NLP — Text Classification
When we talk about advanced technologies like Artificial Intelligence, Machine Learning, Computer Vision, Linguistics, and more, Natural Language Processing (NLP) stands as a dominant area of research and application, besides data annotation. Natural Language Processing comes with several complex and simpler processes and real-world applications like named identity recognition, machine translation, sentiment analysis, information retrieval, social media analysis, text classification, data mining, and more.
In today’s article, we will talk about one of the simpler processes that comes under Natural Language Processing.
What is Text Classification?
Text Classification is the term used to describe the process of labeling and organizing text data into clearly classified groups. Text Classification is a fundamental and significant part of NLP for Machine Learning. NLP helps make sense of the huge volumes of unstructured text data making data classification easier and more efficient.
Text Classification, also known as Document Classification, stands as one of the most significant tasks in Machine Learning- Supervised Machine learning, to be more specific. Text Classification may involve categorizing different documents and data, like on web pages, media articles, library books, galleries, and more.
The main process of classifying text data comprises near-to-specific stages that comprise setting up the environment for text classification, loading datasets into jupyter, extracting attributes and patterns from text data files, running ML algorithms, Grid Search for parameter tuning, and more.
Popular Applications of Text Classification
Some of the most popular and widespread applications of Text Classification would include spam detection in emails, sentiment analysis, labeling documents, language detection for translation applications, age/gender identification of users, online content tagging, speech recognition in virtual assistants, and a lot more.
3 Main Approaches of Text Classification
Rule-Based Approaches
The rule-based approaches apply a set of linguistic rules to classify the text data at hand. This category of approaches need in-depth domain expertise, along with needing more time for seamless compilation, and throwing more challenges when it comes to scaling.
Machine Learning Approaches
Machine Learning is used for training models on large text datasets to categorize new text data. Models are predominantly trained by a process called ‘feature extraction’ which largely involves transforming text data into numeric data. The most common machine learning algorithms you can use for text classification includes Deep Learning Algorithms, Vector Machine Support, and Naive Bayes Classifiers.
Hybrid Approaches
The Hybrid approaches combine both rule-based and machine learning techniques to determine classifiers that can be calibrated in specific situations/use cases.
Top Examples/Use Cases of Text Classifications
Sentiment Analysis
Sentiment Analysis refers to the process of analyzing and determining whether a given piece of text data is written with a positive or negative approach towards the subject of the text data.
Language Detection
Language Detection refers to the process of analyzing and identifying the language in a given set of text data.
Topic Detection
Topic Detections refers to the process wherein the theme or topic on any given text data is analyzed and determined.
Urgent Issue Detection
Text Classification helps a large number of businesses comprehend and make sense of huge volumes of dynamic data by implementing specific technologies that include aspect-based sentiment analysis to determine what people are talking about more.
Essential Resources for Successful Text Classification
Datasets
A text classifier needs accurate and sensible training data to generate the desired results and Machine Learning algorithms can make accurate predictions by only learning from examples of similar data. Hence, when an algorithm comes across examples of previously and accurately tagged datasets, it becomes more capable to accurately make predictions on text datasets that the algorithm is not familiar with. Hence, datasets and training data are the fundamentals of successful and accurate Text Classification.
The most popular and publicly available datasets for Topic Classification would include Reuters News Dataset and 20 Newsgroups. For sentimental analysis, the main datasets include Amazon Product Reviews, IMDB Reviews, Twitter Airline Sentiment, etc. And Other common dataset examples would be Spambase, SMS Spam Collection, Hate Speech & offensive Language.
Tools
To feed your trained data into a Machine Learning algorithm, you will need to create a text classifier, but to do that, you will need to use some text classification tools. The tools are your resources that will help you nail your job.
The Text Classifications come under two main categories, namely a) Open-Source Libraries, and b) SaaS APIs. The open source libraries help perform text classification more efficiently but it demands a lot of in-depth experience in data science and computer engineering.
Whereas, the SaaS APIs require very little coding and are also highly scalable, which make it the more preferred between the latter and former. The most popular SaaS APIs for text Classification in Natural Language Processing would include MonkeyLearn, IBM Watson, Amazon Comprehend, Lexalytics, Google Cloud NLP, Aylien, & MeaningCloud.
Wrapping Up!
AIW is one of the best ML & AI-enabling organizations that comes with multilingual and cross-trained professionals who understand language, understands your syntax, and can help you to accelerate and boost your NLP solutions besides data annotation and other related processes.
