Sentiment Analysis: Comprehensive Guide on NLP

In this post, you will learn the comprehensive guide on Sentiment Analysis to solve business decisions easily with different examples.

In order to understand what sentiment analysis can do for your business, You first need to have a solid understanding of exactly what it is and how it works.

Once you do, you’ll be ready to take advantage of the ways in which this interesting approach to NLP can improve your overall business operations and even potentially help you make more money.

Related Article: What Is Natural Language Processing? | Used of NLP

What is Sentiment analysis?

It is a type of text analytics that examines a body of text and assesses its overall sentiment (the author’s attitude or emotional state) or its overall opinion (what the author believes about something).

It helps measure market sentiments, customer satisfaction, and many other forms of social media activity.

Sentiment analysis is generally used to predict customer trends, both positive and negative.

In marketing, It can help brands monitor what people are saying about them on social media.

If there are any negative comments, companies may be able to address these issues before they spiral out of control.

Additionally, sentiment analysis may also be useful in identifying word choices that should be included in future content creation processes.

No matter what product, service, or idea we are talking about, at some point someone will have an opinion about it, and as a business that can be both an advantage and a disadvantage.

You may want to investigate customer satisfaction or find out how much people love the latest online release of your product.

How does it Works?

It has been around for decades, and in recent years, several companies have applied sentiment analysis to business intelligence.

Rather than relying on surveys and social media networks, sentiment analysis uses natural language processing to determine what consumers are saying about a company’s products or services.

It is about identifying and categorizing opinions, Sentiments can be positive or negative and there are various ways to measure them.

You can use a scale that ranges from extremely negative (-5) to extremely positive (5).

If your customers talk about you or your products in a positive way on social media, blogs, and forums then they are expressing a positive sentiment towards you.

But if they write negatively, they might not be as happy with your brand as you think and That is where sentiment analysis is required.

It helps businesses understand customer opinion which ultimately helps in making strategic decisions regarding business operations.

Where is Sentiment Analysis Useful?

It can be useful in many ways, some of which include: determining consumer and employee opinions, measuring the effectiveness of marketing campaigns, and product development.

These tools are beneficial to both large and small companies, Often larger corporations use these techniques to monitor brand image while smaller business owners use these techniques as a gauge of customer satisfaction.

While sentiment analysis may seem like an easy way to obtain information, there is more involved than just typing words into the software.

There is a significant amount of preprocessing involved before any machine learning techniques are applied.

To obtain more accurate results care must be taken to determine what should be used as a relevant text and what should not be included during processing such as emoticons or profanity.

Why Busineses need Sentiment Analysis tools?

There are different sentiment analysis tools available in the market and These tools will provide you with a better understanding of your customer base.

With such tools, you’ll be able to gain insight into what your customers are thinking and feeling about your products or services.

By providing real-time insights into a customer’s feelings and emotions you’ll be able to take these conclusions and use them to influence future decisions based on fact rather than assumption.

Simply put; without properly tracking your customer’s feelings/emotions, how can you ensure that they remain happy and continue using your product/service? This is an important question.

Why is Analysis of Sentiment so Important?

It is important to help businesses understand how their products or services are perceived by their customers and prospects. With high amounts of data being created every day, sentiment analysis enables business leaders to get a quick snapshot of what customers are saying online.

That information can be used to improve products, services, and marketing strategies. Furthermore, getting positive customer feedback on social media in real-time provides greater opportunities for customer service teams.

It also allows them to reach out quickly to retain happy customers who are likely to share good experiences with others.

Conversely, negative feedback gives organizations an opportunity to fix issues before they go viral on social media sites such as Twitter and Facebook.

The process of measuring opinions about a particular product or service falls under customer analytics and market research.

Types of Sentiment Analysis

There are two primary types of sentiment analysis, One is affective computing, or using software to analyze and interpret human emotions through facial expressions or tone of voice, The other type is textual sentiment analysis.

1. Effective Computing

Effective computing To identify emotion through facial expressions, an algorithm analyzes human reactions to images or videos of people expressing a range of emotions.

The tech then breaks down an individual’s response into multiple, measurable parts that can be used to determine how users feel about a specific product or service.

This technique is gaining popularity among business leaders who want to determine if their ads are having an emotional impact on consumers.

