Text classification, also known as text categorisation, is one of the fundamental tasks in Natural Language Processing (NLP). It involves assigning text to categories or tags, making it a powerful tool for processing the vast amount of unstructured data available in social media, emails, and chats. Businesses are increasingly adopting text classification to structure data effectively and ease the decision-making process.
Applications of Text Classification
Text classification is primarily used for:
- Sentiment Analysis: Analysing the tone, intent, and emotion behind a text.
- Spam Detection: Classifying emails as spam or non-spam.
- Language Detection: Identifying the language from a given text.
- Topic Categorisation: Categorising text based on themes or topics.
The most common ways to use text classification are through SaaS APIs or open-source libraries, which we will explore in this article.
Text Classification Using Open-Source Libraries
The availability of numerous libraries has made machine learning a preferred option for developers. If you’re a developer with machine learning knowledge, these libraries can help you develop text classifier models. Below are the best libraries in popular programming languages like Python, R, and Java, which provide extensive machine-learning tools for improving workflow and efficiency.
Top 10 Best Text Categorisation Libraries for Machine Learning
- NLTK: First up is NLTK. It is the most popular open-source library for Python that provides a collection of various algorithms in the natural languages. It has a well-known open-source and widespread online community which is widely known for its ease of use. NLTK offers sentiment analysis, thematic segmentation, tokenisation and recognition of named entities all under the same roof by directing the program to understand, process and then execute the written text, making it one of the best tools for text categorisation.
- Spacy: Next up is Spacy! Spacy is the second most popular open-source software library which is written in Python and Cython. It is the second most popular NLP library after NLTK. Spacy has an active open-source community making it engaging for users to share resources. It offers tokenisation, part-of-speech tagging, dependency parsing, lemmatisation, named entities recognition and Sentence Boundary Detection.
- Scikit-learn: Scikit-learn, also known as sklearn, is an open-source machine-learning library for Python. Though Scikit-learn is majorly written in the Python programming language, a few other algorithms are written in Cython to ensure maximum performance and weightlifting. It uses the Numpy library for Python to execute linear algebraic operations. It consists of various algorithms for clustering, classification and regression like Gradient boosting, random forest, SVM and DBSCAN.
- Naive Bayes: Next up is Naive Bayes! Now, Naive Bayes is one of the future-ready algorithms in ML, that work in accordance with the “Bayes Theorem”. The Naive Bayes classifier executes classification based on how many times a set of events is usually to occur. Its widest use is for Text classification due to its user-friendly algorithm which is a reliable source for undergoing text classifiers. It requires less training time and less training data, i.e., less CPU and Memory consumption.
Here’s a tutorial on how to classify text using Naive Bayes: Watch the tutorial
- Caret for R: Caret is an NLP package for the R programming language that can build machine learning models effectively. It stands for “Classification and Regression Training”. It consists of various functions that can be used for complex regression and classification difficulties. This package is capable of creating predictive models and has various other tools that can be used for text classification like splitting data, pre-processing, model-tuning, resampling and variable importance estimation.
- TensorFlow: TensorFlow is a free open-source software library for programming diverse tasks in machine learning. It provides a wide range of tools and libraries that are really flexible and easy to use. It has a wide community for sharing resources that allow users to build and engage machine learning applications. TensorFlow provides a dynamic way of categorizing text using NLP. You can use pre-canned estimators to build baselines, use convolution and LSTM layers to build custom estimators and compare or evaluate models using TensorBoard.
- LingPipe: LingPipe is an amazing library for Java that can be used for categorizing text with the help of computational linguistics. It can classify search results from Twitter in different categories, check the queries for any kind of spelling error and locate the identity of a citizen, administration or location. LingPipe has an advanced classification model that takes up text classification models generated by the user and further classifies the document using the information gathered from the language models. There are furthermore ways to generate text classifier models, language models being one of them.
- Apache OpenNLP: Apache OpenNLP is a machine learning library which is written in Java, commonly used for NLP activities like understanding the intent behind a piece of text, tokenisation, segmenting sentences, entity extraction and implementation of text classifiers which can further ease the task in hand. The Apache OpenNLP library consists of a wide list of tools in order to automate the workflow and is a must for your open-source checklist.
- MALLET: MALLET is an NLP package for Java that is primarily used for text classification, extracting information, clustering and similar machine learning uses on text. It includes a wide range of algorithms and codes for determining the performance of the classifier using several metrics and various tools that are useful for classifying the document. MALLET also consists of tools that are used for various other applications such as extracting the named entity from text.
- JaTeCS: JaTeCS is an open-source library for Java that supports the analysis of automatic text categorisation, quantification and ordinal regression in open mining applications. Similar to other ML frameworks, it provides different NLP tools, weighting and feature selection methods for external software like libSVM or SVM light. There are various tools for analyzing text that include common lexical and text corpora resources, NLP tools, language support and weighting methods.
Conclusion
If you are working on Python, the best user-friendly and active library you can make use of is NLTK and then comes Spacy. Both of them have an active community of like-minded programmers, where you can share queries with others and gain information about the coding aspect. Other than that, if you are more familiar with “R”, then give “Caret” a try! Naive Bayes is a classifier that can be used throughout Python, R and Java. If you are more familiar with Java, Apache OpenNLP is a great library for NLP and text analysis.
Open-source libraries are the most preferable way in machine learning and NLP, but it is ideal for programmers and developers who have a background in coding and machine learning. If you’d prefer to get started with a more generic framework, without really getting under the hood, here’s our pick of the top 10 best SaaS/MaaS text classification APIs for businesses.
References
- Esuli, A., & Fagni, T. (2009). JaTeCS: an open-source Java TExt Categorisation system. Semantic Scholar
- Data Science and Machine Learning Tutorials. (2019). TensorFlow 2.0 Data Transformation for Text Classification. Towards Data Science
- Kaggle. (n.d.). Text Classification Using Spacy. Kaggle
- MonkeyLearn. (n.d.). Text Classification APIs. MonkeyLearn Blog
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