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Sentiment Analysis1

Sentiment Analysis

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Understanding what customers have to say about your brand and what they feel will remain the most important factor for any business. 

Businesses are joining the digital world. Customers as well are shifting towards using apps or websites to interact with the companies. Especially now when businesses have little or no interaction with their customers, customer feedback is a curious business across all industries. 

However, with all these different communication channels available to the customers it is difficult to sort through the data overload and understand customer sentiment. This brings in Sentiment Analysis, the most effective method to analyze customer sentiment. 

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What is Sentiment Analysis?

Sentiment Analysis2

Sentiment Analysis enables a business to gauge the positive or negative sentiment of the customers. It is a part of NLP or Natural Language Processing, the process is carried out on text-based customer insight. It helps to understand customer sentiment regarding the brand and its products or its services across various industries. 

It is utilized by companies to recognize different types of customer sentiment in social media platforms. It helps measure brand reputation. 

Analyzing customer’s thoughts and opinions as they share them on social media, allows companies to understand what customers like or dislike, and what makes them happy or unsatisfied. These insights help brands tailor their services and products in a way that meets customer expectations and needs. 

What is Sentiment Score?

Sentiment score is the system that detects the depth of emotion in text feedback. 

The process involves analyzing a piece of text (a social media comment or a document). As a result, you get a sentiment score that tells you how positive or negative the comment is. For example, a score ranging from 0 to 10 with ‘0’ being negative and ‘10’ being positive. 

Customer Feedback

Sentiment

I would recommend visiting the café to all of my friends. The coffee is amazing and the staff are all very helpful and friendly. 

Positive

I would not recommend this book store. The books are terribly maintained and yet expensive. The staff are lazy as well. 

Negative

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What makes Sentiment Analysis important?

Sentiment Analysis3

Sorts through huge Data:

  • It helps a company by automatically organizing a large amount of data left behind by customers on social media conversations, reviews, emails, etc. Sentiment Analysis sorts through the large volume of data in real-time.

    Manually filtering through all the data is a difficult task and one is bound to make mistakes. However, the algorithm of Sentiment Analysis saves any human participation, making the process more efficient.

Prevent crisis with Real-Time Analysis:

  • Sentiment Analysis automatically analyzes the conversations and collects mentions of the predefined keywords. This helps a brand identify any negative situation or crisis so that the company can react to the problem and resolve it. 

    The brand can receive a real-time analysis of any text that displays negative emotion across social media, websites, emails, or surveys. You can take action before the situation gets out of hand or the negative comments spread across the internet.

Useful information:

  • Customers are more open to share their opinions and thoughts with the brand, be it positive or negative. It helps monitor and track customer feedback across various communication channels.

    You learn more about what motivates them to continue being your customers and what makes them want to leave your company. You gather useful and actionable insight. 

    Companies want their customers to perceive the brand positively. Sentiment Analysis helps uncover the negative emotion so that companies can take action and transform them into a positive emotion.

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How does Sentiment Analysis work?

Sentiment Analysis utilizes NLP and Machine Learning algorithms to detect emotion behind text feedback across the internet. There are primarily three techniques of how sentiment analysis works. Each of the three techniques depends on the amount of data you want to analyze.

Rule-based approach

A rule-based approach involves human-crafted rules to determine the polarity, subjectivity, or sentiment of the text conversation. 

For example:

  • Create two separate lists of polarized words used in the text.
  • Count the number of negative and positive words. 
  • Compare the score of the polar emotions
  • Once analyzed, if the count for positive words is more than the negative, the system will report positive sentiment and vice-versa. The result will be neutral when the numbers are even. 

However, it does not consider how words are arranged or combined to form a sequence. If you add new rules to support vast vocabulary or expression it may make the system very complex. It may also affect the previous result.

Developing data, uploading it to the system, and maintaining the system need to be done manually. Thus, it may be insufficient when data has to be changed or complex data needs to be analyzed. 

Automatic System

The Automatic System follows the same process as Rule-Based Approach. However, in the case of an Automatic System, there is no need for manual work. 

The system works independently as Machine Learning classifies the text, analyzes it, and identifies the sentiment category (positive, negative, or neutral). 

