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Sentiment Analysis. Image-web

What is Sentiment Analysis?

Sentiment Analysis or Opinion Mining is the process to determine or assess people’s opinions, sentiments, evaluations, appraisals, attitudes & emotions towards various entities like products, services, organizations, individuals, issues, events, topics & their attributes. Although Natural Language Processing (NLP) has a long history, a little research has been started before 2000; but now a day, this field has become a very active research area.

Why is Sentiment Analysis used for?

Sentiment Analysis is particularly important in tracking emerging trends or changes in perceptions on social media. It also helps data analyst to gauge brand/product reputation, public opinion, market research, any social data and customer feedbacks.

Why is Sentiment Analysis Important?

Sentiment plays an extremely important part in understanding the emotional state of customers. It can be combined with other customer analytics methods like predictive analysis, can uncover unknown insights and tease out the most important issues to fix to make your customers happy. Using sentiment analysis, it is very easy to detect customers’ complaints and resolve problems.

Sentiment analysis could also be used in politics and public actions e.g. in voting applications, the clarification of political positions, real-time event monitoring, policy proposals and so on.

What are the benefits of Sentiment Analysis?

The utility of sentiment analysis in different fields comes to fruition when large amounts of data can be analyzed and drilled into to provide a macro view and a micro view of sentiment. Results of the analysis can then be prioritized according to relative importance.

· Optimize product quality

· Optimize current marketing strategies or discover new marketing strategies

· Marketing campaign effectiveness can be measured

· Crisis management

· Improve Customer Service

· Improve Media Perceptions

· Increasing Sales Revenue

How accurate is Sentiment Analysis?

Manual evaluation can’t be accurate (around 60–65%) and most of the time it gives us wrong result. But when we automated sentiment analysis through natural language processing (NLP), we are sure that the results are reliable.

Types of Sentiment Analysis

· Standard Sentiment Analysis: it helps to categorize the subjective details whether they are positive, negative or neutral data

o Positive

o Negative

o Neutral

· Fine-Grained Sentiment Analysis: it provides a more precise output than previous one

o Very positive

o Positive

o Neutral

o Negative

o Very negative

· Emotion Detection: This sentiment type finds the customers’ feelings within the text or responses provided

· Aspect-Based Sentiment Analysis: Reviews are often considered the best way to understand the customers’ thoughts about a product or service. By discussing the specific detail or aspect of the product, the composed opinions provide useful insights to the companies.

· Intent Analysis: it tries to understand the action underlying in the reviews or opinions given by the customers. This sentiment analysis type provides opportunities to resolve customers’ complaints or issues related to the product or service.

Data used for Sentiment Analysis

The use of sentiment analysis has been a great help for organizations to monitor their brand reputation and get stay alert on any issues to arise. This is achieved by analyzing the reviews and tone embedded in the text that can be retrieved from different platforms.

· Social Media Monitoring

· Brand Monitoring

· Customer Support

· Customer Feedback

· Market Research

Sentiment Analysis tools

It’s a tedious process to go through each comment one-by-one and analyze them. Thus, a sentiment analysis tool processes customers’ feedback and automatically interprets whether it’s positive, negative, or neutral. Then, it compounds whole data and displays it in the form of charts or graphs, from where data analyst starts their work.

There is a wide range of tools that are used in sentiment analysis. These tools can be classified through the different techniques that are used for sentiment analysis. Few of them are listed below:

· HubSpot’s Service Hub

· Quick Search

· Sentdex

· Clickworker

· Repustate

· Emoticons


· Lexalytics

· Critical Mention

· Brandwatch

· Social Mention

· Sentiment Analyzer


· Social Searcher

· Rosette

· MonkeyLearn


The benefits of sentiment analysis are obvious, and are a great help in assisting organizations to understand what their customers think and feel about their brand. Additionally, using it in the right context is critically important as well.

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