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Sentiment analysis, also referred to as opinion mining, is an approach to natural language processing (NLP) that identifies the emotional tone behind a body of text. This is a popular way for organizations to determine and categorize opinions about a product, service, or idea. It involves the use of data mining, machine learning (ML) and artificial intelligence (AI) to mine text for sentiment and subjective information.Sentiment analysis systems help organizations gather insights from unorganized and unstructured text that comes from online sources such as emails, blog posts, support tickets, web chats, social media channels, forums and comments.
Artificial intelligence can recognize particular objects in images, videos, or others with the guidance of specified parameters.
Facial recognition software embedded in most modern smartphones is the simplest example of this technology in action. It memorizes the face of an authorized owner and compares it to the one in front of the camera to unlock the smartphone.
• Automotive industry This technology maintains self-driving cars’ capability of recognizing objects around them and making safety-first decisions depending on the environment.
• Security and surveillance Private enterprises use object, face, and retina recognition technologies embedded in smart cameras for public safety.
• Agriculture. Robotic tools empowered with computer vision can distinguish seedlings from weeds.
• Retail Smart cameras with a visual object recognition feature can detect customers and track their behavior in a store. Such insights are used to create better product and shelf placements.
Data analytics is the science of analyzing raw data to make conclusions about that information. Many of the techniques and processes of data analytics have been automated into mechanical processes and algorithms that work over raw data for human consumption.
Data analytics is broken down into four basic types.
1. Descriptive analytics: This describes what has happened over a given period of time. Have the number of views gone up? Are sales stronger this month than last?
2. Diagnostic analytics: This focuses more on why something happened. This involves more diverse data inputs and a bit of hypothesizing. Did the weather affect beer sales? Did that latest marketing campaign impact sales?
3. Predictive analytics: This moves to what is likely going to happen in the near term. What happened to sales the last time we had a hot summer? How many weather models predict a hot summer this year?
4. Prescriptive analytics: This suggests a course of action. If the likelihood of a hot summer is measured as an average of these five weather models is above 58%, we should add an evening shift to the brewery and rent an additional tank to increase output.
Natural language processing (NLP) refers to the branch of computer science—and more specifically, the branch of artificial intelligence or AI—concerned with giving computers the ability to understand text and spoken words in much the same way human beings can.
A forecast is a prediction made by studying historical data and past patterns. Businesses use software tools and systems to analyze large amounts of data collected over a long period. There are three basic types—qualitative techniques, time series analysis and projection, and causal models.
Business intelligence comprises the strategies and technologies used by enterprises for the data analysis and management of business information.A Business Intelligence Analyst is responsible for taking the data that a company holds and mining it to achieve valuable insights. These insights are then used to inform critical business decisions. The insights play a crucial role in shaping the company's future and the way it operates.
The text-to-speech (TTS) assistive technology uses artificial intelligence to translate information written in a human-readable form in one language into audio, voice, or speech with a human accent. Such systems turn text into audio or speech output using AI-driven algorithms as the input.
An enterprise AI platform is an integrated set of technologies that enables organizations to design, develop, deploy, and operate enterprise AI applications at scale. Enterprise AI applications represent a new category of enterprise software.Machine learning is a pathway to creating artificial intelligence, which in turn is one of the primary drivers of machine learning use in the enterprise.
