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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.
Topic modeling to extract the main themes using NNMF and Factor Analysis. Correspondence analysis in order to identify words or concepts (or content categories) associated with any categorical meta-data associated with documents. Pre-and post-processing with R and python script; Analyze more than 70 languages including Chinese, Japanese, Korean ...
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 ...
[2] [3] PolyAnalyst includes features for text clustering, sentiment analysis, extraction of facts, keywords, and entities, and the creation of taxonomies and ontologies. Polyanalyst supports a variety of machine learning algorithms, as well as nodes for the analysis of structured data and the ability to execute code in Python and R.
The Natural Language Toolkit, or more commonly NLTK, is a suite of libraries and programs for symbolic and statistical natural language processing (NLP) for English written in the Python programming language. It supports classification, tokenization, stemming, tagging, parsing, and semantic reasoning functionalities. [4]
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.
Multimodal sentiment analysis is a technology for traditional text-based sentiment analysis, which includes modalities such as audio and visual data. [1] It can be bimodal, which includes different combinations of two modalities, or trimodal, which incorporates three modalities. [ 2 ]
Spark NLP for Healthcare is a commercial extension of Spark NLP for clinical and biomedical text mining. [10] It provides healthcare-specific annotators, pipelines, models, and embeddings for clinical entity recognition, clinical entity linking, entity normalization, assertion status detection, de-identification, relation extraction, and spell checking and correction.