04-24-2019, 12:29 AM
Breaking Bard: Using AI to unlock Shakespeare’s greatest works
Spoiler alert: At the end of Romeo and Juliet, they both die.
OK, as spoilers go, it’s not big. Most people have read the play, watched one of the famous films or sat through countless school lessons devoted to William Shakespeare and his work. They know it doesn’t end well for Verona’s most famous couple.
In fact, the challenge is finding something no one knows about the world-famous, 300-year-old play. That’s where artificial intelligence can help.
Phil Harvey, a Cloud Solution Architect at Microsoft in the UK, used the company’s Text Analytics API on 19 of The Bard’s plays. The API, which is available to anyone as part of Microsoft’s Azure Cognitive Services, can be used to identify sentiment and topics in text, as well as pick out key phrases and entities. This API is one of several Natural Language Processing (NLP) tools available on Azure.
By creating a series of colourful, Power BI graphs (below) showing how negative (red) or positive (green) the language used by The Bard’s characters was, he hoped to shine a new light on some of the greatest pieces of literature, as well as make them more accessible to people who worry the plays are too complex to easily understand.
Harvey said: “People can see entire plotlines just by looking at my graphs on language sentiment. Because visual examples are much easier to absorb, it makes Shakespeare and his plays more accessible. Reading language from the 16th and 17th centuries can be challenging, so this is a quick way of showing them what Shakespeare is trying to do.
“It’s a great example of data giving us new things to know and new ways of knowing it; it’s a fundamental change to how we process the world around us. We can now pick up Shakespeare, turn it into a data set and process it with algorithms in a new way to learn something I didn’t know before.”
What Harvey’s graphs reveal is that Romeo struggles with more extreme emotions than Juliet. Love has a much greater effect on him challenging stereotypes of the time that women – the fairer sex – were more prone to the highs and lows of relationships.
“It’s interesting to see that the male lead is the one with more extreme emotions,” Harvey added. “The longest lines, both positive and negative, are spoken by him. Juliet is steadier; she is positive and negative but not extreme in what she says. Romeo is a fellow of more extreme emotion, he’s bouncing around all over the place.
“Macbeth is also interesting because there are these two peaks of emotion, and Shakespeare uses the wives at these points to turn the story. I also looked at Helena and Hermia in A Midsummer Night’s Dream, because they have a crossed-over love story. They are both positive at the start but then they find out something and it gets negative towards the end.”
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Spoiler alert: At the end of Romeo and Juliet, they both die.
OK, as spoilers go, it’s not big. Most people have read the play, watched one of the famous films or sat through countless school lessons devoted to William Shakespeare and his work. They know it doesn’t end well for Verona’s most famous couple.
In fact, the challenge is finding something no one knows about the world-famous, 300-year-old play. That’s where artificial intelligence can help.
Phil Harvey, a Cloud Solution Architect at Microsoft in the UK, used the company’s Text Analytics API on 19 of The Bard’s plays. The API, which is available to anyone as part of Microsoft’s Azure Cognitive Services, can be used to identify sentiment and topics in text, as well as pick out key phrases and entities. This API is one of several Natural Language Processing (NLP) tools available on Azure.
By creating a series of colourful, Power BI graphs (below) showing how negative (red) or positive (green) the language used by The Bard’s characters was, he hoped to shine a new light on some of the greatest pieces of literature, as well as make them more accessible to people who worry the plays are too complex to easily understand.
Harvey said: “People can see entire plotlines just by looking at my graphs on language sentiment. Because visual examples are much easier to absorb, it makes Shakespeare and his plays more accessible. Reading language from the 16th and 17th centuries can be challenging, so this is a quick way of showing them what Shakespeare is trying to do.
“It’s a great example of data giving us new things to know and new ways of knowing it; it’s a fundamental change to how we process the world around us. We can now pick up Shakespeare, turn it into a data set and process it with algorithms in a new way to learn something I didn’t know before.”
What Harvey’s graphs reveal is that Romeo struggles with more extreme emotions than Juliet. Love has a much greater effect on him challenging stereotypes of the time that women – the fairer sex – were more prone to the highs and lows of relationships.
“It’s interesting to see that the male lead is the one with more extreme emotions,” Harvey added. “The longest lines, both positive and negative, are spoken by him. Juliet is steadier; she is positive and negative but not extreme in what she says. Romeo is a fellow of more extreme emotion, he’s bouncing around all over the place.
“Macbeth is also interesting because there are these two peaks of emotion, and Shakespeare uses the wives at these points to turn the story. I also looked at Helena and Hermia in A Midsummer Night’s Dream, because they have a crossed-over love story. They are both positive at the start but then they find out something and it gets negative towards the end.”
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