Unlocking the Power of Data: How NLP Enhances Business Intelligence. BI Business Intelligence, Big Data, and Natural Language Processing (NLP)

By Massimo Re

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Index | ITA

In this article, we will seek to explain the functioning of Natural Language Processing (NLP) in a practical and technical yet simplified manner. NLP utilizes advanced algorithms and linguistic models to understand and interpret human language.

NLP Functioning:

Below, I explain the operational process of NLP:

1. Tokenization:

• Practical Description:?The first phase involves breaking down a sentence or text into smaller units called "tokens," which can be words, phrases, or individual characters.
• Technical Process:?NLP utilizes tokenization algorithms to identify and isolate each token within a text string.

1. Morphosyntactic Analysis:

• Practical Description:?Concerns the understanding of grammatical structures in sentences, including word roles, grammar, and word form.
• Technical Process:?Morphosyntactic analysis algorithms scrutinize each token to comprehend its grammatical properties.

1. Named Entity Recognition (NER):

• Practical Description:?This phase focuses on the identification and labeling of entities, such as names of people, places, organizations, dates, and so on.
• Technical Process:?NER algorithms employ statistical models to recognize patterns and contexts suggesting affiliation with a specific category.

1. Syntactic Analysis:

• Practical Description:?Aims to comprehend the grammatical structure of sentences by establishing relationships between words.
• Technical Process:?Syntactic analysis algorithms use models to create syntactic trees representing the hierarchical structure of sentences.

1. Semantic Analysis:

• Practical Description:?Seeks to understand the meaning of words and phrases in context.
• Technical Process:?Using advanced models and algorithms, NLP interprets the meaning of words, considering the surrounding context.

1. Machine Learning:

• Practical Description:?Many NLP systems incorporate machine learning techniques to enhance their understanding and adaptability to new data.
• Technical Process:?Machine learning algorithms are trained on large sets of annotated data to improve the accuracy and adaptability of the NLP system.

1. Language Generation:

• Practical Description:?NLP can generate text coherently in some applications, such as automatic responses or articles.
• Technical Process:?NLP utilizes language generation models to produce text that reflects appropriate context and grammar.

In Layman's Terms:

Essentially, the algorithm understands lemmas or words with the same meaning, connecting them to a concept or feeling. For example, leaf foliage is linked to a feeling of joy if green in spring, romanticism if red in autumn, or a sense of ending or melancholy if autumn is associated with negative concepts.

This process broadly illustrates how NLP works, with complexity varying based on the specific application and the intricacy of human language.

Context and Origin of Text:

Understanding the origin of all this text is crucial. The web is brimming with articles accessible through search algorithms and programs like Google.

In addition to this, comments and articles are abundant on various social media platforms. The applications are endless, spanning the fields of product and political marketing, serving as a form of public opinion verification, and assessing the effects of attempts at social psychology or psychological warfare.

We will now delve into understanding the functioning of NLP in the following articles, providing practical examples of the use of Big Data, AI, BI, and NLP in areas such as:

• Product launches in specific markets.
• Applications to luxury products.
• Usage in the entertainment industry.
• Management and profitability of celebrities, models, or actors.
• Verification of operations and the effects of collective psychology manipulation activities.

For example, campaigns promote sustainability, against gender-based violence, social inclusion, or induce desirable behaviors.

The author explicitly invites you to read the upcoming articles with practical examples to learn more about the applications of NLP.?

These will be posted in newsletters and appear as links to this article.

Conclusion:

This article provides an overview of the basics of NLP and its potential practical applications. In the upcoming chapters, we will explore each application in detail, offering concrete examples and insights into how NLP can revolutionize various areas of our daily and professional lives. Stay tuned for a deep dive into the world of Natural Language Processing.

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