Sentiment analysis is the process of applying natural language processing and machine learning to determine the feeling of the users towards a brand, product, or service. Sentiment classification will help you tap right into customer feedback and position your company as an industry leader. 

“Sentiment analysis is a really handy tool which sweeps through the internet looking for mentions of your brand or product and, then, categorises the overall ‘feeling’ of what people are saying about you.  The categories are ‘Positive’, ‘Neutral’ and ‘Negative’ and, with some tools, you can dig deeper to get more insight into how people feel about your business in terms of satisfaction. With some tools, you can also read actual comments for a more ‘human-centric’ take on the results of your sentiment analysis. 

Sentiment analysis is important for us at Number For Live Person as it helps us to see how accurate our results are and to make improvements based on customer satisfaction.” Dima Suponau, Former Microsoft, CEO & Founder Number For Live Person 

Applying sentiment analysis across different departments will have a positive impact on your business bottom line. Sentiment analysis, also called data mining, can help you measure your PR and marketing campaigns, improve customer service, conduct market research, and develop a better product.

There are challenges when it comes to sentiment analysis, but it is worth the effort. 

How can you effectively apply a sentiment analysis system? How does sentiment analysis algorithms work? Here’s everything you need to know about sentiment analysis!

What is sentiment analysis?

Before we delve into the nitty-gritty of data science, machine learning techniques, and text analytics, let’s answer a simple question — what is sentiment analysis?

Sentiment analysis, also called opinion mining, is the process of text analytics that helps you understand the author’s emotions. Sentiment analysis tools classify online mentions as positive, negative, or neutral. 

Sentiment analysis provides you with qualitative data and helps better understand the true meaning behind the numbers. 

Your target audience expresses its thoughts and opinions online. Listening to what they have to say and analyzing survey responses or social media conversations will help you prepare products or services tailored to their needs. 

Automated sentiment analysis can identify problems you are not aware of. For example, are your customers happy with your packaging? Or the shipment company? 

They might leave some valuable feedback on social media channels you are not part of. Finding and analyzing these comments will help you stay one step ahead of your competitors and become an industry leader. 

How does sentiment analysis work?

Sentiment analysis applies various natural language processing techniques to analyze and classify online mentions. Moreover, the analysis uses machine learning to provide more accurate results over time. The text analytics get better with every analyzed result. 

There are three types of sentiment analysis algorithms:

– rule-based

– automatic

– hybrid

Rule-based sentiment analysis

A rule-based sentiment analysis applies predefined rules to online mentions. 

First, you create a list of expressions and words. If one of them was used in a sentence, the algorithm classifies the statement as positive, negative, or neutral. 

This approach to sentiment analysis gives you full power over the process. Based on the sentiment lexicons you have created, the tool will categorize the mentions exactly how you need it. 

There are several disadvantages to rule-based sentiment analysis. 

Since the analysis is based on positive and negative words, the system will have trouble with the analysis of different combinations of words. For example, the expression “pretty bad” or “not bad at all” (in british it means it’s actually really good) can be confusing, as it contains two words with opposite sentiment. 

Automated sentiment analysis

Data scientists do their best to improve the process of sentiment analysis. That is why they came up with automatic methods of sentiment analysis. 

Automated algorithms tag the mentions as positive, negative, or neutral. Deep learning ensures that the more data the system can crunch and categorize correctly, the more accurate the results will be in the future. 

In other words, thanks to data mining, sentiment analysis tools will be able to provide better results every day. 

Machine learning algorithms also ensure that a tool can correctly identify sarcasm or irony. Of course, the system is not perfect yet, but it is getting closer. 

Automated sentiment algorithms will also save you a lot of time. Instead of manually searching for mentions and setting up rules, a media monitoring tool will perform the process for you. You will get actionable insights at your fingers.

Hybrid sentiment analysis

Last but not least — there are hybrid approaches to sentiment analysis. In that case, you can manually assign the sentiment to the mention and help a machine algorithm get even more accurate results. 

