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Sentiment analysis (also known as opinion mining) 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.
Applications, Methods and features & Types
Explore the main themes, entities and connections around Sentiment analysis. Start with the topic map, then use the sections below for research and deeper semantic analysis.
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Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.
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sentiment analysis text subjective words positive data features negative based one objective methods also learning may information language task level
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| Sentiment analysis | is a | possibility to capture nuances about objects of interest | 0.90 | text |
| reviews | instance of | and study affective states and subjective information.Sentiment analysis is widely applied to voice of the customer materials | 0.80 | text |
| survey responses | instance of | and study affective states and subjective information.Sentiment analysis is widely applied to voice of the customer materials | 0.80 | text |
| online | instance of | and study affective states and subjective information.Sentiment analysis is widely applied to voice of the customer materials | 0.80 | text |
| social media | instance of | and study affective states and subjective information.Sentiment analysis is widely applied to voice of the customer materials | 0.80 | text |
| and healthcare materials for applications that range from marketing to customer service to clinical medicine | instance of | and study affective states and subjective information.Sentiment analysis is widely applied to voice of the customer materials | 0.80 | text |
| enjoyment | instance of | at emotional states | 0.80 | text |
| anger | instance of | at emotional states | 0.80 | text |
| disgust | instance of | at emotional states | 0.80 | text |
| sadness | instance of | at emotional states | 0.80 | text |
| fear | instance of | at emotional states | 0.80 | text |
| and surprise.Precursors to sentimental analysis include the General Inquirer | instance of | at emotional states | 0.80 | text |
These clusters group vocabulary that occurs around closely connected concepts in the source material.
Bridges can reveal useful research angles that are easy to miss in a flat list of related terms.