This means that nlp will translate the words it finds into a set of numbers that the machine can understand. Nlp machine learning algorithms need to be given “trial data” as well as “expected output” . For example, suppose you enter the text “i like this product”, then enter the output “friendly”. Then, nlp will automatically use statistical analysis methods to build insights based on the data you entered earlier. Thus, the tool can predict the output for the next text. The more data entered, the more accurate the output will be . Because the tool has more references to study. So what are the results like? Examples like this: this product really helped me (friendly) the product can function well, but nothing special (neutral) i feel at a loss for buying this product (unfriendly) so, regardless of the purpose of its use, the way nlp works is more or less the same as above. Usually, the difference lies only in the analytical technique used.

Text Classification

What are the techniques used in nlp? There are many analytical techniques used in nlp technology. Even so, the average technique has the same basis, between semantic analysis or syntactic analysis : semantic analysis – a type of analysis that focuses on the meaning per word. Syntactic analysis – this type of analysis places more emphasis on the relationships between words or the structure of text in sentences. So, from these two types of analysis, various branches of analysis techniques used in nlp technology emerge, including: 1. Part Uruguay WhatsApp Number List of speech (pos) tagging part of speech tagging is an example of a technique that uses syntactic analysis. Because, this technique seeks to pay attention to the relationship between words. The trick is to put a “tag” in each word. Well, the tag itself represents the role of each word in the sentence.

Tokenization

Sounds quite confusing? It’s as simple as this. Suppose you see the sentence: “yesterday, i bought a laptop”. So, a tool using pos tagging will read the sentence like this: “yesterday” : description of time “i” : subject “buy” : verb “laptop” : noun in this way, the machine or tool can understand the context of the sentence more easily 2. Lemmatization in contrast to pos tagging, the basis of lemmatization is semantic analysis. Because, this technique seeks to reveal the context of writing by looking at the basic forms of each word. For example, suppose Mobile Lead there is an online review that says: “i bought the product yesterday, and i feel very satisfied.” if the analysis technique used is lemmatization, then the sentences that the machine reads are as follows: “i”, “already”, “buy”, “product”, “yesterday”, “and”, “i”, “taste”, “ satisfied”, “very” . As you can see, the word “change” is changed to its base form, which is “already”.