The common types of Machine Learning Algorithm used for Automatic Systems are

  • Naive Bayes
  • Linear Regression
  • Support Vector Machines
  • Deep Learning
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Hybrid System

It is a combination of both the systems mentioned above. A Hybrid System is considered more efficient and accurate because of the balance in automation and manual process.

How can businesses use Sentiment Analysis?

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Sentiment Analysis is used across different industries namely, healthcare, banking, call centers, government sectors, etc. There is an endless no. of ways Sentiment Analysis can be used to enjoy the benefits in all these sectors of industry.

Social Media Monitoring

Social Media can either be a blessing for your brand or it could become a nightmare as well. Positive and negative news spread across all sorts of social media platforms like wildfire. Thus, it makes complete sense to monitor and track any conversation regarding your brand on social media. 

For customers, social media is the platform that empowers them to share their opinion about their experience with your brand. Sentiment Analysis can help you to analyze how customers feel about a certain situation and prevent it from spiraling out of control. This prevents any PR crisis for your marketing team to handle. 

Monitoring social media can help your brand:

  • To track customer’s perception of the brand
  • To identify every meaningful interaction your customer has with your brand. 
  • To recognize any unsatisfied customers and respond to them
  • To identify platforms and type of customers who are relevant for your brand
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Monitoring Brand Mentions

Brand Mentions includes reaching news articles, blogs, product reviews, forums, press releases, and many more channels to gather information. Sentiment Analysis observes and gathers brand mention across the web and delivers them to designated teams. 

It helps you understand the emotion behind the customer’s opinion. Tracking sentiment in regard to brand mention can help you to identify the shift in customer sentiment along with the rise and fall in satisfaction about the brand, products, or services. 

Sentiment Analysis automatically tracks and analyzes all the interactions around your brand across the web. It helps you respond to any potential crisis while you can still prevent it.

Analyze Customer Feedback

Sentiment Analysis works in textual data. You can make use of it by analyzing the responses on the survey as well as interactions on customer support. 

Companies collect customer feedback via email surveys, NPS® , phone interviews, user reviews, etc. However, all such feedback is unstructured data. Sentiment Analysis can be used to categorize the data and identify customer sentiment.

Response to open-ended questions can be difficult to analyze. With the use of Sentiment Analysis, you can classify customer feedback into positive, neutral, and negative sentiment. This will help you gain insight into what makes customers satisfied or unsatisfied with your product/service. 

You can also use Sentiment Analysis to further investigate NPS® surveys. NPS® helps you identify promoters, detractors, and passives in your customer base. Although it provides you quantitative data, you can ask respondents to explain their reason for the score. You can then use Sentiment Analysis to gain qualitative insight. 

The ultimate goal of observing customer sentiment is to identify unsatisfied customers and elevate them into satisfied customers. It can separate positive and negative responses. As a result, you can prioritize customer feedback and focus on resolving any potential issue. 

Product Analysis

Sentiment Analysis can help a brand with exploring market trends and also in competitive analysis. It sifts through the web to provide you market reports, news, or product reviews that are relevant to your business.

Customer feedback regarding the products and services you offer are treasures that you can take advantage of. The reviews and comments about the products and services help detect the performance of specific aspects. 

It helps you uncover which aspect of the product or service leads to a negative review from customers. Analyzing customer reviews can help you identify the gaps in customer expectations and what you deliver. 

The insight you gather from all over the web is especially important at initial stages of marketing campaigns when you need to have those products tested by users. Sentiment analysis at this point enables a company to make improvements in their products and services to align with customer demand and expectations. 

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Competitor analysis: Market Research

Sentiment Analysis can also be used to analyze your competitors. You can monitor social media to spy on how your competitors are performing in the market. Using sentiment analysis to analyze your competition can help you see how things are seen by customers in general.  

You can track your competitor’s customers to examine their strengths and weaknesses. You can identify what customers dislike about your competitors and build excellent strategies to counter those weaknesses and improve customer experience.

What are the Advantages of Sentiment Analysis?

Sentiment Analysis can identify positive and negative emotions in textual context. Now let’s see how you can benefit from them.

Identify Emotional Triggers:

With Sentiment Analysis, you can identify what campaigns, conversation or message triggers their emotional response and how. Customers base their decision on the emotions they develop toward your brand. 

