Sentiment Analysis NLP

In this article, we will write about Sentiment analysis NLP. We will cover all the basics, including what it is, how to use it, and more. Also, we will provide you with sentiment analysis examples and tools. Thus, you will learn everything there is to know about this topic. So, without further ado, let's begin.

Introduction

Sentiment analysis NLP can help companies in many ways. For example, it can help companies understand how customers feel about their products and services. In other words, sentiment analysis can help companies understand which products or services customers like more and which ones they don't like. So, it can help companies improve their products or services. Also, it can help companies increase sales.

Of course, you can go through customer reviews and surveys manually, but that takes a long time to do. You can save time and learn more about your customers with sentiment analysis.

Analyzing customer reviews and opinions also comes down to human emotion and bias. Namely, a person reading a review can be biased and read into it a lot more than he needs. However, since sentiment analysis is a type of software, it will remove human bias. Thus, it will interpret reviews in a more objective way.

Sentiment analysis NLP can also process a large amount of data at once. So, companies will be able to take all of their customers' opinions into account at once, and not just a selected few. As we mentioned, sentiment analysis can help you save time. Namely, you can automate a sentiment analysis bot to analyze data on its own. That way, you can save even more time and focus on other tasks!

What Is Sentiment Analysis?

Sentiment analysis is a part of NLP (Natural Language Processing). It is also known as opinion mining. It measures and tries to understand people's opinions through NLP. Sentiment analysis can analyze information from social media, online news, and many other online sources. Also, it can analyze and assess people's emotions, beliefs, views, etc.

As we mentioned, you can use sentiment analysis to learn how people feel about your products and services. Namely, you can learn if they have positive or negative opinions of your products or services. Then, you can make changes to your products. Also, you can improve your products and services according to your customers' opinions.

Types

There are four types of sentiment analysis NLP that you can use. Those include:

• Graded - with this type of analysis, you can analyze your customer reviews based on whether their reviews are positive or negative. Namely, you can grade them as very positive, positive, neutral, negative, or very negative. You can use this to analyze five-star ratings. For example, if you get 5/5 stars on a rating, that result will be interpreted as "very positive." On the other hand, if you get 1/5 on a rating, that will mean that the review is "very negative."
• Emotion detection - with this analysis, you can detect emotions. For example, you can learn whether a customer is happy, angry, sad, or frustrated.
• Aspect-based analysis - this type of analysis can help you learn which product a customer is talking about in their review. Namely, it can understand what a customer is talking about without the help of a human. So, if, for example, a customer complains about the battery life of a product, the analysis will know and will tell you. That way, you'll know what you need to improve. Also, you won't waste time changing your entire product or service.
• Multilingual sentiment analysis - this type of sentiment analysis involves a lot of resources and preprocessing. Also, you will have to know how to code to use this kind of analysis.

Examples of Sentiment Analysis NLP

In this section of the article, we will write about some examples of sentiment analysis NLP. That way, you will be able to understand it more clearly.

First, we will write about some basic examples.

• "Hulu has the best movies." This review is easy to understand and analyze. The model will be able to classify this review as a positive one without any problems.
• "Disney+ has a great user interface." This statement is straightforward, and a sentiment analysis NLP model will be able to understand it. It will think that the statement is positive and add it to the other positive reviews your receive.
• "I like the colors they offer." This review is also simple to understand. Your sentiment analysis bot will have no trouble understanding that the customer is expressing a positive opinion. Also, it will be able to tell you which part of your product your customer does like.

Now let's look at some more challenging examples.

• "Not liking horror movies is not uncommon" - this sentence uses double negation and is more challenging to understand. Since it uses negation, the bot will see it as a negative comment when in reality, it is not.
• "I can't stand this show sometimes" - in this statement; a viewer is saying that they find a certain show unenjoyable sometimes. That does not mean that they do not like the show at all, only on occasion. However, a bot will see this as a negative comment and convey it to you as such.

Where Can You Use Sentiment Analysis NLP?

Now that we have seen what kind of data a sentiment analysis NLP bot works with let's explore some of its use cases.

