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See Them Run

  by Marion Todd


(about 345 pages)
86,133
total words
of all the books in our library
70.94%
vividness
of all the books in our library
8.77%
passive voice
of all the books in our library
3.05%
all adverbs
of all the books in our library
0.94%
ly-adverbs
of all the books in our library
2.11%
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
It hadn’t been cheap. ‘I’ll take it with me and have the rest tonight. Too good to waste.’ ‘Whatever.’ ‘Would you like me to come with you this morning? Play the legal card?’ ‘Nope.’ ‘Clare…’ She glared at him, her eyes flashing. ‘If you hadn’t badgered me – yes, Tom – badgered me to have that glass of wine, I wouldn’t be facing a disciplinary meeting this morning.’ She had the bit between her teeth now. ‘You know something? Maybe my judgement was impaired. Maybe he’s right. I shouldn’t have been drinking on duty. End of.’ ‘Clare, you had a couple of mouthfuls, no more.’ Tom sat back in his chair. ‘You didn’t even finish the glass. You certainly wouldn’t have been over the limit.’ ‘A drink’s a drink. And I’m supposed to be hunting a murderer. I shouldn’t have done it. I should have said no. But because I didn’t, because I chose to drink the wine you bought, a man died. I had alcohol in my system when I was on duty and a man is dead.’ ‘But that wasn’t your fault, surely? This – this mad driver you’re huntingcan you honestly say if you hadn’t had a mouthful of wine that you’d have stopped him before he killed again? Honestly?’ ‘We’ll never know, Tom, will we?’ Tom took her hand in his. ‘I’m sorry.’ She withdrew her hand. ‘I’m a big girl, now. My decision, my mistake.’ He gave what he hoped was a sympathetic smile but she didn’t grey Henley, swilled Chianti and balsamic vinegar round the pan. ‘That’s a wonderful smell to come home to,’ Clare said as she slipped off her jacket. ‘Let me pour you a glass,’ Tom said. ‘Sorry, Tom, I have to go back tonight.’ ‘Half a glass?’ ‘Not even that.’ ‘Quarter? It’s a good one.’ He held up a bottle of Malbec. ‘Oh, go on then. But no more than that. I have to drive later.’ He poured a small glass for her and turned back to his red wine reduction. ‘Ciabatta’s warming in the oven. Salad on the table. It’s a lovely evening. Want to eat in the garden?’ Clare looked out of the window. Tom had unearthed a plastic table and chairs from the shed and put them where they would catch the evening sun. The grass had been cut and the edges neatly trimmed. It looked as if he’d weeded the borders too. It was a tempting sight. ‘Why not?’ The meal was delicious, as she knew it would be, the steaks cooked to perfection. Clare wiped her plate clean with a strip of warm ciabatta, savouring the last of the sauce. She tried to put the investigation to the back of her mind, for a few hours at least. She kicked off her shoes and flexed her feet, wriggling her toes. Tom took his cue and lifted one foot to his knee. He began to massage it and Clare leaned back and closed her eyes. Blackbirds were chirping

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 1722.66 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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other books by Marion Todd

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