SAS Text Miner is a powerful analytics suite for extracting insights from unstructured text. Looking for other robust text‑analysis platforms that can handle preprocessing, mining, modeling, and visualization? Below are 20 alternatives to consider.
Get targeted exposure with custom position pinning and highlighted placement.
Open‑source data science platform with drag‑and‑drop operators for text preprocessing, sentiment analysis, topic modeling, and machine‑learning pipelines.
Modular, visual workflow tool offering extensive text‑mining extensions, including tokenization, TF‑IDF, word embeddings, and integration with Python/R.
Cloud‑based AI service that extracts entities, keywords, sentiment, emotion, and categories from text with pre‑trained models and custom model support.
Suite of REST APIs for language detection, key phrase extraction, sentiment analysis, and named‑entity recognition, easily integrated into Azure pipelines.
Scalable API offering syntax analysis, entity extraction, sentiment scoring, and content classification for multilingual text.
Self‑service analytics platform with built‑in text‑mining tools for parsing, sentiment, topic modeling, and predictive modeling within a visual workflow.
Open‑source visual programming suite featuring text mining widgets for preprocessing, word clouds, TF‑IDF, and clustering.
Qualitative data analysis software that supports coding, content analysis, and mixed‑methods text mining with statistical reporting.
Enterprise‑grade text analytics engine offering sentiment, intent, taxonomy, and theme extraction with on‑premise and cloud deployment options.
No‑code platform for building custom text classifiers and extractors; includes pre‑trained models for sentiment, keyword extraction, and topic tagging.
High‑performance open‑source Python library for industrial‑strength NLP, providing tokenization, POS tagging, dependency parsing, and named‑entity recognition.
Comprehensive Python library for teaching and prototyping NLP tasks such as tokenization, stemming, classification, and corpus handling.
Python library focused on topic modeling and similarity detection, offering implementations of LDA, Word2Vec, Doc2Vec, and more.
Simple Python library built on NLTK and Pattern, providing easy‑to‑use APIs for sentiment analysis, noun phrase extraction, and translation.
Fully managed service that uses machine learning to find insights and relationships in text, including sentiment, entities, key phrases, and language detection.
SAS’s newer cloud‑native offering that extends SAS Text Miner with interactive visualizations, model building, and integration with SAS Viya.
API‑first text analytics platform delivering sentiment, topic extraction, classification, and multilingual support with customizable models.
Customer experience analytics suite that processes unstructured feedback, delivering sentiment, intent, and root‑cause analysis.
Comprehensive predictive modeling environment that includes a text‑mining node set for preprocessing, feature extraction, and model scoring.
Automated machine‑learning platform that adds text‑data handling, allowing users to upload documents and automatically build NLP models.