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Top 5 Sentiment Analysis Example

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The term sentiment analysis is quite popular. But most people do not really notice how important it is for a company’s success. Sentiment analysis provides an insight into all the problems that your company may be facing. It is like being able to listen to all the conversations that your customers are having about your product. It helps you understand how your brand is being perceived and work towards improving its image based on those insights.

Read on to know more about sentiment analysis, how it can benefit your business and its real-life applications.

What is sentiment analysis?

Humans can understand complex meanings in spoken and written words. For example, in a sentence like, ‘Great! my computer is not working’, it is not actually great but sarcastic, which a human would easily understand but a machine might not be able to. But humans have taught machines how to interpret the exact meanings of texts with the help of Natural Language Processing (NLP) and Sentiment Analysis.

Sentiment Analysis is being used by companies to analyze customer opinions about their products. How sentiment analysis is different from other types of analysis is that it is done by automation. Even though it’s possible to collect feedback through your customer service team, more honest opinions can be collected through other platforms like Facebook, Twitter, Amazon, and other forums of discussion.

The technology reads between the lines of Facebook updates, tweets, and email messages. It is the method of determining the opinions of an individual or a group of people, by monitoring these conversations. The language of the text is processed to understand the emotions and attitudes of people towards a topic, product, or service. 

To explain further…

Imagine a situation where you are the owner of a small business and you receive about 50 responses to your email surveys each month. You should be able to handle these surveys and record all the responses manually.

Now imagine this number increasing to 20,000. This is probably the number of responses being handled by a bigger company. It would still be an important part of decision making but is an impossible task to do manually. The result also needs to be summarized into a readable and actionable format so that it can be put to use. 

Then there is the issue of human bias. A person may be having a bad day that would influence their decision-making skills. They can interpret a message as positive or negative according to their mood. How they interpret a text may also depend on them having a preconceived idea about the topic.

Sentiment analysis is an automated process driven by algorithms that score the words according to the tone of the topic of discussion. It takes a piece of text, analyzes it, and gives it a score to measure the range that it falls in – whether it's positive, negative, or neutral. Also known as opinion mining and emotion AI, it is an important tool to gauge customer opinions. 

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Why is sentiment analysis needed?

There is an overload of data these days. Companies too are dealing with a huge amount of customer feedback that needs to be analyzed for decision making. And it is not possible for humans to analyze all this data manually. 

Since sentiment analysis is an automated process, a large amount of data can be considered for information, rather than a small amount of data that a human would be able to analyze, based on intuition. 

Now that we have discussed the benefits of sentiment analysis, let’s see 5 examples of how it is being used in real life. 

Examples of sentiment analysis

  • Brand reputation management

It pays to listen to what customers have to say about your brand. Sentiment analysis helps companies hear what their customers are saying, every time the brand is mentioned anywhere. This gives them an understanding of what the customer thinks about the brand. They can understand customer perception and find patterns and trends that affect the brand. Companies get insights into what customers are talking about their products, after every marketing campaign or product launch. 

Brand monitoring through sentiment analysis is equally useful for various industries including technology companies, fashion brands, media, and marketing.

  • Improving Customer support

One bad customer service experience can lead to a drop in sales for a company. Nowadays it has become much easier for customers to put forth their opinions on various social media channels. So, one bad experience of a customer can lead to a bad impression of the product. 

By doing sentiment analysis across various channels, companies can give their customer support teams a heads up in case something negative is going to come their way. They can put an emergency plan into place in case some feature of their product is highly criticized by customers. Sentiment analysis makes it possible for companies to continuously track the strong and weak points of a product and make improvements seamlessly.

  • Product analytics

Sentiment analysis is helping companies keep an eye on what is working for their product and what is not. It helps track specific comments or remarks about the product, or about its performance.

Through analysis, it is possible to create marketing campaigns for certain sections of people who have shown interest in a particular feature of the product. Product analysis also makes it possible to change certain features of the product that is not working during the initial stages. Sentiment analysis also makes it possible to get new ideas through customer feedback.

  • Competitor analysis

Sentiment analysis gives an idea about how the customer perceives a company’s brand as compared to their competitors. Companies can track their products as well as their marketing campaigns, to that of their competitor’s. 

  • Market research

Sentiment analysis can bring additional perspective to market research. It can give insights into how the consumer is going to view a product. It is quite useful for companies launching their products for the first time, or for ones who are launching a product into a new market. Tracking market research through sentiment analysis gives them an idea about how their product is being received in the first weeks or days into the launch.

This gives them the possibility of making any changes or solving early problems being faced by the product. It can save the company a lot of issues or money being spent down the line. 

Skyl.ai provides machine learning solutions for sentiment analysis which saves businesses hours of manual data processing, by automating text analysis and turning them into actionable data.

Check out our Machine Learning solutions for Sentiment Analysis.

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