1. The first use case we will write about is "Social media monitoring." Billions of people from all over the world use social media on a daily basis. That means that there are millions of posts and comments written every day. Going through each comment and post can take you a few days to complete. However, a sentiment analysis bot can sort through all of those posts and comments in minutes. Also, it can compile a list of all the comments where your company is mentioned. In addition, it can tell you whether the comments are mostly positive or not. That way, you will find out what people are saying about you and your company in no time.
2. You can also use sentiment analysis NLP to learn what people think about your brand. This is called "Brand Monitoring." You can also use aspect-based sentiment analysis to monitor what people think about your brand. Using aspect-based sentiment analysis will help you to see which parts of your brand people like and which they don't like.
3. Sentiment analysis NLP can also help you improve your customer support. All companies should provide good customer support. Good customer service can help you attract more customers. Also, it can make them stay loyal to your brand. Sentiment analysis NLP can make your customer support faster. Also, it can help you provide more effective support and solve more problems. Namely, it can help you you can find the most dissatisfied customers or the most urgent issues and make them a priority above the rest. Also, you can route tickets to the appropriate person or team in charge of dealing with them.

How to Use Sentiment Analysis?

As we mentioned, sentiment analysis uses machine learning and natural language processing (NLP) to operate. It uses them to learn whether a text is positive, neutral, or negative. There are two main approaches to sentiment analysis. Those are rule-based and automated sentiment analyses.

Rule-Based Approach

Using rule-based sentiment analysis involves four steps:

1. The first step is creating lists of positive and negative words. Those lists are also called lexicons. For example, positive lexicons include words like affordable, fast, simple, etc. Negative lexicons can include words like complicated, slow, expensive, etc.

2. The second step involves formatting the text in a way that a machine can understand. You can format the text by using several different methods. Those methods include tokenization, lemmatization, removing stopwords, and more.

3. During the third step, your computer will count the number of positive or negative words in a text.

4. The fourth step involves calculating the total sentiment score for a text.

Those are the four steps you need to complete if you want to use rule-based sentiment analysis. Completing those four steps will allow you to find out how people feel about your products and services. Since we covered the first approach to sentiment analysis, let's move on to the next one.

Automated Approach

Using automated sentiment analysis involves three steps:

1. The first step involves preparing the text so that a computer can read it. It is similar to the first step used in the rule-based analysis approach. Since we already covered that, we won't do it again here.

2. The second step involves testing. Namely, here you can test how your sentiment analysis NLP model works. That way, you can see whether it is reading the text correctly

3. The third and final step involves feeding new text into the model. That way, you can see whether it can understand new text or words that it hasn't seen before.

5 NLP sentiment analysis tools

There are many sentiment analysis NLP tools you can choose from and use. So, we will write about five sentiment analysis NLP tools that you can use, depending on which one you like best.

spaCy

"spaCy" is an open-source sentiment analysis package. It supports more than 60 languages. spaCy is built mainly in Python, which is one of the most popular programming languages out there. This tool is suitable for both beginners and advanced programmers. It offers helpful guides and other documents that can help you learn more about sentiment analysis and how to use it. You can install spaCy on Windows, Linux, and macOS devices.

Pattern

"Pattern" is a sentiment analysis package built mainly in Python. You can use "Pattern" to collect data via web scraping or integrating APIs. "Pattern" offers all kinds of tools to its users. These include data mining tools, Natural Language Processing tools, machine learning, network analysis, etc.

"Quick Search" by Talkwalker

"Quick Search" is a sentiment analysis tool made by Talkwalker, which is a platform powered by Artificial Intelligence. This tool works best with social media channels. It can tell you how people feel about your content. "Quick Search" can go through your comments, mentions, engagements, and other social media data. By going through your social media, "Quick Search" can create reports for you. Those reports can show you how customers are responding to your social media activity. That will help you plan and create effective marketing campaigns that your customers will like. Also, you will be able to engage your customers more with "Quick Search."

Repustate

"Repustate" has an excellent text-analysis API that can assess the emotions behind what people are writing on the Internet. It can understand how your customers feel about your products or services and write a report for you. With that report, you can learn what your customers feel. For example, you can learn whether your customers are satisfied with your products or not. "Repustate" can also analyze emojis and tell you if people use them in a negative or positive way within the context of a message.

Lexalytics

"Lexalytics" offers a text-analysis tool that can help you understand your customers better. With "Lexalytics," you can:

• Store and manage text documents;
• Analyze text with natural language processing;
• Build dashboards;
• Make reports;
• And more.

"Lexalytics" is not like all the other sentiment analysis tools. Namely, it tells you why customers feel the way that they do, instead of how they feel.

Conclusion

There are also many other great sentiment analysis tools out there. Also, many companies are developing their own tools that might prove to be even better than those on the market. One such company is Ideta which is a company that offers an excellent and easy-to-use chatbot solution. Also, Ideta is now in the process of creating its own sentiment analysis tool as well.