Business owners increasingly want to know more detailed information about their clients: understand their preferences, social status, or even mood. Over the past decades, the methods of collecting this kind of information have changed dramatically. What used to be done manually through a phone call or service center can now be successfully automated.
What Is Customer Sentiment Analysis?
With the help of customer sentiment analysis, organizations can learn about their weaknesses, improve their services or establish more effective communication with clients. All this will lead to an increase in the number of customers and an increase in income.
Consequently, semantic analysis is an automated process that studies customer sentiment by analyzing their feedback or comments.
Types of sentiment analysis
Let’s take a closer look at the different types of semantic analysis.
Sentimental models are generally classified by polarity, urgency, emotionality, and intentions. Along these lines, when analyzing the polarity, we determine how the customer is disposed of (negatively, positively, or neutrally). However, such a characteristic often falls within the limits of emotions, where there is already a wider range of feelings (anger, joy, sadness, etc). In addition, the model can classify the result even by urgency or intention, revealing whether the customer is interested or not interested in the purchase. A variety of technologies are used for sentiment recognition. They may include biometric data, text analysis, natural language processing, or artificial intelligence. Different information requires different analysis models.
Fine-Grained Model. The Philosophy of Duality
Such a model typically distinguishes mood according to 5 different polarity categories – very negative, negative, neutral, positive, and very positive. However, the formulation of human emotions or feelings works as an ideal mechanism that can be expressed in exclusive categories.
Aspect-Based Model. Subjects, Notions, and Reviews Are Detected
The aspect-based analysis is useful in that it helps identify specific topics that people are discussing. It is most often used to analyze consumer feedback. The model analyzes our feedback, such as “difficult to use” or “easy product integration”. Based on such phrases it can extract our mood (positive or negative) and, for example, the category in question. In plain words, the model analyzes what customers feel and why.
Sentiment analysis can help companies automatically sort and analyze customer data, automate processes like customer support tasks, and get powerful insights on the go. Aspect analysis of feelings extracts the characteristics of the subject from the division of large data into blocks. The model evaluates a set of reviews about the product, highlighting the character of the subject and the phrases that are related to this characteristic. In this way, the analysis makes a general conclusion about the customer’s feedback.
The advantage of such an algorithm is also the combination of similar aspects of a product or service. In this way, it is possible to determine how important features are for a particular industry. For example, the combination of the words “room” and “air conditioner” is often found, and therefore are important functions for the hotel industry. From this, the overall assessment of the hotel or any other area is formed.
Expression of Emotions
The model reveals such aspects of emotions as sadness, joy, anger, disappointment, sadness, happiness, etc. With the help of machine learning algorithms, it’s possible to hide the inaccuracy and ambiguity of the natural lexicon. For example, people often use oxymorons to add emotion to their comments, but machine learning algorithms can take this into account to produce accurate results of human emotions.
Analysis of the consumer’s intentions is no less important than the analysis of their emotions.
The model will distinguish emotions in this sentence as follows:
- intent = “buy”
- assigned object = “iPhone”
- intended = “I”
Just like said Jeff Catlin: “Intent is kind of a sexual trait, but the grammar analyzer is the key that makes it work, the ability to understand what people are talking about, regardless of the type of content. We built Grammar, a parser for Twitter dealing with bad punctuation, weird capitalization, etc.”
One of the developments in banking sentiment analysis was to develop a model to find out whether its customers intend to stay with their bank or switch to another.
How to Do Customer Sentiment Analysis
Analyzing customer sentiment manually is a long and tedious process that yields inaccurate results. Most companies switched to an automated system a long time ago. Therefore, let’s analyze how sentiment analysis works and how to put it into practice.
Where to Find Information About Customer Searches
In order to effectively implement sentiment analysis in your service, it is worth working with customer reviews, support conversations, micro surveys, live chats, or social media comments. All of this adds up to actionable, but unfiltered data that needs to be prepared for analysis. You should take into account grammatical errors, typos, relevancy, meaning, and other criteria. All this is a long and slow process, which can be automated with the help of various software.
Top 9 Sentiment Monitoring Tools
Having figured out exactly how you can get an analysis of your customers’ Sentiment and what you need, you can use tools that automate all this work. Different software can collect different data for you, but the functionality of these APIs is truly impressive. Let’s take a closer look at the leaders in the tool that provide quality sentiment analysis.
There are many services with different functionality, languages, data, and analysis systems that will provide you with information about the sentiment of your customers. All you need to do is choose the program you will use and start understanding your customers better now.
Why is Customer Sentiment Analysis So Important?
Use Your Advantages and Anticipate Their Implementation
Every client wants to apply for services and receive only a positive experience. That is why it is very important to understand exactly what your client likes, to develop your services in this direction, and to understand where the shortcomings of other services are. Sentiment analysis works precisely for this purpose. It provides information thanks to which you can achieve informational support for your client and prevent the situation from worsening. You will be able to understand the reasons and factors that contribute to negative customer experiences so that you can avoid mistakes in the future.
