How can Sentiment Analysis improve your business?

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Sentiment Analysis is a process used to identify the sentiment in a text, that is if it is positive, negative or neutral. It is done by combining natural language processing (NLP) and machine learning techniques to find out sentiment scores in a sentence. 

Sentiment Analysis is performed by splitting the text into individual entities such as phrases, words or sentences. After this, the related topic to each word is identified and the score is assigned.

Jump Directly to

1 .Types of Sentiment Analysis

2. Why does one need Sentiment Analysis?

3. How does it work?

4. Algorithms

5. Case Studies

6. Proxies for Sentiment Analysis

In short, it helps identify the feelings of the customers using emoticons, texts or images.

Sentiment Analysis

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It helps data analysts to conduct market research, understand the reputation of a product, get public opinion, monitor their brand value and understand their customer experience. When used effectively, it can be used to analyze social media streams and gauge public opinion. It can help you gather information on different aspects of the business as shown below.

Sentiment Analysis example

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Types of Sentiment Analysis

There are different types of sentiment analysis. And, each of it helps your business in a different way- some help you find the emotion, while some the intention of your customers. Each of these will help you get closer to your customers and help in increasing your return on investment.

1 . Fine-grained Sentiment Analysis

This is not about bucketing the sentiments of your customers into positive, negative or neutral but ranking them with the level of positivity and negativity. 

There are 5 categories:

  • Very positive
  • Positive
  • Neutral
  • Negative
  • Very negative

Very positive is ranked 5, positive is ranked 4, neutral is 3, negative is 2 and very negative is ranked 1, They help in determining the heights of the feeling a customer has towards your product. 

2. Emotion detection

Emotion detection is used to determine emotions such as anger, happiness, sadness, etc. Emotion detection systems use lexicons which is a list of words/emoticons that convey  meaning. This helps you to quickly know what the mood of the customer is. Most often, customers might not give you a feedback when you ask them to type in a text box.

3. Aspect-based Sentiment Analysis

This is used to analyze a specific feature of the product and not how the customer feels about the product as a whole. This allows you to concentrate on developing the areas that the customer feels there is a scope of improvement. For example, let us say you are buying a phone, the customer likes all the aspects of it, except for the front camera which may/may not be the key feature. feedback on that will help you improve and widen your target audience.

4. Multilingual sentiment analysis

It is a process of getting feedback from customers of different languages and then analyzing them to get  feedback. Let us assume that you have a large customer base, and people from different parts of the world will access your site. If you wish to get an overall feedback from your customers, then it becomes a  little tedious, because the text would be in different languages. You can easily use sentiment analysis here to find out the overall feedback. 

Why does one need Sentiment Analysis?

With the evolution and easy access to the internet, the emotions and sentiments of the people can be easily influenced. Let us consider a small example here, you are looking to book a resort in Air bnb, when you look into the review section, you see that, there is a comment that says “not so good”. Now, the reason why the review said that could be anything. But, the human brain will not get into the nuances of it. Although the review is not specific to anything there are high chances that one could ignore the property and choose a different one.

In the same example, if the owner has replied back to the thread, either giving a valid reason or asking for more information, then there are chances that you might consider. This is because you find someone to be responsible. Sentiment analysis plays with your sentiments building and forming a trust. With social media, easily accessible people want to know others opinions on anything and everything. These sentiments can be positive, negative or neutral. When it comes to business, the owner would want to know what their customers’ sentiments are to increase customer satisfaction. 

A single bad comment can go viral and bring the whole brand down. Identifying and handling them the right way at the right time saves your business. 

A few years ago a user posted a photo of oranges from Whole Foods and this created a whole new level of virality.

Sentiment Analysis instagram

This post gathered about 8 million impressions. While the Twitteratti’s were expecting an apology, whole foods made use of this unexpected advertisement and came up with a new tweet by promoting their new glass jar along with oranges.

Here are 5 reasons why you need to implement sentiment analysis:

1. Target individuals and improve your service

By identifying customers who feel negative about your service or product, you can reach out to them and make amendments. Let us say you have a customer who has left you a -.96 negative comment saying that he/she has ordered the product but has not got it in time, it gives you a chance to get in touch with them, cool them down and get the details of the order before they go ahead and cancel it. It gives you a room to show them that you are responsible for the business that you are doing.

