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Mathematica – provides built in tools for text alignment, pattern matching, clustering and semantic analysis. See Wolfram Language, the programming language of Mathematica. MATLAB offers Text Analytics Toolbox for importing text data, converting it to numeric form for use in machine and deep learning, sentiment analysis and classification ...
Sentiment analysis (also known as opinion mining or emotion AI) is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information.
Sentiment Analysis: assigns a polarity (positive, negative, neutral) to a document or to the individual topics or attributes appearing in a document (aspect-based sentiment). Text Clustering: discovers the underlying themes in a document collection and groups these documents according to their similarities and their adherence to those themes.
Relate unstructured text with structured data such as dates, numbers or categorical data for identifying temporal trends or differences between subgroups or for assessing relationship with ratings or other kind of categorical or numerical data. Visualization tools to visualize and interpret text analysis results: Dendrogram with optional bar chart
Lexalytics, Inc. provides sentiment and intent analysis to an array of companies using SaaS and cloud based technology. [1] [2] Salience 6, the engine behind Lexalytics, was built as an on-premises, multi-lingual text analysis engine. It is leased to other companies who use it to power filtering and reputation management programs.
Clarabridge offers customer experience software as a service (SaaS) using AI-powered text and speech analytics. The data typically used for analysis comes from social media (such as Facebook, Twitter, or review sites), call center notes, email, chats, and surveys.
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