Machine learning
Natural Language Processing
Machine Learning algorithms are capable of training the computer to perceive and understand human speech without transforming it into computer language. This technology is called Natural Language Processing.
NLP opens the door to the development of the News Media Industry. The NLP algorithms are capable of searching the necessary information, parsing, analyzing, and systematizing the news according to the settled criteria.
NLP Techniques
01
Lemmatization and stemming
Stemming and lemmatization are the first two steps to build an NLP project. They represent the field’s core concepts and are often the first techniques you will implement on your journey to be an NLP master.
02
Keywords extraction
Keyword extraction is an NLP technique used for text analysis. It is often used as a first step to summarize the main ideas of a text and to deliver the key ideas presented in the text.
03
Named Entity Recognition (NER)
NER is a technique used to extract entities from a body of a text used to identify basic concepts within the text, such as people’s names, places, dates, etc.
04
Topic Modelling
Multiple algorithms can be used to model a topic of text, such as Correlated Topic Model, Latent Dirichlet Allocation, and Latent Sentiment Analysis. The most commonly used approach is the Latent Dirichlet.
05
Summarization
Text summarization is the process of reducing a large body of text into a smaller chuck containing the text’s main message. This technique is often used in long news articles and to summarize research papers.
06
Sentiment Analysis
Sentiment analysis can be implemented using either supervised or unsupervised techniques. We use a supervised technique called Naive Bayes algorithm to perform sentiment analysis.