Sentiment analysis tools

“Sentiment analysis is an online tool which helps businesses to snoop on customers and potential customers to see what people are saying about their brand.  Using keywords, sentiment analysis quickly collects comments made online and sorts them into positive, neutral and negative to give you an overall view of how people feel about your product or business. 

At UpperKey, we’ve been using sentiment analysis for some time now.  As a property management business, our reputation is everything and we work really hard to protect it.  Sentiment analysis helps us to do this by identifying negative comments and complaints and dealing with them quickly.” Johan Hajji, CEO & Founder at UpperKey

There are two ways you can implement sentiment analysis into your company — a sentiment analysis tool or an API.

Before you decide on any of them, you should examine your needs and your available resources. 

SaaS sentiment analysis tools

Sentiment analysis is one of the features of many media monitoring tools.

The premise behind the tool is simple — the tool collects all publicly available mentions containing your predefined keyword and thoroughly analyses the results. 

Media monitoring tools automatically assign positive, negative, or neutral sentiment to the collected mentions. 

This type of solution has many clear benefits. You can log into the dashboard and filter the mentions to examine only positive or negative statements. 

Moreover, you can correlate sentiment with other metrics, for example, the volume of mentions or influencer scores. That way, you can analyze the results in-depth and make more informed business decisions.

Most media monitoring tools operate in SaaS models, so you can use them only when you need to for instance Brand24 would be a good example. 

Sentiment analysis APIs

On the other side of the spectrum, you have sentiment analysis APIs.

There are many open-source libraries available, mainly in Python and JavaScript, as those languages are best for deep learning and advanced data analytics. 

If you have experienced engineers with data science and programming background, take a closer look at some of these Python libraries: 

– NLTK

– TensorFlow

– Scikit-learn

– PyTorch

If you are more interested in Javascript, take a look at these NLP libraries:

– OpenNLP

– Stanford CoreNLP

– Lingpipe

The obvious advantage of a customized sentiment solution is the ability to tailor it exactly to your needs. 

Of course, you need manpower to implement this solution. It will be expensive to build your sentiment analysis algorithm as you need your resources to do that. 

Why is sentiment analysis important?

Sentiment analysis is the cherry on the top of your overall brand analytics. You can implement the insights you get from sentiment analysis across your organization, starting with the customer experience team and ending with the IT team responsible for product development. 

What exactly are the benefits of sentiment analysis?

Brand monitoring 

Brand monitoring helps you manage and protect your brand image and reputation online. 

Brand reputation is one of the most important assets your company has. People buy from brands they trust. 

Analyzing the sentiment of online mentions about your brand will help you spot any surge in the number of negative mentions. You can prevent escalation of most crises only by reacting swiftly to unhappy consumers. 

But sentiment analysis in brand monitoring is much more than that. 

“Brand monitoring is a goldmine of information when it comes to information about your brand. The information from news sites, blogs, podcasts, forums, and various social media channels will help you craft the right message for the right audience” says Lukasz Zelezny, Search & Social UK Based Consultant.  

Understanding customer feelings is the context of your numerical data. Real-time sentiment analysis will help you understand how your brand position itself and how your image evolves. 

Crisis management

Unfortunately, you can try to prevent a crisis, but you will never be 100% immune to one. Sooner or later, a crisis will hit you, and you will have to switch into crisis management mode. 

Sentiment analysis will help you identify the root of the problem. Pinpoint the main problem, and you will be able to prevent the crisis from spreading. 

Besides, you will be able to monitor the response and lead through the crisis. Are you hitting the right tone? Is the response helping your brand? Monitor sentiment during the whole duration of the event and use the knowledge to prepare even better for the future.  

Product development

Your existing and potential customers are a goldmine of insights and feedback that can help you develop a product tailored to their needs. 

Adding sentiment analysis to the customer experience evaluation will help you pinpoint your strengths and weaknesses. You can show off your best qualities and increase the gap between you and your competitors. 

