Text classification
Through content analysis and the assignment of a set of predetermined tags or categories, text classification also referred to as text tagging or text categorization, is the process of automatically grouping material into structured groupings. Assigning a label or text to a whole text from a limited number of classes is another way to think of it. Classifying text documents automatically using pre-trained categories is the aim of text classification. This procedure makes it simple for companies to automate business procedures and get insights from data. It connects structured and unstructured data, allowing machines to sort huge amounts of text into useful categories.
Text classification is a major Natural language processing (NLP) challenge. It serves as a fundamental building component for more complex NLP activities. A large number of language processing jobs can be thought of as categorisation tasks.
Text Classification Applications
Many practical uses for text classification exist, such as:
- Sentiment analysis: classifying the sentiment of a text as neutral, negative, or positive. This aids in comprehending consumer ideas and feedback.
- Document classification is the automatic assignment of subjects or groups to documents, which improves the structure and retrieval of content. Examples include classifying news articles according to their subject codes or determining whether a research paper is about epidemiology or embryology.
- By evaluating the content and features of emails or messages, spam detection improves communication security by identifying and removing spam.
- Topic Labelling: Giving documents a topic or category automatically.
- Language identification is a useful technique for multilingual content processing that involves identifying the language in which a piece is written.
- Authorship Attribution: Identifying the author of a piece or other details about the author, including age or gender. Forensic linguistics, social sciences, and digital humanities are all affected by this.
- Putting newswire items into categories to make information retrieval easier is known as newswire indexing.
- Sorting resumes according to qualifications, experience, and skills is known as resume screening.
- Recommending pertinent blogs, articles, or goods to people in accordance with their interests is known as content recommendation.
- Understanding the intent underlying search queries is necessary to classify user intent.
- identifying whether a text discusses sports, is sarcastic, positive, negative, trustworthy, or reflects a predetermined set of intentions.
- Reviewing digitised medical records.
Approaches to Text Classification:
Generally speaking, there are three major types of text classification techniques:
Rule-based systems use a set of carefully developed linguistic rules to divide texts into structured categories. Terms like “Donald Trump” and “Boris Johnson,” for instance, may classify a paragraph as “politics.” These systems could be brittle and have trouble adjusting to new information.
Machine Learning Techniques: These systems use historical observations from labelled datasets to learn how to classify data. User data is tagged as test and train data beforehand. You can utilise algorithms like maximum entropy and conditional random fields. Bag of Words techniques are frequently used in machine learning methodologies.
Hybrid Systems: These integrate machine learning with rule-based techniques, employing the former to detect more complex things and the latter to swiftly identify easily identifiable ones.
Steps Involved in Text Classification:
Several phases are involved in the general pipeline for developing text classification systems:
Step 1
Data Preparation: Gathering a tagged dataset in which every text item belongs to a specific category.
Step 2
Pre-processing is the process of getting the text data ready by handling missing values, deleting stop words, stemming or lemmatising, and tokenising it. Text segmentation and document triage which involves transforming digital data into clearly defined text documents with features like language and character encoding identification and text sectioning can also be included in text preprocessing. An essential component of this step is defining what a “word” is.
Step 3
Text input can be transformed into a numerical representation that machine learning models can comprehend through the process of feature extraction. Common methods include of:
- Word order is ignored when representing text using the Bag of Words (BoW), which is a vector of word frequencies.
- The bag of N-grams records the frequency of n-word sequences.
- Words are given weights according to their significance in a document in relation to a corpus using a technique called TF-IDF (Term Frequency-Inverse Document Frequency).
- Semantic and syntactic vector representations of words are called word embeddings.
- Text is converted to a word count matrix using CountVectorizer.
- The Hashing Vectorizer is a tool for large-scale text classification that uses a hashing method to map words to a fixed-size feature space.
Step 4
A classification algorithm (such as Naive Bayes, Logistic Regression, Support Vector Machine, Decision Trees, or Neural Networks) is selected and trained on the prepared data and features. This process is known as model training. Based on the training examples, this entails determining a function that will give unseen examples the most accurate label possible.
Step 5
Evaluation: Examining the trained classifier’s performance on a different test dataset using measures like F-measure, accuracy, precision, and recall. Cross-validation methods can be applied, particularly in situations when there is a shortage of labelled data.
Classification Algorithms Used in Text Classification:
Text categorisation can be done using a variety of classification algorithms:
- Based on Bayes’ theorem, the Naive Bayes classifier is a probabilistic algorithm that makes the conditional independence and bag-of-words assumptions.
- One linear model that may be applied to either binary or multi-class classification is logistic regression.
- A strong algorithm that determines the best hyperplane to divide classes is the Support Vector Machine (SVM).
