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Introduction

Introduction

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.

Conclusion

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.

Data analytics is the process of extraction of meaningful information from data, increasingly with the aid of specialized tools and techniques. Data analytics help organizations and scientists make more informed business decisions.
Python has been around since the late 1980’s but has only really started making its presence felt in the data science community recently.

What does fuzzy mean?
Fuzzy means some thing that is not clear, usually because of other unwanted noises. Typos while searching can be termed as fuzzy too. But wouldn’t it be great if the computer could correct you mistakes or perhaps present you result that you might have wanted to type in?
Yes, such technology exists, and we use it every day, for e.g. all search engines like Google, Bing etc.
With Elasticsearch, we can now include these features into our own application for better user experience. In Elasticsearch there is something called fuzzy search, which is the topic of the discussion.

INTRODUCTION
It’s no secret that recent advancements in technology has propelled many industrial sectors into appropriating intelligent decision making and encouraging insights driven strategy development, thus making it far more profitable than it was when it had been averse to change. With recent universal stress on reduced environmental impact as well as fluctuating oil prices, it has become imperative, now more than ever, for the oil and gas sector to indulge itself heavily in creating avenues for utilization of technologies like Artificial Intelligence and Machine Learning.

In enterprise grade applications and specifically in product data management, the main focus of PLM vendors was about how to manage CAD files and optimizing check-in/check-out process , managing BoM , process control and measurement. This was mainly driven by the scientific discipline of “Knowledge management” (This term appeared in early 1990s) which was to use software to manage knowledge base , decision support systems and other joined efforts. But most PLM systems failed to deliver anything beyond data records which are yet to be discovered, analyzed and connected.