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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.
WordStat is a content analysis and text mining software. [1] It was first released in 1998 after being developed by Normand Peladeau from Provalis Research.The latest version 9 was released in 2021.
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 ]
In this paper, we propose a systematic and automatic approach to technical pattern recognition using nonparametric kernel regression, and apply this method to a large number of U.S. stocks from 1962 to 1996 to evaluate the effectiveness of technical analysis. By comparing the unconditional empirical distribution of daily stock returns to the ...
Embedding vectors created using the Word2vec algorithm have some advantages compared to earlier algorithms [1] such as those using n-grams and latent semantic analysis. GloVe was developed by a team at Stanford specifically as a competitor, and the original paper noted multiple improvements of GloVe over word2vec. [9]
For example, his paper with Poria S, Cambria E, Howard N, and Huang G-B, on "Fusing audio, visual and textual clues for sentiment analysis from multimodal content", published in (Elsevier) Neurocomputing 174: 50-59 (2016), is an ISI highly cited paper.
Consumer Sentiment Index 1952 - 2022. The University of Michigan Consumer Sentiment Index is a consumer confidence index published monthly by the University of Michigan. The index is normalized to have a value of 100 in the first quarter of 1966. [1] Each month at least 500 telephone interviews are conducted of a contiguous United States sample ...
Hierarchical latent tree analysis is an alternative to LDA, which models word co-occurrence using a tree of latent variables and the states of the latent variables, which correspond to soft clusters of documents, are interpreted as topics. Animation of the topic detection process in a document-word matrix through biclustering. Every column ...