 2. Track customer sentiment 

Tracking customer sentiment is better than calculating the NPS (Net Promoter Score) of your product? It helps you in understanding why the score of your NPS has changed or the factors aligned to how things have changed.

3. Determine customer segments 

It helps in understanding the different segments of your business. For example, what do people with a certain income base feel about your product. Is your product priced too much for them? How can you deliver at a rate that is affordable for them or how much would they be willing to pay for the service that you offer? You can also understand the behaviour of the customers from this, such as why are returns from a certain area too high?

4. Track how customers feel

As the business plan changes so do customer sentiment. Let us assume that you have revamped your entire site, or released a new user interface, this will definitely have an effect on the customer sentiment. how would your customers feel when they have been using an interface for years now and one day they log in to find a new user interface? Some might like it, while some may find it difficult. You can use sentiment analysis to find out if your campaign or effort is successful among your customers.

5. Determine your key promoters and detractors

Every business will have promoters and detractors. It is not necessary that every customer has to take up the NPS survey to identify this. Using Sentiment analysis it is possible to identify the emotions of customers even without them taking up the survey.

How does it work?

Any given entity, it could be a word, phrase or a sentence is marked with a value. This metric is called polarity. The values can be anything between +1 (Extremely positive) to -1 (extremely negative). These are then bucketed into positive, negative and neutral. Based on how they are bucketed, these scores are used to understand how people express themselves. 

The simplest approach is to use a dictionary to search for words and indicate a sentiment. For instance “Love conquers all” is considered as positive while “He conquered the kingdom and things fell apart” is considered negative.  It is the same verb but using it in sentences show how they are rated. This approach works to some level. However, there are advanced algorithms that are matched with NLP to understand positive and negative words. For example, Bloody can be used along with anything. “Bloody hell” constitutes negativity whereas “Bloody excellent” indicates enthusiasm and is positive. 

Let us see how this works, here is an example from Lexalytics:

(Image: lexalytics)

Although both sentences convey the same meaning, the first sentence has a lot more negativity when compared to the second. The human brain figures this out with the adjective-noun combination used. Therefore, the impact within the readers of the first sentence is more aggressive than those of the second. 

Most often we come across such phrases and react heavily to instances that do not require such harsh emotions. This is what sentiment analysis does.

Customer sentiment is useful because it helps the business identify what the customers feel. Once they crack that, they can reach out to customers and identify why they feel in a particular way. It helps them to understand what their customers are happy or unhappy about.   

In the below example, reviews of a hotel are categorized as positive and negative based on sentiment analysis.

A deeper analysis and interaction with the customers can help them delve deep and find out what they are unhappy about. When a customer says didn’t fix the issue, it could be anything ranging from hot water to room service. Understanding this helps the business to act upon easily and improve the categories in an effective and targeted manner.  

The need for advanced algorithm comes in when comments of people from different demographics need to be analyzed. Different people have different ways to say the same thing. Some might say neat, while another might refer to it as clean, whereas some might eat could eat of a floor. Although all of these means the same, it is just different ways to express the same thing.

Algorithms

Sentiment analysis can be implemented using different algorithms.

1 . Rule-based Approaches

There are a set of manual rules that the algorithm follows. These rules may use inputs from NLP techniques such as stemming, parsing, tagging etc. or use lexicons.

RULE-BASED APPROACHES

The list of positive and negative words will be fed into the algorithm. The algorithm will count the number of positive and negative words in an entity and if the number of positive words is more it will flag the text as positive and if the number of negative words is more it will flag it as negative. If it is equal then it is neutral.

It does not take into account how the words are combined. It requires a lot of manual input from the users every time. New rules must be added every now and then to get the accurate output.

2. Automatic Approaches

Automatic approach relies on machine learning techniques. It is modelled into the system and when the text is sent in, it is interpreted as positive, negative or neutral. 

AUTOMATIC APPROACHES

This algorithm also requires a constant update. Every time a new word is identified, it must undergo a training process. 

Case Studies

Here are a few stories of how sentiment analysis was used.

1. Uber

Uber uses the sentiment analysis tool to see if users like the new interface of their  app. And, this is what they quote.

“At Uber, we use social listening daily, which allows us to understand how our users feel about the changes we’re implementing. As soon as we introduce a modification, we know which parts of it are greeted with enthusiasm, and which need more work. We’re happy that the new app was received so well because we’ve put a lot of work into it.” 

