Resumes of Processes – Fundamentals of Natural Language Processing

Resumes of Processes

Recruiters spend a lot of time every day reading through resumes to find the right person for a job. Each resume has the same information, but they are often formatted and printed in different ways. This is a classic example of unstructured data. Recruiting teams can quickly get the most important information about candidates by using an entity extractor. This includes personal information such as name, address, phone number, date of birth, and email address, as well as training and experience information such as certifications, degrees, company names, skills, etc.

Sentiment Analysis

This is a common type of text analysis in which a score is assigned to indicate whether a text extract is positive (or negative). For example, a store might look at customer reviews to see which ones are good and which ones are bad. Sentiment analysis is an important part of natural language processing (NLP) in the world today. Sentiment analysis, also called “opinion mining,” is a way to figure out how someone feels about a body of text. So, how does this apply to our world? Social media has been an essential component of life since the Internet’s inception. When we search, post, and interact online, whether on social media or elsewhere, we can make things happen or be affected by what other people do. Sentiment analysis is therefore a powerful tool for political campaigns, marketing, business, and making decisions based on what people say they will do.

Corporations use NLP techniques to figure out how people feel and what they think, especially in the areas of semantics and figuring out what someone means. “Word sense disambiguation” in NLP means being able to figure out what a word means in a certain situation. Voice tagging and other NLP techniques are often used in social media to figure out things like the subject, verb, and object of a sentence. NLP-based sentiment analysis is then used to find an underlying relationship and figure out whether the tone of the sentiment is positive, neutral, or negative.

Text analytics tools provided by the Language Service can analyze labels and text to determine the sentiment of the text. It provides some context for the sentiment the author wanted to convey. This feature can find both good and bad feelings in online forums, customer reviews, and social media. The text is analyzed by the service using a machine learning classification model. It then gives the text a score between 0 and 1 based on how it makes the reader feel. Scores closer to 1 show a positive attitude. For scoring, the point in the middle of the range (0.5) is considered neutral or indecisive.

Sentiment analysis might be applied to the following two restaurant reviews, for example:

• “Yesterday, we came here for lunch, and the first thing I observed was how kind the staff was. After a kind greeting, we were immediately directed to our table. The food was delicious, the chairs were comfortable, and the ambiance was so warm.”
• “One of my worst eating experiences ever happened at this establishment.” Both the meal and the service were subpar. “Never again will I dine in this establishment.”

The second review can reduce the score to as low as 0.1, whereas the first review is much better and the score could be as high as 0.9.