The same rule applies to your weaknesses. Your customers may complain about the issues you are not aware of. Identifying the problem at an early stage will help you provide a solution quickly. Listening to your customers will help you position your brand as a customer-centric company.  

“Hugely important is what it is.  Sentiment analysis tools use machine learning to chase up mentions of your brand, product or service online and to then present the overall results in terms of positive, negative or neutral.  As well as being able to drill further to get more insightful results and read actual comments, these tools are now being trained to recognise emojis for even more accuracy. 

 I regularly use sentiment analysis at Chilifruit as a lot of my new business is gained by word of mouth and by online reviews.  As such, it’s absolutely vital that I know what is being said about my brand at any given time.  In the rare case of negative comments, I’m able to respond directly and nip the issue in the bud before it becomes a reputational problem.” Milosz Krasinski, Managing Director at web consulting company Chillifruit 

Customer research and insights

Your customers will not only complain online. They will also leave valuable feedback that helps you improve your product, service, and messaging. Researching  consumer attitudes is a must-have these days.

No matter in what industry you are operating, your ultimate goal is to present your product or service to the right audience. People who are actively looking for the solution you have to offer will also tell you what they like and dislike about your offering. 

You can also apply sentiment analysis to customer experience. It is much cheaper to sell to your existing client base than to acquire new customers. Your customers expect a hassle-free, smooth experience. If you are not able to provide one, they will look for an alternative. 

Automated sentiment analysis and text classification will help you swiftly assign any incoming queries. Reducing the time your customers have to wait for a response will result in reduced churn levels. 

Market research 

Sentiment analysis will not only help you better understand your customers; it will also improve your understanding of your business niche. 

You can compare your products to your competitors and identify any niches your product will fit. 

If you apply the sentiment to social media accounts, you can see what type of content resonates best with your audience and on which platforms you are most likely to succeed. 

Campaign monitoring

Sentiment analysis should also be an indispensable part of your PR and marketing campaign assessment. 

Most PR professionals and marketers focus on the number of mentions, the importance of the source of the mentions, or social media reach. 

Sentiment analysis is the cherry on the top of all these data.  

After all, a high volume of mentions is not an indicator of a successful campaign. But a high volume of mentions and prevailing positive sentiment most certainly is!

Sentiment analysis challenges

Since we already know a lot about the benefits of sentiment analysis and data mining, let’s examine the challenges you will face. 

Although sentiment analysis is getting better with every analyzed mention, the process still has to be aware of its shortcomings. 

Sarcasm and irony

The biggest challenge is, of course, detecting irony and sarcasm. That shouldn’t surprise you, given that many people have a problem with recognizing sarcastic statements. 

Comparisons

Consider this statement:

This is better than nothing. 

Would you assign it a positive, negative, or neutral sentiment? As with sarcasm and irony, it is hard to decide without the context. Your sentiment analysis tool of choice will face the same problem. 

Is sentiment analysis accurate?

At this point, you might think that sentiment analysis is not worth the effort.

But it is!

First of all, sentiment analysis will assess the vast majority of online mentions correctly. From the start, you will be able to benefit from sentiment analysis and implement the results across your company. 

Secondly, the automatic and hybrid approaches to sentiment analysis will improve the results over time. Of course, the algorithms have to be based on large data sets. But you will see improvements over time. 

And thirdly, the main problem of sentiment analysis — sarcasm and irony — is not that common. People usually say what bothers them right away. 

Should you implement sentiment analysis?

The answer is simple — yes, you should. 

Sentiment analysis will give the necessary context to other data available at your company. You can apply the data to many different aspects of your business, from brand monitoring and product development to customer experience and market research. Incorporating sentiment analysis into your overall brand analytics you can work faster, deliver more accurate results, and exceed your KPIs. 

Sentiment analysis is no longer a technological curiosity. The process has practical applications that will help you bring your business to the next level.