Top 10 Best Open-Source Text Categorization Libraries in Machine Learning
October 10, 2020
Text classification, also known as text categorization, is one of the fundamental tasks used in Natural Language Processing (NLP). It is used to assign the text based on categories and tags. There is a vast pool of data out there in the form of responses, social media, email, chat and most of it being unstructured and unprocessed, make it hard for business to accurately measure their responses in a chronological manner. Thus, text classification is being widely adopted by businesses throughout the world to find an effective way to analyze the data in a structured and insightful way, and thus ease the decision-making process furthermore!
Text classification is primarily used for:
- Analyzing the tone, the intent and the emotion behind a text by using sentiment analysis
- Classifying emails as spam or non-spam
- Detecting the language from a given piece of text
- Categorizing text on the basis of the theme or the topic
The most common way to use text classification is either by using Saas APIs or open-source libraries which we will discuss in this article.
Text Classification using open-source libraries
It is because of the countless number of libraries that machine learning has become the preferred and the most conventional option for developers. If you are a developer, having prior knowledge in machine learning will be ideal in order to develop text classifier models. In this article, I will try to breakdown the best libraries that will help you along your data journey and provide insight that will help you at every step. The list includes ML (Machine Learning) libraries from the best programming languages — Python, R and Java which provide an extensive list of machine learning libraries for improving the workflow and efficiency.
The top 10 best text categorization libraries for Machine Learning:
- NLTK
- Spacy
- Scikit-learn
- Naive Bayes
- Caret for R
- TensorFlow
- LingPipe
- Apache OpenNLP
- Mallet
- JaTeCS
1. 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, tokenization 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 categorization.
2. 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 tokenization, part-of-speech tagging, dependency parsing, lemmatization, named entities recognition and Sentence Boundary Detection.
3. 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 in order to execute linear algebraic operations. It consists of various algorithms for clustering, classification and regression like Gradient boosting, random forest, svm and DBSCAN.
4. 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. https://www.youtube.com/watch?v=EGKeC2S44Rs&t=139s
5. 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.
6. 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 allows the 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.
7. 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 locations. 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 further more ways to generate text classifiers models, language models being one of them.
8. 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, tokenization, 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.
9. 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 metrices 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.
10. JaTeCS: JaTeCS is an open-source library for Java that supports analysis on automatic text categorization, 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 softwares 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, then comes Spacy. Both of them have an active community of likeminded 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 friendly 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.
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