Sentiment Analysis is one of the most important and popular applications of Natural Language Processing. Sentiment Analysis is a process of extracting the opinion of a person towards a specific topic from texts. According to Wikipedia

Sentiment analysis (also known as opinion mining or emotion AI) refers to the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information.

By using Sentiment Analysis, we can extract subjective information from texts. It can help businesses to increase engagement with their customers, analyzing feedbacks, etc.

With modern Deep Learning algorithms, the ability to understand the sentiment of texts has increased exponentially. Algorithms like LSTM Network can perform Sentiment Analysis with amazing accuracy.

How Sentiment Analysis work?

First, we need to understand, that Sentiment Analysis is a Supervised Machine Learning problem. In other words, in Sentiment Analysis, our algorithm learns a mapping between the text and sentiment. The sentiment can be Positive, Negative, and Neutral. In some cases, we work only with Positive and Negative sentiment also.

As it is a Supervised Classification problem, we need a Classification algorithm. A Classification algorithm classifies inputs to specific classes. In this case, the input is the text from various sources and output classes are the possible sentiments.

All Machine Learning algorithms work only with Numbers. But text information is string, not numbers. That means we need some way to represent these texts into numbers. To do that, we use Word Embeddings.

Word Embedding algorithms are algorithms that represent the texts into numbers without losing the meaning of the text and the order of the words in the sentences. Word2Vec and GloVe are some examples of Word Embedding algorithm.

Text is a type of Sequenced Data. In Sequence Data, the data comes as a sequence. In this case, it is a sequence of words.

To process these sequence data, we usually use Recurrent Neural Networks. LSTM is a type of Recurrent Neural Network that works better than other types of Recurrent Neural Networks and has some other advantages also. LSTM can perform Sentiment Analysis with amazing accuracy.

Applications of Sentiment Analysis

Sentiment Analysis is one of the most widely used technique in Data Analysis. It is very important and various industries are dependent on it.
There are so many applications of Sentiment Analysis. Some of them are discussed below.

Customer Engagement

Customer Engagement is one of the most important way to attract potential customers. Various businesses do different things to increase engagement with customers. Unfortunately, they don’t work that well.

Sentiment Analysis can play a very important role in increasing engagement with customers.

For example, these days companies receive thousands of posts through various Social Networking platforms. People use these platforms to tell their experience with a specific product/service, ask questions, etc. Using Sentiment Analysis businesses can analyze these posts on a very large scale and take specific actions. By taking specific actions businesses can increase customer engagement easily.

Decision Making

Another application of Sentiment Analysis is that it helps the management team of a business to make decisions that are beneficial to the company.

Sentiment Analysis can be used to extract the opinion of a large group of peoples towards a specific topic. By extracting the opinion, companies can understand the demands of people. Based on public demand, they can make changes to the business.

Sentiment Analysis can also be used in detecting trending topics. By detecting the trending topics, the management can plan for the future of the business. For example, based on new trends, a clothing company can make specific changes in their products to meet customer expectations.

Competitive Business

There are multiple companies that provide the same products or services. Because of that companies face massive competitions. They have to compete with companies. For example, there are so many companies that make mobile phones. That means customers have so many choices.

Sentiment Analysis can be useful in such scenarios. For example, getting x% positive and y% negative reviews on a certain product doesn’t make that much sense unless we compare the result with other results from other companies. By doing that we can understand how well a particular business is doing over its competitors.

Employee Engagement

Maintaining a good relationship with employees is very important for businesses. But in large corporations, with thousands of employees, maintaining it is very hard.

Employee Feedbacks are very important and useful in understanding the employees, their problems, etc. But as there are so many feedbacks, process all these feedbacks is very hard and challenging.

Sentiment Analysis can make this process very simple and fast. We can use Sentiment Analysis to analyze all the feedback and comments.


There are so many applications of Sentiment Analysis. It is widely used in numerous industries. In businesses, Sentiment Analysis can be used in various situations and they can greatly affect the performance of the businesses.


Today in the digital age, everyone has access to gadgets like a mobile phone or a professional camera that can take a picture or record audio and video of different types of incidents, events etc. that occur in our life. There are so many online platforms like YouTube, Instagram etc. where we share these types of files. Just imagine the amount of data we are talking about and all kinds of meaningful information we can extract out of these through proper analytical tools. To perform Data Analysis, it is important to understand that these recorded images, videos, or audios are very complex and unstructured. But there are tools available by which we can perform the analytics to extract information from these types of data

Problem Actualization

According to the requirement of the client, the analysis tool that we have built needed to have the ability to analyze these data and extract meaningful information from these data. They wanted a system to upload different images, pdf documents, audio files, and video files to the system for analysis.

For images and pdf documents, the client wanted the textual information from those files. That means they needed an OCR system.
According to Wikipedia
“Optical character recognition or optical character reader, often abbreviated as OCR, is the mechanical or electronic conversion of images of typed, handwritten or printed text into machine-encoded text, whether from a scanned document, a photo of a document, a scene-photo (for example the text on signs and billboards in a landscape photo) or from subtitle text superimposed on an image (for example from a television broadcast).”