What is the Sentiment Analysis? Use Case, Challenges, Benefits, and More.

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What is the Sentiment Analysis? Use Case, Challenges, Benefits, and More.

We can surely tell that with the development of e-commerce, SaaS tools, and digital technologies, sentiment analysis is growing the thing. Let’s see what sentiment analysis is.

Sentiment analysis Learn about Use Case and Challenges

After a fast look into Google Trends, we can see that sentiment analysis has grown more and more popular over the years.

In this blog post, I’ll explain:

  1. What is the Sentiment Analysis? Why Sentiment Analysis is Important?
  2. How Sentiment Analysis is Done?
  3. Challenges of Sentiment Analysis
  4. Use Case of Sentiment Analysis.
  5. Benefits of Sentiment Analysis.

What is the Sentiment Analysis?

It is a process for calculating the opinions of individuals or groups. Such as a segment of a brand’s audience or an individual customer in communication with a customer support agent. Based on a scoring device, It monitors conversations and evaluates language and voice inflections to quantify attitudes, opinions, and emotions related to a business, product or service, or topic. It is also known as opinion mining. As part of the overall speech analytics system, It is an integral part that defines a customer’s opinions or emotions.

Why Sentiment Analysis is Important?

First of all, It saves time and effort because the process of sentiment extraction is fully automatic. It’s the algorithm that analyses sentiment data, and so human participation is rare.

Secondly, It is important because emotions and attitudes towards a topic can become actionable pieces of information useful in various areas of business and research.

Thirdly, It is growing a more and more popular point like artificial intelligencemachine learning, and natural language processing technologies that are growing these days.

Fourthly, as technology develops, It will be more available and affordable for the public and smaller businesses as well.

And finally, sentiment analysis tools are becoming smarter with every day. The more they’re filled with data, the smarter and more accurate they become in sentiment extraction.

How Sentiment Analysis is Done?

The science behind sentiment analysis is based on algorithms using natural language processing to categorize pieces of writing as positive, neutral, or negative.

The algorithm is designed to identify positive and negative words, such as “fantastic,” “beautiful,” “disappointing,” “terrible,” etc.

This, however, isn’t always that easy.

Interesting Read: Do you know the types of Sentiment Analysis? And Why do you need Sentiment Analysis?

Challenges of Sentiment Analysis

Due to language complexity, It has to face at least a couple of problems.

Contrastive combination

One difficulty a tool has to face is contrastive combinations. They happen when one piece of writing (a sentence) consists of two different words (both positive and negative).

Sample sentence: “The weather was terrible, but the hike was amazing!”

Named-entity identifying

Extra big problem opinion Mining algorithms face named-entity identification. Words in context have various meanings.

Sample Sentence: Does “Everest” refer to the mountain or to the movie?

Anaphora resolve

Also known as pronoun resolve, explains the problem of references within a sentence: what a pronoun or a noun refers to.

Sample sentence: “We went to the theater and went for dinner. It was awful.”

Sarcasm

Is there any opinion mining tool identifying sarcasm? Please advise one!

Sample sentence: “I’m so happy the plane is delayed.”

The Internet

It just so happens that any language used online takes its personal form. The economy of language and the Internet as a common result in poor spelling, contractions, acronyms, lack of capital, and poor grammar. Analyzing such parts of writing may cause difficulties for opinion Mining algorithms.

Use Case of Sentiment Analysis

1. Segment User Groups Based on Opinions:

Tracking sentiment provides an organization to see which customers are more opinionated than others. For example, many believe that 80% of customer issues come from 20% of users. If this stat happens to be true, you will be capable of segmenting the qualities of that group, and each fixes common issues or even avoid those buyers. (Of course, avoiding users would have to mean there is little to no ROI based on the level/type of opinions of said group.)

2. Plan Product/Service Changes:

Analyzing customer opinions is a treasure trove of data, particularly when it comes to what you sell. Updating software products, increasing the design of physical goods, or bettering your services can all come from customer sentiment. At times, this data can even yield new products/services for your business to offer.

3. Plan Process Changes:

Customer sentiment isn’t always positive. But, negative feedback isn’t necessarily false. These opinions may require sorting out in a systematic way, meaning improving your overall customer service (or other) process.

4. Continuously Track Sentiment Over Time:

The sentiment is a metric worth constantly checking. As you improve both your processes and products, opinions will change. Seeing these changes allow for better navigating the turbulent waters of sentiment.

Benefits of Sentiment Analysis

A related opinion mining score provides insight into the effectiveness of call center agents and client support representatives and also serves as a useful measurement to gauge the overall opinion on a business’s products or services. When sentiment analysis scores are linked across certain segments, businesses can easily classify common pain points, areas for improvement in the delivery of customer support, and overall satisfaction between product lines or services.

By monitoring emotions and opinions about products, services, or even customer support effectiveness continuously, brands are capable of identifying subtle shifts in opinions and adapting readily to meet the changing needs of their audience.

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