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Viper Nine

  by Rob Aspinall


(about 363 pages)
90,783
total words
of all the books in our library
58.92%
vividness
of all the books in our library
5.20%
passive voice
of all the books in our library
1.54%
all adverbs
of all the books in our library
0.23%
ly-adverbs
of all the books in our library
1.31%
non-ly-adverbs
of all the books in our library

clippings from this book

We’ve analyzed hundreds of millions of words, from thousands of different authors, training our linguistic models to recognize the most vivid words in the English language… the words that create the most intense sensory experiences: colors, textures, sounds, flavors, and aromas.

Based on our analysis, we’ve scanned through the pages of this book to find the two pages at the extremes, both the most-passive and the most-vivid pages, so that you can compare them side-by-side and see the difference:

MOST PASSIVE PAGE
MOST VIVID PAGE
he replied. ‘Isn’t that where you’re headed?’ Anna asked Driver. ‘Then I’m definitely coming,’ said Wells. Driver shrugged, the argument won. ‘Do we know which cell is controlling which hacks?’ Mo shook his head. ‘No, but if there’s one that will be sophisticated enough to hijack intelligence agencies and defence systems, it will be in Hong Kong.’ ‘What’s so special about Hong Kong?’ Pope asked. ‘Because it’s the home of Attack Dog.’ Mo looked around the table. By the blank expressions, Driver figured she wasn’t the only one in the dark. ‘Don’t you people know anything?’ the German grumbled to himself. ‘It’s a Chinese hacking group.’ Pope chewed on his own confusion. ‘If they’re Chinese, what are they doing in Hong Kong?’ ‘Because there’s more freedom of movement than on the mainland,’ Lim explained. ‘How can we know for sure it’s this Attack Dog?’ Driver asked. ‘It’s in the code,’ Mo said. ‘Elvis had a look. The Beatles had a sound. Attack Dog have their own signature style of code. And man is it a masterpiece. A malware upload isn’t going to cut it.’ ‘Then you should go with Lim,’ Driver said. Mo’s eyes grew to the size of cue balls. ‘What? Me?’ He shook his head. ‘I’m not – I can’t—’ Lim was quick to agree. ‘He’s not field-trained. He’ll slow me down. And I’m not a babysitter.’ ‘Yes,’ agreed Mo. ‘I’m far more use to you here.’ ‘I thought you said you couldn’t do this remotely,’ Wells said. ‘I’m Driver rolled onto her side again, flung the covers aside and hauled herself out of bed. The dark laminate flooring was chilly under her soles. She got to her feet and left the phone on the bedside table. Padding across the room, the matte-white walls were bare of any sign of life and seemed to glow in the dark. Driver sleepwalked her way through her tiny one-bedroom home into the living area. She opened the fridge and peered into the harsh light, grabbed a carton of orange juice and a cup off the blender, setting both down on the worktop. Next came a jar of coffee off a shelf. A spoon dug in and a heap of ground beans tipped into the blender. Working in the light of the almost-bare fridge, Driver took the last couple of eggs from the inside of the door. She cracked them in the blender and reached inside again to a lonely jar of peanut butter. A large spoonful completed the mound of ingredients in the blender, to which Driver added a dash of water. The high-speed churn of the blender was offensively loud. Driver pulled the cup from the machine. ‘Down the hatch,’ she said to herself before necking the whole lot in one. She slammed the cup down on the worktop and grimaced. Driver shuddered at the foul taste as a loud belch took her by surprise. Flinging the cup in the sink and rubbing an eye with the base of her palm

emotional story arc

Click anywhere on the chart to see the most significant emotional words — both positive & negative — from the corresponding section of the text…
This chart visualizes the the shifting emotional balance for the arc of this story, based on the emotional strength of the words in the prose, using techniques pioneered by the UVM Computational Story Lab. To create this story arc, we divided the complete manuscript text into 50 equal-sized chunks, each with 1815.66 words, and then we scored each section by counting the number of strongly-emotional words, both positive and negative. The bars in the chart move downward whenever there’s conflict and sadness, and they move upward when conflicts are resolved, or when the characters are happy and content. The size of each bar represents the positive or negative word-count of that section.

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