Analytics, AI/ML
January 30, 2024

Understanding Sentiment Analysis with Social Listening

Cogent Infotech
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Dallas, TX
January 30, 2024

Wouldn't you agree that what your customers feel about your brand matters a lot? 

In the long term, the perception of your organization in the eyes of customers is equally important as the quality of your product or services. Understanding how consumers perceive your brand has become simpler through sentiment analysis with social listening.

Social media has proved to be like an open-mic platform for customers where they can freely post about anything. If they have some unresolved grievances or had a great experience with a particular product or service, they will rarely shy away from posting it on social media. Moreover, Gen-X is so active on social platforms that they even post opinions about products or services they haven't yet used. 

It has become crucial for organizations to use social media analytics to monitor such social activity, which can help them maintain or enhance their brand image. And sentiment analysis on social media has become a foundation of social media analytics. 

Let's look at sentiment analysis, its accuracy, the types of sentiment analysis, and why your company needs it.

What is Sentiment Analysis?

"Sentiment" is an emotional attribute that defines an individual's perception. This sentiment can be positive or negative. And sentimental analysis indicates the direction of the customer's sentiment. 

Sentiment analysis is also termed opinion mining since it analyses what the customers or prospective customers have to say about your brand. It is a method that analyses online written pieces and determines the emotions associated with them.

It uses natural language processing and machine learning techniques because human language is complex, and to understand the sentiments, one needs sophisticated and intelligent means.

Sentiment analysis is used in social listening, social monitoring, image analytics, and customer experience analytics. It is an additional layer of analytics that helps to contextualize data and categorize customer emotions by their type or intensity. 

In simplest terms, sentimental analysis entails determining if customers have favorable, negative, or neutral feelings about a specific issue, brand, product, or service.

How is it Done?

Sentiment analysis is a technique that employs natural language processing (NLP) to recognize and categorize the posts, comments, reviews, etc., as positive, negative, or neutral. 

Sentiment analysis can be categorized into these thre types:

Fine-Grained Sentiment Analysis

It entails analyzing the reviews and ratings from customers through social listening. They are further classified to determine if they represent positive, negative, or neutral sentiment.

For example, comment on social media such as "After four months usage, my Samsung phone battery drains within 6 hours." signifies a negative sentiment about the phone. The company can use this to improve their respective mobile phone models. 

Aspect Based Sentiment Analysis

This type of analysis identifies the opinions of a specific feature or aspect of the product and service through social listening. And again, the direction of the sentiment (positive, negative, or neutral) is determined based on the analysis.

Suppose you offer SEO services to local businesses and have a dedicated social media page for advertisement. One of the comments within your post says, "Excellent results from their services, but the prices are steep." This comment can be considered neutral since customers value your service, and perhaps you need to work on the pricing aspect. 

Emotion-based Sentiment Analysis

When your customers leave feedback or post comments about your brand, they do it with a specific emotion. Emotional-based sentiment analysis identifies whether the emotion was positive or negative. 

"Loved the hospitality from your hotel staff during our holiday visit, Kudos!"

What does this type of comment reflect? It is a positive comment that reflects emotional happiness. 

How Accurate is Sentiment Analysis with Social Listening?

As per a report, sentiment analysis can be accurate from 60%-65%. It is important to understand that human sentiments are subjective and difficult to analyze accurately. Moreover, people sometimes use confusing terms, making it difficult for AI or NLP to process them precisely. 

For example, if a customer uses sarcasm to post his opinion about your brand, it will not be easy to get accurate results. Also, when there are dual or multiple sentiments in a single post or review, it becomes difficult to determine the direction of the customer's emotion.

Why Does Your Company Need Sentiment Analysis with Social Listening?

Now that you have a better insight into sentiment analysis, it is time to determine why you should use it? Companies can leverage sentiment analysis by:

Brand Monitoring

Customers often use social media platforms to post their experiences. These can be in reviews, blogs, discussion forums, comments, etc. It is essential to determine what type of experience your customer had and how their comments affected your brand image. Your brand image can be badly impacted if proper action is not taken at the right time. 

Sentiment analysis helps you understand the opinions your customer's posts reflect about your brand. The social media monitoring dashboard would give you a clear indication of positive vs. negative brand mentions. 

Keeping Reputation Crisis at Bay

Every organization knows that reputation is a significant factor in determining the success and failure of the organization. Every brand will have to face a reputation crisis, but what matters the most is how the situation is handled. What do you think would happen if nothing is done when the company's reputation is attacked? It would have a spiral effect, and all the hard work done by the company could go into the drain within a short period. 

You can use sentiment analysis with social listening to keep such a crisis that hurts the brand's reputation at bay. It would guide you to take the right action at the right time and avert the disaster. 

Assessing the Effectiveness of the Campaign

When your organization comes up with a campaign, it will attract attention and response. The comments, posts, likes, etc., would give you a better insight into your campaign's success. With the help of sentiment analysis, you can assess how effective your campaign is.

Organizations can use sentiment analysis with social listening when launching a new product or introducing new services. You can better connect with the customers when you know how they feel about your campaigns. It will also allow you to take corrective action if the campaign is not moving in the right direction.

Keeping a Tab on The Competition

One of the most interesting advantages of using sentiment analysis with social listening is keeping an eye on your competition. Through social listening, you can better understand how the customers responded to a particular campaign or their grievances for your competitor's products or services. 

Through sentiment analysis, you can understand the weak points of your competition and improve on such aspects to gain a competitive advantage. Also, you can get a better insight into what worked for them and what worked against them. This would allow you to make smarter decisions when creating a strategy for your brand. 

Concluding Thoughts

We live amongst the generation where people explicitly express their emotions towards an event, product, or service. Companies can get a better insight into their sentiments through the customer's comments, posts, opinion-poll responses, number of likes, etc. 

Sentiment analysis with social listening is not just essential but also the need of the hour. You need to put your customers first, listen to what they say, and react to it effectively and through the proper channel. 

If you are in the business world, this would be the right time to leverage sentiment analysis with social listening.

Check out Cogent's blogs to learn more about such trends and technologies.

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