It is not enough for business owners to have excellent products and services to present their company. It’s necessary to understand whether customers are positively or negatively think about the offered products. Such class of data analysis as sentiment analysis comes in. Its tools allow companies to improve their products and services, identify competitors' strengths and weaknesses, and create targeted advertising campaigns.
Sentiment analysis (also known as opinion mining or emotional AI) is a subset of natural language processing, which determines the polarity of emotional evaluations in text, using machine learning algorithms (hereinafter ML) and artificial intelligence (hereinafter AI).
Types of Sentiment Analysis
As already mentioned, opinion mining is based on the polarity of the text (positive, neutral, negative), however, it can also go beyond this polarity. Depending on how the enterprise wants to interpret customer reviews and requests, opinion analysis can reveal certain feelings and emotions, urgency and intentions.
We will explore the main types of sentiment analysis:
Assessment of Moods
It is used in cases where the accuracy of polarity is important for the organization. This one may include different levels of positive and negative: very positive, neutral, very negative, etc.
The polarity is usually expressed with a numerical rating, called «assessment of mood». For example, the rating may range from -10 to 10. In this event, number 0 corresponds to a neutral mood.
Intention-based Sentiment Analysis
This type discovers and understands customers' intentions towards brand, product, service or user experience. Tracking user behaviour on the Internet helps to create a template and run targeted ads campaigns.
Detecting Emotions
It allows to identify such emotions as joy, disappointment, anger and sadness. To detect these sentiments, systems use word and emotion lists, i.e., lexicons or ML algorithms.
Aspect based analysis
Key analysis is useful when tied to a specific attribute or function described in the text. ML algorithms are taught to analyze topics for pattern detection. Thus problems that customers report through social networks, reviews, online communities, or internal channels of communication with clients are identified.
What is opinion mining used for?
The feedback process is used by companies to categorize feedback in natural language.
Let’s consider a few specific examples of using this kind of analysis:
Emotional AI for customer support
Customer service specialists use mood analysis to automatically sort incoming emails of users split by «urgent» and «not urgent» categories, for instance. By identifying those who are not satisfied in advance, employees devote their time to solving the problems of clients in need.
Opinion mining for brand monitoring
One way to apply sentiment analysis is to form a full report on how your brand, product, and company are perceived by customers and stakeholders.
Mood analysis is used as well to measure the impact of a new product, advertising campaign, or consumer responses to the organization’s latest news on social media.
Sentiment analysis for market research
ДOpinion analysis is also a tool helping to understand the subjective reasons why customers do or do not react to something (for example, why consumers use one product, whether customer service provided their support, etc.).
Advantages of Sentiment Analysis
- Big Data sorting
Opinion analysis helps companies to process huge amounts of unstructured information efficiently and economically.
- Real-time analysis
Emotional AI identifies critical issues in real time. It helps to immediately identify disasters and crises so that organizations can take necessary measures and protect its image.
- Improved customer service
Sentiment analysis allows you to easily find and track previous messages from clients and quickly and efficiently solve their requests.
When a company regularly reviews customer feedback, it can anticipate new trends and eliminate problems. Sentiment analysis may give an organization an understanding of how users really feel about it.
At AZN Research, we use Microsoft cognitive text analysis solutions in many projects. For example, Customer Intelligence Suite (CIS) is a system that uses the latest AI and ML innovations to track data from all sources across the enterprise for adverse sentiment. This allows the company to take the necessary measures to eliminate them in a timely manner.