 

Source: Brand24

UBER

2. Satoria Group

Satoria Group was founded in 1997, they wanted to gather information from their customers and provide exceptional quality. They manage hotels like Best Western Hotel Felix, Start Hotel Atos, Promenada Centre, or Augustów Medical SPA Resort. 

According to Trip Advisor, 98% of hotel guests make their decision based on online reviews. Therefore, Satoria group uses Sentiment Analysis to understand their customers and provide rich services.Additionally, on using Sentiment Analysis they have gained more than 10 corporations with events and are now able to identify brand ambassadors at ease.

3. Wayfinding in Atlanta Airport

Lexaltics processed over 2,759 Facebook reviews for the Atlanta International Airport and found out that people found it difficult to distinguish terminals.  Customers at Atlanta Airport, rely on Air Trains to get to the baggage claim, which is miles away from the gate. So, in the absence of a train, guests need to walk this distance. 

Using Sentiment Analysis, it was found that most of the common people were dissatisfied because of this. 

ATLANTA AIRPORT

ATLANTA AIRPORT

With this information, the Atlanta airport can work to provide better service for their customers.

4. Banking

A major bank in South Africa wanted to differentiate itself by improving customer service. They wanted to project themselves as a bank that really cares about its customers. The bank used Repustate, a sentiment analysis tool to help it meet this challenge.

The comments from different categories such as mobile banking fees and charges, branch banking, online, support and products were bucketed. The positive and the negative feedbacks were collected and were acted upon. 

Here is what the graph looked like before making the amendments. 

banking

Most of the feedback included long waiting hours, no tellers, hours of operation etc. Once the bank identified the key reasons in branch banking and online and rectified it. And this was the change. 

banking

5. Automobile Industry

An automobile industry wanted to find out how customers from different geographical locations react to their campaign. He used Repustate to perform this research. His main intention to find out how his brand is perceived online among car affectionates. Social media outlets, as well as fifty blogs and forums, were identified as target sites for extracting content. Content from different languages such as Arabic, German, Spanish and English were crawled.

A net sentiment score was derived by simply taking the percentage of positive blocks of text and subtracting the percentage of negative ones.

automobile industry

The company sent out messages to all the customers with  negative feedback that they were doing their best to solve the issues. Post the PR campaign (Push 1 and Push 2), it was found that:

  1. The PR push had no effect on negative German speakers.
  2. The opinion of the English speaking audience did not affect to change and become positive.
  3.  The Arabic and Spanish speaking markets were close to neutral throughout the period.

 

Proxies for Sentiment Analysis

Sentiment Analysis is mostly done by scraping data from multiple websites and analyzing the data on the whole. It is used to detect patterns and trends in customer perception. 

With the help of Web Scraping it is possible to automate this process. You can easily extract the data that you need with the help of Web Scraping. There are multiple Web Scraping tools that help you to easily access to this data. Proxies play a vital role when it comes to web scraping.  Proxy servers work as a middleman between your web scraping tool and the website. The HTTP request to any website will pass through the proxy server first and the proxy server will pass on the request to the target website. The main reason why you need a middleman proxy server is to hide your IP address from all websites so that even in the worst case you will not get blacklisted.

proxies for Sentiment Analysis

Limeproxies offer dedicated proxies that will help you perform web scraping. While there are multiple vendors out there who offer proxy services. We at Limeproxies offer you just more than that. We offer a commitment for guaranteed Uptime, Support and Services so that you can be assured that your work will go as scheduled.

Here are the benefits of using Limeproxies:

  • 1 Gbps speed with 100+ subnets, 
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  • Can use 25 IP addresses at a time
  • Guaranteed fast response time to any tech support issues that may arise.
  • With our premium private proxies, you’ll never have to share an IP address with anyone else because we use dedicated IPs.
  • Ability to change your proxy IP address on-demand, anytime.
  • Fully automated control panel 
  • 24/7 customer service. 

Bottom Line

Sentiment analysis can be applied to any business and is extremely useful for brand monitoring, product analytics, customer service, market research and so on. If your brand has customers across the globe and if you’d like to curate an overall opinion of your product, then it can be done only by using sentiment analysis. Incorporating sentiment analysis into your business helps brands perform faster, identify shortcomings, and increase the overall satisfaction rate of your customers. This brings in more business. It helps teams move to a competitive edge and become more productive. 

About the author

Rachael Chapman

A Complete gamer and a Tech Geek. Brings out all her thoughts and love in writing blogs on IOT, software, technology etc

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