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Sentiment Analysis An Overview

On 22 Oct, 2020 | No comments
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Sentiment Analysis…

Sentiments, this word is available in almost everyone’s vocabulary, be it the share market fluctuations or a movie review or a relationship. All of us would have used this word many times in our daily lives. This simple word is of great importance in the world of customer experience and if wisely analyzed, it is a gold mine of suggestions, concerns, gaps solutions etc. from the customer’s lens.

With this article, I have tried to simplify the core of “Sentiment Analysis” and its application in lines with the expectations of the new age customers.

What is Sentiment analysis?

Let us understand what it means. Today, customers raise their concerns via multiple channels and one of them is “Social Media”. A tweet from a customer about a bad experience is way more than a message. It has customer’s sentiments or emotions basis a certain experience. It will not be wrong to say that “Sentiment Analysis is judging the opinion of a text”. This is widely applied to reviews, survey responses, online and social media applications. Sentiment analysis helps wade through that data and figure out what people really think. It uses machine learning and text mining to provide a more complete picture.

For example, the Obama administration used sentiment analysis to measure public opinion to campaign messages and policy announcements ahead of 2012 presidential election.

Application of Sentiment Analysis?

Just Imagine that I am in charge of a product, and I am keen to know how the product is being viewed by customers. So, I start going through tweets or reviews at the portal.

“I love this product your product is so reliable”

“Don’t use this product I hate this product”

This is simple enough. Any basic sentiment analysis software will tell that the first tweet is positive, and the second tweet is negative. But human tweets (or rather, expression) are often much more complicated than that. They convey a wide range of emotions and often require context to fully understand. Look at the following tweets:

“This product is healthy and non-toxic which makes It different from other”

“They are not providing the refund, support and service are so lazy”

At this moment we want to find some keywords and make it a regression problem that gives a value between some range in the form of sentiment value.

Our Sentiment Analysis tool - SentimarQ :

1. Text Analysis

Before you can even begin building an automatic sentiment classifier, you’ll need large amounts of diverse sentiment data. QDegrees has access to 500,000+ qualified contributors that can process, analyze, and label text data quickly and inexpensively based on client specifications.

2. Social Listening

Our crowd of contributors can analyze user-generated content about your brand, competitors, or products. Track, analyze, and respond to conversations about your brand with social media monitoring services like QDegrees is providing.

3. Emotion Analysis

Train a machine to recognize emotions with human-annotated emotion data. Whether you want to collect images of facial expressions from a diverse crowd or detect underlying emotion in text, Lionbridge’s network of contributors can help gather the training data you need to train your model in emotion recognition.

Sentiment Analysis Uses :

The information you gain from analyzing consumer sentiment can be used to improve your business in many ways.

1. Drive Decisions

Our Sentiment analysis solution provides insight on any change in public opinion related to your brand that will either support or negate the direction your business is heading. High or low sentiment scores help you identify ways to restructure teams or develop new creative strategies.

2. Highlight competitive advantage

There are strategic benefits in knowing consumer sentiment related to your competitors. Sentiment analysis can help predict customer trends, so keeping a pulse on public opinion of other businesses in your industry provides a control group to compare your scores against.

3. Predict product lifecycle

Data derived from sentiment analysis reveals how well your product is faring in the market, how this performance can be improved, and if it’s time to pull it off the shelves.

4. Improve customer experience

Never underestimate the return on investment from a customer who feels like his voice has been heard. Understanding consumer sentiment provides a direct opportunity to fix the problems real users identify and put more resources behind the things your business is doing well.

How We Are Different :

At QDegrees, we believe businesses shouldn’t have to sacrifice speed, scale, or accuracy to understand what consumers are saying about their brands. Our opinion mining and sentiment analysis solution (SentimarQ) combine AI, Machine learning with real-time visualization on business analyst tools deliver the most accurate results, helping companies complete large-scale sentiment analysis projects in days.



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