Social Networks Ban Shakespeare As Subversive & A Danger To Society!

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A Short Analysis of Sentiment Analysis & Emotion Recognition in AI
I
n this introductory podcast, on the topic of sentiment analysis and emotion recognition, we are going to present a well-known textual statement, allowing the listener to understand the problems Artificial Intelligence faces when having to deal with and interpret emotion and sentiment.
The process of sentiment analysis and emotion recognition entails using artificial intelligence components and algorithms to extract emotions and sentiments from online texts, such as tweets.

The information extracted can then be used by marketing, customer support, and public relations teams to foster positive consumer attitudes.

The nature of language within the electronic medium has evolved to include much more than textual statements, including, but not limited to, acronyms, emojis, also known as emoticons, and other visuals, such as images, gifs, and video.

Visuals can stand alone or have text superimposed over them, with or without sound. This text, too, must be read and understood by an algorithm, along with the visual accompanying it. Emoticons can have various meanings depending upon their context in a conversation. To interpret the sentiments and emotions within a textual statement without the context of an entire conversation or even a string of conversations is difficult enough.

Adding visual communication into the mix, which lacks any real sort of universal interpretation, can lead to catastrophic erroneous analysis and conclusions, even where there is a differentiation between emotion and sentiment.
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This podcast is a redacted & condensed version of an original Academic article, ‘Sentiment analysis and emotion recognition: Evolving the paradigm of communication within data classification,’ written by the CEO of If What If, Ted Gross, which was first published in “Applied Marketing Analytics” Volume 6 Number 1, a journal of Henry Stewart Publications. For a PDF of the entire article, you can use ResearchGate, Google Scholar, figshare, or contact If What If directly. In all cases, if you cite this podcast or the full published

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