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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.
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]
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 ...
MeTA is a modern C++ data sciences toolkit featuringL text tokenization, including deep semantic features like parse trees; inverted and forward indexes with compression and various caching strategies; a collection of ranking functions for searching the indexes; topic models; classification algorithms; graph algorithms; language models; CRF ...
Word2vec is a technique in natural language processing (NLP) for obtaining vector representations of words. These vectors capture information about the meaning of the word based on the surrounding words. The word2vec algorithm estimates these representations by modeling text in a large corpus.
BERT is meant as a general pretrained model for various applications in natural language processing. That is, after pre-training, BERT can be fine-tuned with fewer resources on smaller datasets to optimize its performance on specific tasks such as natural language inference and text classification , and sequence-to-sequence-based language ...
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]
The overarching goal is, essentially, to turn text into data for analysis, via the application of natural language processing (NLP), different types of algorithms and analytical methods. An important phase of this process is the interpretation of the gathered information.