- Decision Trees: Tree-like structures that categorise text based on a sequence of decisions.
- Maximum Entropy Classifiers: A probabilistic model that seeks to satisfy the restrictions provided by the training data while identifying the probability distribution with the maximum entropy.
- One kind of linear classifier and a fundamental component of neural networks are perceptrons.
- k-Nearest Neighbour (k-NN): A non-parametric technique that groups documents according to the feature space’s k nearest neighbours’ majority class.
- By identifying local dependencies, Convolutional Neural Networks (CNNs) are deep learning models that have demonstrated efficacy in text classification.
- LSTMs and other recurrent neural networks (RNNs) are neural network topologies that can capture long-range dependencies and are appropriate for sequential data, such as text.
- Large, Pre-Trained Language Models: Text classification at the cutting edge has been accomplished by models such as BERT.
Evaluation of Text Classification:
The percentage of assessment instances that are successfully identified is known as accuracy, and it is the most basic evaluation metric for text categorisation. Recall, accuracy, and F-measure are further crucial metrics. A more accurate evaluation of the classifier’s performance can be obtained by using K-fold cross-validation when there is a limited quantity of labelled data available.
Interpreting Text Classification Models:
Utilising methods such as Integrated Gradients, LIME (Local Interpretable Model-agnostic Explanations), ELI5 (Explain Like I’m 5), and Saliency Maps, which highlight significant words or phrases in the input text, one can learn why a text classifier produced a specific prediction.
Relationship with Other NLP Tasks:
Other NLP tasks are similar to text categorisation:
- Part-of-Speech (POS) tagging assigns grammatical categories to each document word. Assigning POS tags to words categorises them.
- WSD is the process of determining a word’s meaning in a given context. WSD classifies word instances by sense.
- Information extraction (IE) forms organised data from unstructured text. Text classification can be incorporated into Internet Explorer pipelines.
- Sentiment analysis: As previously stated, this particular usage of text categorisation aims to determine a text’s emotional tone.
- Dividing text into meaningful components, such phrases or themes, is known as text segmentation. It is possible to identify segment boundaries by using text categorisation.
In conclusion, text classification is a basic problem in Natural language processing (NLP) that has many uses. Using a variety of techniques, such as rule-based systems, machine learning algorithms, and hybrid approaches, it entails automatically classifying text into predetermined categories. Usually, the procedure consists of pre-processing, feature extraction, model training, evaluation, and data preparation. Effectively assessing and drawing conclusions from the enormous volumes of textual data available requires an understanding of text classification.
How is text classification implemented?
These procedures will help you create, train, and implement a text classification model.
Create a training dataset
When training or optimising a language model for text categorisation, it is crucial to prepare a high-quality dataset. The model may effectively learn to recognise particular words, phrases, or patterns and their corresponding categories when it is given a diverse and labelled dataset.
Get the dataset ready
Raw datasets are insufficient for machine learning algorithms to learn from. Consequently, you need to use preprocessing techniques like tokenisation to clean and prepare the dataset. Each word or sentence is broken up into smaller units called tokens by tokenisation.
Because tokenisation may impact model performance, you should exclude duplicate, redundant, and anomalous data from the training dataset. The dataset is subsequently divided into training and validation data.
Develop the model for text classification
Select and use the provided dataset to train a language model. The model attempts to categorise text into its appropriate groups while learning from the annotated dataset. When the model regularly converges to the same result, training is over.
Assess and improve
Use the test dataset to evaluate the model. Examine the model’s F1 score, recall, accuracy, and precision against predetermined standards. To fix overfitting and other performance problems, the trained model might need to be further adjusted. Continue to refine the model until the desired outcomes are obtained.
What advantages can text classification offer?
Text classification models are used by organisations for the reasons listed below.
Boost precision
Text classification algorithms require little to no more training to accurately classify text. They assist organisations with overcoming human mistake that may occur during the manual classification of textual data. Furthermore, when it comes to labelling text data on a variety of themes, a text classification system is more reliable than people.
Deliver analytics in real time
When processing text data in real time, organisations are under time pressure. You may provide quick answers and extract useful insights from unprocessed data by using text categorisation algorithms. Text classification systems, for instance, can be used by businesses to evaluate client feedback and promptly address urgent demands.
Tasks involving text classification at scale
In the past, organisations have classified documents using manual or rule-based methods. These techniques are resource-intensive and sluggish. You can more successfully extend document categorisation efforts across departments to promote organisational growth with machine learning text classification.
Translate between languages
Text classifiers can be used by organisations to detect language. In discussions or service requests, a text classification model can identify the language of origin and route the request to the appropriate team.