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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.
The term text analytics also describes that application of text analytics to respond to business problems, whether independently or in conjunction with query and analysis of fielded, numerical data. It is a truism that 80% of business-relevant information originates in unstructured form, primarily text. [9]
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 tasks. [1] Medallia – offers one system of record for survey, social, text, written and online feedback.
Relate unstructured text with structured data such as dates, numbers or categorical data for identifying temporal trends or differences between subgroups or for assessing relationship with ratings or other kind of categorical or numerical data. Visualization tools to visualize and interpret text analysis results: Dendrogram with optional bar chart
The application of sophisticated linguistic analysis to news and social media has grown from an area of research to mature product solutions since 2007. News analytics and news sentiment calculations are now routinely used by both buy-side and sell-side in alpha generation, trading execution, risk management, and market surveillance and compliance.
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 ]
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.
Voyant "was conceived to enhance reading through lightweight text analytics such as word frequency lists, frequency distribution plots, and KWIC displays." [3] Its interface is composed of panels which perform these varied analytical tasks. These panels can also be embedded in external web texts (e.g. a web article could include a Voyant panel ...