What is natural language processing and sentiment analysis? Detailed explanation of use cases and methods

Sentiment analysis is a useful way to quantitatively analyze the reviews of your products. This sentiment analysis can be analyzed from more data by utilizing the technology of natural language processing. In this article, we will explain the use cases and methods of sentiment analysis by natural language processing.

What is Sentiment Analysis of Natural Language Processing?

Here, we will give an overview of natural language processing and sentiment analysis.

What is natural language processing?

According to “Introduction to Natural Language Processing: 1. An Overview of the Current Situation and History”, “Natural Language Processing is an artificial language such as a programming language, such as Japanese, English, or Russian. It refers to the processing of the language that a person speaks or writes on a daily basis with a computer. ” There are many mechanisms that utilize natural language processing, such as AI assistants and search engines, that are deeply involved in our lives. Sentiment analysis is part of natural language processing.

What is sentiment analysis?

Sentiment analysis is the classification of given information into emotions. Information may include emotions such as negative, positive, and neutral. For example, SNS such as Twitter and Instagram often post impressions and reviews of specific products. Sentiment analysis is to analyze these sentences and what kind of opinion they have. Sentiment analysis can mainly analyze text, voice, and images.

Importance of sentiment analysis

Sentiment analysis plays an important role in the development of a company. Sentiment analysis can analyze what kind of evaluation your company’s corporate activities have received, so it can be used for future marketing activities. For example, if you can quantify your impressions and reviews of your company’s products and services on SNS and summarize them in the final report, you can make company decisions based on specific grounds. Sentiment analysis will lead to improved quality of corporate products and services.

Example of using sentiment analysis of natural language processing

Sentiment analysis of natural language processing has the following use cases.

• Analyze call center voice
• Visualize SNS information
• Analyze facial expressions from videos

Analyze call center voice

Sentiment analysis is used for call center support. Sentiment analysis is useful because it is difficult for operators to read changes in their feelings, such as how the customer on the phone is feeling now. When sentiment analysis reveals changes in customer sentiment, it becomes clear what to do. In particular, call centers often have manuals for dealing with customers, so it is important to respond according to the emotions of the other party. Therefore, sentiment analysis will improve the responsiveness of the call center.

Visualize SNS information

Various information by an unspecified number of people is posted on SNS. Some of them are malicious opinions or simply accusations, but there is also a lot of useful information for businesses. When investigating the opinions of users, it is effective to use SNS. Furthermore, by classifying SNS posts with the sentiment analysis function, you can efficiently collect opinions. Sentiment analysis will greatly help you make decisions about creating new services and improving services.

Analyze facial expressions from videos

Sentiment analysis can analyze facial expressions of people in videos in addition to audio and text. For example, in the smartphone app, there is a photo app that adds image effects according to facial expressions. In the app, if you make a crying face, it will be treated as if you are shedding tears.

Three Ways to Perform Sentiment Analysis in Natural Language Processing

There are three ways to perform sentiment analysis in natural language processing:

• Use an emotion dictionary
• Take advantage of deep learning
• Use existing APIs and services

Use an emotion dictionary

An emotion dictionary is a dictionary that corresponds to a word as positive or negative. For example, “early” is associated with positive, “slow” is associated with negative, and so on. Since the emotion dictionary has a simple structure, it is easy to create and the judgment itself can be done smoothly. On the other hand, if a word that is not in the dictionary appears, it is impossible to deal with it, and it is necessary to make rules for similar words.

Take advantage of deep learning

Deep learning is a technology that makes neural networks more efficient. A neural network is a model in which neurons, which are mathematical models inspired by neurons in the human cranial nerve system, are combined into a layered network. By enlarging and complicating this neural network, high performance is demonstrated in natural language processing tasks.

Use existing APIs and services

If it is difficult to implement by yourself, it is a good idea to use the existing APIs and services. You don’t have to implement APIs and services from scratch, you can easily incorporate them into your own services. Many APIs and services automatically analyze emotions when you enter a sentence.

There are hurdles in sentiment analysis of natural language processing

There are the following hurdles to perform sentiment analysis of natural language processing.

• Have specialized knowledge
• Security measures required
• There is a risk of personalization

Have specialized knowledge

Of course, specialized knowledge is required to implement from scratch. There are various types of knowledge, such as knowledge of programming languages ​​such as R and Python, knowledge of natural language processing, and knowledge of machine learning. It will take a considerable amount of time to acquire this knowledge.

Security measures required

When handling personal information in natural language processing, security measures are required to prevent information leakage. Many companies are suffering as cyber attacks are becoming more sophisticated. In some of the damaged companies, the customer information held within the company was leaked and social credibility was lost. In order not to be harmed, it is necessary to take security measures for the server to be used as well as security measures for programming.

There is a risk of personalization

Sentiment analysis requires difficult systems and skills, which can lead to individualized work depending on the individual. When dealing with difficult systems, it takes time to learn because it is necessary to learn from the operation method. If it takes time, it takes a lot of time for other employees to handle it, and as a result, it is easy for only a specific person to use it.

Smart problem solving with no-code AI cloud “UMWELT”

Companies can solve various problems by introducing AI, including sentiment analysis of natural language processing. However, since it is necessary to secure AI human resources in order to build it within the company, there are many cases where the introduction cannot proceed due to a shortage of human resources. Therefore, we recommend the no-code AI cloud “UMWELT”.

When introducing AI, it takes a lot of time because it is necessary to build the system infrastructure and implement AI. Since UMWELT is a cloud-based tool, it can be easily installed without the need to build an environment. UMWELT is supported by TRYETING consultants from before the introduction to the start of operation, so even those who are introducing AI tools for the first time can use it with confidence.

summary

Sentiment analysis of natural language processing is a technique that can be used for various tasks. However, many companies may not have implemented it because it requires specialized knowledge to build. In addition, I think many companies are considering the introduction of AI for the purpose of improving operational efficiency. UMWELT is no-code and anyone can easily improve work efficiency by AI. If you are thinking of introducing an AI system, please consider UMWELT.