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  2. Sentiment analysis - Wikipedia

    en.wikipedia.org/wiki/Sentiment_analysis

    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.

  3. Multimodal sentiment analysis - Wikipedia

    en.wikipedia.org/wiki/Multimodal_sentiment_analysis

    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]

  4. Natural language processing - Wikipedia

    en.wikipedia.org/wiki/Natural_language_processing

    Natural language processing (NLP) is a subfield of computer science and especially artificial intelligence.It is primarily concerned with providing computers with the ability to process data encoded in natural language and is thus closely related to information retrieval, knowledge representation and computational linguistics, a subfield of linguistics.

  5. List of text mining software - Wikipedia

    en.wikipedia.org/wiki/List_of_text_mining_software

    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 ...

  6. Lexalytics - Wikipedia

    en.wikipedia.org/wiki/Lexalytics

    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.

  7. Word embedding - Wikipedia

    en.wikipedia.org/wiki/Word_embedding

    The use of multi-sense embeddings is known to improve performance in several NLP tasks, such as part-of-speech tagging, semantic relation identification, semantic relatedness, named entity recognition and sentiment analysis. [38] [39] As of the late 2010s, contextually-meaningful embeddings such as ELMo and BERT have been developed. [40]

  8. 10 Critical Steps to Writing ChatGPT Prompts for Beginners - AOL

    www.aol.com/10-critical-steps-writing-chatgpt...

    9. Build a custom GPT. If you have a paid ChatGPT plan, you can build custom GPTs that carry out specific actions. For example, if you regularly need to turn a topic into social media captions ...

  9. Category:Natural language processing - Wikipedia

    en.wikipedia.org/wiki/Category:Natural_language...

    Semantic analysis (computational) Semantic analytics; Semantic compression; Semantic decomposition (natural language processing) Semantic folding; Semantic interpretation; Semantic neural network; Semantic space; SemEval; Sentence embedding; Sentence extraction; Sentiment analysis; Seq2seq; Sketch Engine; Sparrow (chatbot) Speech segmentation ...