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Sentiment Analysis

What is sentiment analysis?

Sentiment analysis is a process of analyzing a piece of text and identifying opinions and judgements. This procedure shows you whether the sentiment of a piece of text is positive, negative, or neutral.

Natural Language Processing (NLP) and machine learning are used in unison to score the sentiments for categories, topics, or entities in a phrase.

Sentiment Analysis is also known as opinion mining or emotion AI. It can be used to transform unstructured text from social media, news, forums, blogs, etc., into structured data.

What is sentiment analysis used for?

Sentiment analysis is used by organizations extensively to conduct market research. It is also used to improve social listening by analyzing the sentiment that the market displays towards the brand on social media.

It is also useful to understand the underlying emotions in customer feedback and improve the customer experience based on that.

Sentiment analysis is deployed in intelligent customer service chatbots to understand the customer’s emotional state and deliver an appropriate response. Here’s why that is critical: Imagine that a customer sends a message in a tone that clearly indicates frustration, and gets a reply in a chipper, happy tone. That would annoy the customer even further. In this situation, a chatbot should be able to understand the customer’s emotional state, empathize with them 

Sentiment analysis also helps you understand the Voice of the Employee by analyzing employee surveys, Glassdoor reviews, etc.

How does sentiment analysis work?

In sentiment analysis, text is first broken down into topics and then scores are assigned to the topics, on a pre-decided scale.

After that, the system either uses each score individually to evaluate specific parts of the text or it adds up all the scores for the topics.

What are the types of sentiment analysis?

Here are the most popular types of sentiment analysis:

1. Fine-Grained Sentiment Analysis

Most sentiment analysis systems employ fine-grained sentiment analysis. It concentrates on the polarity of an opinion. Rather than rating sentiments to just be positive, negative, or neutral, it segments them further into very positive, somewhat positive, neutral, somewhat negative, and very negative.

It can pull this data from product reviews, surveys, etc.

The polarity detected can be tied to emotions as well.

Very positive = love

Somewhat positive = acceptance

Neutral = indifference

Somewhat negative = anxiety

Very negative = anger

2. Aspect-Based Sentiment Analysis

Instead of just looking at polarity, aspect-based sentiment analysis allows you to consider the sentiment regarding specific aspects, features, or elements of your product or service.

It helps you understand how your customers feel about specific attributes of your offerings.

3. Intent Sentiment Analysis

This is about understanding the intent that lies behind a message. Is it an opinion? Does it show appreciation? Is it a complaint, a question, or a suggestion? 

However, since it is difficult to do this without context, this is still a concept and is not practically used yet.


4. Emotion Detection and Recognition

This relies more on algorithms and uses lexicons and machine learning to understand customer emotions. But it doesn’t just show you the emotion, it helps you understand why customers feel how they do.

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Sentiment Analysis

October 14, 2020

Table of contents

Key takeawaysCollaboration platforms are essential to the new way of workingEmployees prefer engati over emailEmployees play a growing part in software purchasing decisionsThe future of work is collaborativeMethodology

What is sentiment analysis?

Sentiment analysis is a process of analyzing a piece of text and identifying opinions and judgements. This procedure shows you whether the sentiment of a piece of text is positive, negative, or neutral.

Natural Language Processing (NLP) and machine learning are used in unison to score the sentiments for categories, topics, or entities in a phrase.

Sentiment Analysis is also known as opinion mining or emotion AI. It can be used to transform unstructured text from social media, news, forums, blogs, etc., into structured data.

What is sentiment analysis used for?

Sentiment analysis is used by organizations extensively to conduct market research. It is also used to improve social listening by analyzing the sentiment that the market displays towards the brand on social media.

It is also useful to understand the underlying emotions in customer feedback and improve the customer experience based on that.

Sentiment analysis is deployed in intelligent customer service chatbots to understand the customer’s emotional state and deliver an appropriate response. Here’s why that is critical: Imagine that a customer sends a message in a tone that clearly indicates frustration, and gets a reply in a chipper, happy tone. That would annoy the customer even further. In this situation, a chatbot should be able to understand the customer’s emotional state, empathize with them 

Sentiment analysis also helps you understand the Voice of the Employee by analyzing employee surveys, Glassdoor reviews, etc.

How does sentiment analysis work?

In sentiment analysis, text is first broken down into topics and then scores are assigned to the topics, on a pre-decided scale.

After that, the system either uses each score individually to evaluate specific parts of the text or it adds up all the scores for the topics.

What are the types of sentiment analysis?

Here are the most popular types of sentiment analysis:

1. Fine-Grained Sentiment Analysis

Most sentiment analysis systems employ fine-grained sentiment analysis. It concentrates on the polarity of an opinion. Rather than rating sentiments to just be positive, negative, or neutral, it segments them further into very positive, somewhat positive, neutral, somewhat negative, and very negative.

It can pull this data from product reviews, surveys, etc.

The polarity detected can be tied to emotions as well.

Very positive = love

Somewhat positive = acceptance

Neutral = indifference

Somewhat negative = anxiety

Very negative = anger

2. Aspect-Based Sentiment Analysis

Instead of just looking at polarity, aspect-based sentiment analysis allows you to consider the sentiment regarding specific aspects, features, or elements of your product or service.

It helps you understand how your customers feel about specific attributes of your offerings.

3. Intent Sentiment Analysis

This is about understanding the intent that lies behind a message. Is it an opinion? Does it show appreciation? Is it a complaint, a question, or a suggestion? 

However, since it is difficult to do this without context, this is still a concept and is not practically used yet.


4. Emotion Detection and Recognition

This relies more on algorithms and uses lexicons and machine learning to understand customer emotions. But it doesn’t just show you the emotion, it helps you understand why customers feel how they do.

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