"Data is the key to business success" is a statement well grabbed by small to medium and big organizations across the globe. Unfortunately, no company can sustain the digital blow without the help of sufficient information about its customers, employees, and other stakeholders. While on the mission to acquire insights, businesses come across all kinds of structured and unstructured data from various sources. Remarkably, text analysis, text mining, and text analytics play a big role in abstracting decision-making information. They stand out from other forms of resources like videos, images, and documents because oftentimes, customers write their opinions, reviews, and feedback in form of text after using the products or services. As companies came to know about the importance of structured text data, they started using software for text analysis, text mining, and text analytics. The technologies provide statistical pattern learning to find patterns and trends from text data. Analytics Insight has listed the top 10 software for text analysis, analytics, and mining that is changing the way text data is used.
DiscoverText delivers powerful enterprise text analytics to staff, students, and researchers in an easy-to-use and affordable way. The software leverages dozens of multilingual, text mining, data science, human annotation, and machine learning features. To make users quickly and accurately evaluate large amounts of text data, DiscoverText also offers a range of simple to advanced cloud-based software tools. The software provides access and sorting options to its customers from the unstructured text on market research, and associated metadata found in customer feedback platforms, emails, large-scale surveys, social media, and other forms of text data.
Watson Discovery is an enterprise search tool and artificial intelligence search technology that breaks open data silos and retrieves specific answers to consumers' questions while analyzing trends and relationships buried in enterprise data. The platform is well trained in the language of customers' domain and applies machine learning technologies to process text. With Watson Discovery, users can ingest, normalize, enrich, and search unstructured data with speed and accuracy.
RapidMiner is a powerful data mining tool that enables everything from text mining, text analysis, and text analytics to model deployment and model operations. The platform brings artificial intelligence to the enterprise through an open and extensible data science platform. Built with a special focus for the data team, RapidMiner unifies the entire data science lifecycle from data preparation to predictive model deployment. At RapidMiner, every analysis is a process, each transformation or analysis step is an operator, making design fast, easy to understand, and fully reusable.
Cloud Natural Language API provides natural language understanding technologies to developers, including text analysis, text mining, text analytics, sentiment analysis, entity analysis, content classification, and syntax analysis. At the platform, natural language uses machine learning to reveal the structure and meaning of the text. Users can extract information about people, places, and events, and better understand social media sentiment and customer conversations. The natural language provision in Cloud Natural Language API allows users to analyze text and also integrate it with their document storage on Cloud Storage.
Microsoft's Azure Cognitive Services bring artificial intelligence within reach for every developer, without requiring machine learning expertise. The software embeds the ability to see, hear, speak, search, understand, and accelerate decision-making into users' apps. Azure Cognitive Services work across all programming platforms and languages and help incorporate AI functionality into various applications with minimal effort and coding.
Bismart Folksonomy is a next-generation tagging system that allows users to mine their datasets and gives them the information they want to acquire in an instant. The software highlights tags from natural language texts, images, videos, and audio files to help locate specific its. The advanced analytics feature in Folksonomy transforms non-structured files into structured ones, delivering insights. Bismart's Folksonomy has special features like a user-friendly tool to merge synonyms, a white list to separate homonyms, the ability to create technical and customized dictionaries, and a blacklist to reduce tags.
Apache OpenNLP is an open-source natural language processing Java library. It leverages text analysis features like sentence detection, tokenizing, named entity recognition, part-of-speech tagging, lemmatization, chunking, and language detection. Apache OpenNLP product allows users to solve complex tasks of preparing and classifying text using machine learning methods. The project was developed by volunteers and is always looking for new contributors to work on all parts.
TAMS, or Text Analysis Markup System, is an open-source program that allows users to quickly code sections of text. It is a convention for identifying themes in texts like web pages, interviews, and field notes. Although TAMS is most often employed to encode ethnographic documents such as interviews, users may also use this program to analyze pretty much any plain text. The platform leverages users to encode sections of texts in order to make them searchable within their corpus.
KNIME Text Processing was designed and developed to read and process textual data and transform it into numerical data in order to apply regular KNIME data mining nodes. Some of the features of the platform include natural language processing, text mining, and information retrieval. KNIME Text Processing imports textual data, processes documents by filtering and stemming, transform documents into a bag of words and document vectors, and finally cluster the documents based on their numerical representation.
Gensim is an open-source library for unsupervised topic modeling and natural language processing, using modern statistical machine learning. It uses top academic models and modern statistical machine learning to perform complex tasks such as building documents or word vectors, corpora, performing topic identification, providing document comparison, and analyzing plain-text documents for semantic structure.
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