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The most famous, well-known, and used NLP technique is sentiment analysis. This technique’s core function is to extract the sentiment behind a body of text by analyzing the containing words.
The technique’s most simple results lay on a scale with 3 areas, negative, positive, and neutral. The algorithm can be more complex and advanced. If the result is a negative number, then the sentiment behind the text has a negative tone to it, and if it is positive, then some positivity in the text.
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.
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.
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.
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.
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.
Sentiment analysis can be implemented using either supervised or unsupervised techniques. We use a supervised technique called Naive Bayes algorithm to perform sentiment analysis.