precautions when utilizing sentiment analysis AI.
Since sentiment analysis handles personal data, sufficient consideration for privacy protection is necessary during data collection and use.
The data acquired by sentiment analysis AI often includes sensitive information related to individual emotions and behavior, and there is a risk of privacy infringement if used inappropriately.
Therefore, when implementing sentiment analysis, strict management is required in the collection, storage, and use of data. Examples include data anonymization and clear policies for using collected data only within the necessary scope.
In the handling of data used for sentiment analysis, it is necessary to ensure thorough data privacy protection under laws and regulations.
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Things to keep in mind when requesting annotation data collection
To improve the precision of sentiment analysis AI, ensuring the quality of training data is indispensable. Emotional shifts involve complex interactions between a person's native culture, environment, education, and the situation during photography. Thus, even muscle movements that look the same can sometimes contain completely opposite emotions.
Therefore, it is important to have the AI understand a broader range of emotions and expressions by training it with diverse data.
Also, the quality of data annotation is important as it directly affects model precision. If annotation is inaccurate or ambiguous, the AI will learn incorrect patterns, resulting in low analysis precision. Therefore, work by annotators with specialized knowledge is recommended.
Sentiment analysis AI is not perfect, and misrecognition may occur. In particular, expressions that depend on context, such as irony and jokes, are difficult to understand accurately even for humans, often leading to incorrect analysis results.
To prevent such misrecognition, "multi-modal analysis," which analyzes emotions by combining text data, audio, images, and biometric information, is effective. By utilizing multiple data sources, more accurate sentiment analysis becomes possible, reducing the risk of misrecognition.
Furthermore, to maintain and improve the precision of sentiment analysis AI, regular maintenance and updates of training data are indispensable. As new phrasing and expressions increase, neglecting data updates risks the AI's judgment becoming outdated or increasing the rate of misrecognition.
Building models specialized for target industries or fields is one way to utilize sentiment analysis AI effectively. Industry-specific models can accurately understand industry-specific expressions and technical terms that general sentiment analysis models cannot capture, and they can take into account cultural elements and subtle nuances.
In particular, in highly specialized fields such as healthcare and finance, many unique words and expressions are used, and general models tend to have difficulty with accurate analysis.
As a countermeasure to specialize for an industry, involving industry experts in the annotation process is effective. By utilizing the knowledge of experts, industry-specific expressions and context can be appropriately understood, improving interpretation precision.
For companies, quickly grasping the emotions of customers and employees and taking appropriate actions contributes greatly to improving customer satisfaction and the work environment.
Therefore, by utilizing sentiment analysis AI, changes in customer or employee emotions can be captured in real-time, enabling early detection of troubles and improvement of service quality.
Sentiment analysis AI will become increasingly important as a powerful tool for companies to build relationships of trust with customers and employees and to create better service delivery and workplace environments.