For example, the use of “emoji” and “appreciative message” triggers positive emotion and changes the customer’s mood to satisfaction. 

If you can identify these emotional triggers you can customize the message or conversation that will help you give better service and create a positive experience for the customer. 

Opportunity to Upsell:

As mentioned above, positive emotion can help create a better experience for your customers. You can use Sentiment Analysis to identify your happy customers. You can also recognize how they perceive your products or services, which aspect they like, and who are more likely to spend more. 

This helps you recognize the opportunity to offer recommendations to your customers based on products and services they liked. You can use Sentiment Analysis to recognize what makes them happy and personalize recommendations, instead of upsetting them with recommendations at random.

Live insight for Customer Service:

It may be difficult for your customer service agent to figure out the best approach for every customer. The customer’s mood may change mid interaction which can make it difficult for agents to adapt to the change without any clear indication. 

Sentiment Analysis can give live insight to your agents during the interaction. They can see the shift in customer’s moods in real-time which can help the agent know how the interaction is going. The agent, as a result, can adapt to the mood of the customer and provide an empathetic service.

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Crisis Management:

Monitoring customer’s opinions on social media and other platforms across the web can help in identifying any potential issue. Sentiment Analysis comes in handy to identify any negative feedback towards your brand so that your brand can prevent any escalation. 

Timely intervention during those situations can help prevent it from spreading all over the internet. Sentiment Analysis enables a brand to stay updated on any negative thread online that may become a potential issue for the brand.

Understanding Customer’s Need:

Sentiment Analysis can give you the insight to understand the needs, expectations, and demands of the customer base. It gives you a glimpse of specific comments and opinions regarding the services and products that your brand offers. It offers you insight into the problems that customers face. This way you can offer an effective solution that can help elevate customer sentiment.

What are the challenges faced in Sentiment Analysis?

Sentiment Analysis can fall victim to some roadblocks. Machines are programmed to analyze human emotions but they may still have difficulty in understanding a few intricacies of human language. Here are some of these challenges in Sentiment Analysis.

Polarity:

Sentiment Analysis identifies the extreme words such as “like” and “hate”. It can recognize words from sentences that are either positive or negative. However, there are many in-between terms used by users in their comments such as “not so bad” or “kind of good”. These terms imply average emotion, i.e., mid-polarity. Oftentimes Sentiment Analysis fails to pick up on these emotions.

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Sarcasm:

Internet users often use irony or sarcasm in their comments. For example, “The phone was so good that it broke down after a month of using it.” People use backhand compliments to express negative emotions. 

Sentiment Analysis, however, will take note of the word “good” and give you a positive score on analysis. It is difficult for the tool to detect the real context behind the sentence. And, you may receive a higher volume of “positive” sentiment score.

Emojis:

Emojis are another hurdle for Sentiment Analysis. It is trained to understand and analyze human language. It can extract text from some form of an image. However, in the case of emojis they are a language in itself, for example, 🙂 means happy or satisfied while 🙁 means sad or unsatisfied. 

Most sentiment analysis tools cannot detect these emojis and as a result, you may not obtain holistic insight.

Comparative Sentence:

These can be difficult to detect by Sentiment Analysis. These sentences may not outwardly show any sentiment but the comments rather state an opinion on the subject. 

For example, “This house is bigger than the others we have seen.” This sentence cannot be classified as negative, positive, or neutral. But when the context is taken into consideration a human can tell that “this house” is considered in positive emotion. 

So, for Sentiment Analysis, it will be difficult to categorize comparative statements.

Double Negative:

We know double negative turns the sentence into positive. But the Sentiment Analysis tool may not know it. For example “I don’t want no pasta”, this comment implies that the person would like to eat pasta. But, Sentiment Analysis may not pick up on the context and may not regard the comment.

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Conclusion

The usefulness of Sentiment Analysis depends on whether you know how to use it to its full capabilities or not. You need to be well aware of where you can apply it and how you can benefit from it. 

Sentiment Analysis can help you identify negative emotions toward your brand, angry customers and prevent any PR nightmare. But it can also help you identify happy customers who are more likely to be a promoter or brand ambassador. You need to know what you are looking for and how to work with it.

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