Every organization uses documents to distribute, improve, and modify their services or products. Natural Language Processing (NLP), a subfield of artificial intelligence and computer science, focuses on the science of using machine learning for text analysis and extracting insights and information from the given text or conversation flow.
With the help of machine learning solutions and techniques, organizations can solve common data problems such as identifying different categories of users, identifying the purpose of a text, and accurately identifying different stages of user reviews and feedback. If the text data can be analyzed using deep learning models, then the correct answers can be created.
As an experiential provider of artificial intelligence services, Oodles AI provides a comprehensive guide to use ML techniques to understand text data and solve all the text problems for your service/products.
Organize your Data
IT teams have to deal with a huge amount of data every day. The first step in approaching the text and solving the text-related problems is to edit or gather information in accordance with its relevance.
For example, let's use a dashboard with the keyword "Fight." In editing information such as tweets or social media posts with this keyword, we will need to categorize them based on content relatedness. A potential policy is to report cases of physical abuse to local authorities.
Therefore, the details need to be separated based on the context of the word. Does the word in the context indicate that a structured game such as a boxing match or does it mean that its context refers to a conflict or clash that does not involve physical assault?
This creates a need for labels to identify relevant texts (indicating physical conflict or rage) and inappropriate texts (all other keyword content). Labeling data and training a deep learning model, therefore, produces quick and easy problem-solving results with text data.
Clean your Data
After collecting your data, it will need to be cleaned to teach a successful and seamless model. Here are other ways to clean your data;
- Get rid of non-alphanumeric characters: Although non-alphanumerics like symbols (financial symbols, punctuation) can hold important details, it can make data difficult to analyze a few models. One of the best ways to deal with this is to remove them or prevent them from text-based operations, such as the use of a hyphen in the word "full time."
- Use Note: Login involves breaking the string sequence into several pieces called tokens. Selected tokens can be sentences (punctuation) or words (word punctuation). In the repetition of sentences (also known as part of sentences), a piece of text is broken down into elements of its elements, while the touch of words breaks the text to the names of its elements.
- Use Lemmatization: Lemmatization is an effective way to purify data using analytic words and morphological analytics to reduce words related to their normal language base form, known as Lemma. For
example, Lemmatizations removes entries to return a word to its base or dictionary.
Use Accurate Data Representation
Algorithms cannot analyze data in text formats, so data should be displayed in our programs in the list of algorithms that the algorithms can process. This is called vectorization.
The natural way to do this would be to enter each code as a number for the reader to read each word's composition in a dataset; however, this is not possible. Therefore, the most effective way to represent data in our systems or classifier is to associate a unique number with each name. As a result, each sentence is represented by a long list of numbers.
In a representation model called Bag of Words (BOW), it is often referred to as the frequency of known words and not the sequence or sequence of words in the text. All you need to do is to decide on the most effective way of designing the vocabulary of the tokens (known words) and how to put their presence in the text.
The BOW method is based on the idea that the more often a word appears in a text, the more it represents its meaning.
Learn more: Build Models Using Machine Learning for Text Analysis