Create a Comfortable Environment For Your Customers
Your customers want you to understand their moods and help them when difficulties arise. Here, it is important to make quick decisions so that they do not leave negative feedback on your platform or social networks. Sentiment analysis works in such a way that you can predict the reactions of customers and make decisions that satisfy them. At the moment, buyers have access to many communication channels, including both emails and support. All these channels contain a lot of information that needs to be evaluated and processed. Sentiment analysis can work with this data to help you solve the problems your customers face more quickly.
“Listen” to Your Customer
Voice of the customer is a method that uses feedback analysis implemented to improve your product. This is done by a feedback system with the help of machine learning algorithms and artificial intelligence, which together form the Customer Sentiment Analysis. Implemented systems will help identify the number of repeated phrases by implementing text analytics using API. Accordingly, it is aimed at improving turnover, strategy, and services. It is worth conducting VOC analysis regularly in order to understand how and where to eliminate deficiencies.
Keep the Customer Loyal To Your Service
A sentiment score works as a signal that something about your service is not satisfying customers. These signals may indicate some service failures, which drives a person back from cooperating with you. Sentiment algorithms can provide you with statistics on the outflow or inflow of your customers.
Promoting Your Services And Increasing Customer Loyalty
For communication services, it is very important to understand the mood of the client and catch all the emotional signals recorded in their comments, requests, or calls. Possessing such information and implementing machine learning algorithms, can increase customer loyalty to your company. After all, it is important for everyone to be heard and understand the personal attention to the service. It can also work great as a way to promote your own brand.
Pay Attention To Each Customer
Amazinum Customer Sentiment Analysis: Use Cases
We already understand how sentiment analysis works and figured out why it is needed and how it affects the following service. Now we can consider real use cases that the Amazium company encountered.
In the Retail Sector
- Goal: the ability to analyze the customers’ satisfaction with their purchases and customer support services.
- Solution: The developed model searches for semantic information and pulls it from social networks, news, video content, and comments. After that – analyzes all the information and predicts specific patterns, using semantic clustering. At the same time, it is also based on a number of factors such as the language, age, and location of the user.
- Outcome: Understanding customer behavior and their response to changing trends.
Sentiment Analysis of employee satisfaction
- Goal: to understand how motivated employees are and how it affects their productivity.
- Solution: The software that automates the employee survey process is based on machine learning and sentiment analysis. After that, the Tool can extract information from social networks, questionnaires, or monitoring in real-time, determine the general mood of all responses, and group them by departments, and keywords. Such analysis should be carried out more often, and then the team will compare the current results with the previous ones.
- Outcome: Human resource managers receive information that can later affect the work of the entire company. With the help of software, employees can improve their work environment or say goodbye to an unmotivated specialist.
In the Hotel and Restaurant Business
- Goal: Provide personalized services to clients and be a leader among competitors
- Outcome: For business owners – an overview of disadvantages and advantages, a better understanding of their clients, and, accordingly, an increase in income. For customers – an objective assessment of a hotel or restaurant that will suit you according to all criteria.
How to Develop a Customer Sentiment Analysis Model
In practice, the analysis of superstructures is built using machine learning algorithms and NLP. At the same time, there are different ways of training the model, depending on the result you want to achieve. Therefore, we will analyze several ways of developing sentiment analysis and you can choose the one that suits you the most. A model developed on the basis of machine learning will be able to create patterns from the information you give it and predict the mood of the text.
So, we will talk about the following methods of training the model:
- Automated model
- Dictionary model
- Hybrid model
- Analysis of named entities
- Automated supervised model
The large model, accordingly, contains already 24 levels, with 1024 hidden, 16 headers, and 340 MP parameters in the neural network architecture. So, to train the model, you need to go through the following steps:
- Configure simple parameters and hyperparameters
- Develop forecasts for the future
- Implement the developed result
Analysis of Named Entities
Automated Supervised Model
This kind of model works on the basis of machine learning or deep learning algorithms, which use already labeled data sets to classify them and predict the results. Such a set should be output by tags based on the input data. In this way, the model can understand what it needs to focus on among the unseen data.
Therefore, the data set you have labeled is key to training the model to produce accurate results. The model will receive different patterns of data in the text and be able to predict sentiments for the text you provide.
To use this dictionary, you need to create a function that can analyze the text and classify it as positive or negative. The overall score is formed from the number of negative and positive words, so the final score is divided by the number of words in the text so that the score is normalized.
If the assessment of positive mood occurs in the range from 0 to 1, then 1 means 100 percent positive mood. If the assessment of negative mood occurs between numbers from 0 to -1, then -1 means negative mood with 100 percent probability.
Such a model relies only on Machine Learning and Artificial Intelligence algorithms. The created set of text data classified as neutral, negative, or positive is placed in the model for training. The algorithm analyzes and studies the data until it correctly evaluates the unfamiliar text. However, in this method, the set of data with which you train the model is important because it will not be able to work with unfamiliar data. Such an algorithm works better than a semi-automatic one but may contain inaccuracies regarding the classification of the text as negative or positive.
There is also a method that uses both an automatic model and a rule-based model.