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The following natural-language processing toolkits are notable collections of natural-language processing software. They are suites of libraries , frameworks , and applications for symbolic, statistical natural-language and speech processing.
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.
Rosette comes as a cloud (public or on-premise) deployment or Java SDK. [8] Rosette provides a variety of natural language processing tools for unstructured text: language identification, base linguistics, entity extraction, name matching, name translation, sentiment analysis, semantic similarity, relationship extraction, topic extraction, categorization, and Arabic chat translation. [9]
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.
Spark NLP is licensed under the Apache 2.0 license. The source code is publicly available on GitHub as well as documentation and a tutorial. Prebuilt versions of Spark NLP are available in PyPi and Anaconda Repository for Python development, in Maven Central for Java & Scala development, and in Spark Packages for Spark development.
It is used in natural language processing and information retrieval (IR). It disregards word order (and thus most of syntax or grammar) but captures multiplicity . The bag-of-words model is commonly used in methods of document classification where, for example, the (frequency of) occurrence of each word is used as a feature for training a ...
Multimodal sentiment analysis also plays an important role in the advancement of virtual assistants through the application of natural language processing (NLP) and machine learning techniques. [5] In the healthcare domain, multimodal sentiment analysis can be utilized to detect certain medical conditions such as stress, anxiety, or depression. [8]
Maynard has been a researcher associated with the General Architecture for Text Engineering (GATE) project at Sheffield since 2000. [1] Her research with the project includes the development of the Java Annotation Patterns Engine (JAPE) for using regular expressions to process annotations, as well as research on information extraction and sentiment analysis.