2. Textual Sentiment

Textual sentiment analysis uses keywords and phrases to understand how an individual feels about a particular product, service, or topic. It can also be used to gauge public opinion by gathering online reviews from websites like Yelp!, TripAdvisor, and Amazon. com.

Unlike effective computing, textual sentiment analysis doesn’t try to tell if you’re happy or sad; rather, it focuses on why you feel that way, what influences your opinion (for example price point), and where your sentiments fall on a range of possible feelings.

Different Usabiities of Sentiment Analysis

It is applicable to businesses in numerous ways, However, in order to use sentiment analysis effectively, you need to have a clear idea of what you want to get out of it.

For example, if you want your business to respond better and improve customer service as a result of emotion detection, then you will have a completely different plan than if your goal is just better product development.

This can lead to more efficient production processes, higher sales revenue, and even new product innovations!

Projects/tools for training/data collection: Depending on how much data you currently have available (i.e., Twitter posts or Facebook updates).

There are plenty of free tools that can be used with your already existing data to collect all relevant information needed in order to train an NLP algorithm.

They also provide sample code to assist you in building a sentiment analysis model, but if Python is more your speed they also offer a Python package called TextBlob which supports over 30 languages.

They also allow you to upload custom dictionaries so you don’t have to hand-code them yourself.

Different Challenges with Sentiment Analysis

Sentiment analysis comes with its fair share of challenges, Companies have to be careful about how they use and store their data in relation to sentiment analysis as most people are not comfortable with having their opinions collected and being used by companies.

This is especially sensitive when it comes to health, finances, or politics. Therefore, firms have taken a series of measures to ensure that they remain compliant with privacy regulations while still making effective use of sentiment analysis.

These measures include establishing policies regarding what information can be collected and stored, anonymous vs identified users’ sentiment analysis results, knowing where your customers can opt-out from future uses of your platform’s sentiment analysis algorithms.

Tips for Using Sentiment Analysis

While sentiment analysis can be a useful tool, there are some things to consider when determining whether or not it’s appropriate for your organization.

The ability to uncover unique insights into how customers feel about your brand is often worth any time invested in training an algorithm or manually analyzing results.

The key is to have an idea of what you want from sentiment analysis before getting started otherwise, results may seem ambiguous at best!

As with any new business process or technology, developing a basic understanding of sentiment analysis will ensure that you’re able to utilize these types of tools effectively and efficiently for long-term business growth.

There is no right or wrong way to implement sentiment analysis in your organization, but there are definitely ways to fail.

If you’re using sentiment analysis as a customer service tool or feedback gauge, be sure to have some sort of support strategy in place so that if customers have questions about results.

You can provide them with information on how those results were calculated and answer any other questions they may have. This will ensure that customers feel supported rather than ignored by your brand.

Text Classifier and NLP

As we mentioned earlier, a text classifier is an algorithm that can categorize unstructured data into predefined classes.

Let’s use textual analysis as a case study, At its core, sentiment analysis is an attempt to understand opinions about an entity by evaluating written content about that subject and assigning it to a category or groups of categories.

Let’s take Twitter feeds from companies such as Dell, Amazon, and Southwest Airlines. Each of these companies could have several hundred thousand tweets posted about them on a given day.

It would be impossible for humans to read all those tweets and classify them based on opinion toward those brands.

But, with NLP techniques and machine learning algorithms, we can create computer programs to accomplish just that.

In other words, we can feed in hundreds of thousands or even millions of pieces of online content (Tweets) and program our algorithm so it will perform sentiment analysis using Natural Language Processing techniques so that we can extract a large amount of useful information out of huge amounts of unstructured data quickly.

This is one example where sophisticated text classification algorithms help turn voluminous data into meaningful insights!

Related Article: Top 10 Natural Language Processing Tools | Text Analysis


Sentiment analysis is a powerful technology that can be used in many business situations.

Using data from sources like social media, a company can gain valuable insight into what their customers or clients think about them and how they could improve.

Whether you’re trying to enhance your customer service or brand perception, sentiment analysis is an excellent resource.

It’s important to keep in mind, however, that not all analytics are accurate and it’s important to remember that sentiment analysis isn’t 100% accurate.