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Moo

  by Jane Smiley


(about 605 pages)
151,293
total words
of all the books in our library
42.73%
vividness
of all the books in our library
8.13%
passive voice
of all the books in our library
3.41%
all adverbs
of all the books in our library
1.18%
ly-adverbs
of all the books in our library
2.22%
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
would have given her six reasons, all of them invasively personal, about why she SHOULD be interested. It was then that she did what she shouldn’t have done. Probably if he had shown any more curiosity, let’s say, curiosity remotely approaching what he had shown early in the fall, Margaret would not have been tempted. His naked urging would have pushed her toward high moral ground, and that’s what she had expected, urging of the most naked sort. Probably she would not have been tempted if he had not sprung so suddenly from the veal to the moral high ground himself. “You know, we had our meeting.” “I figured you would have by now.” “Don’t you want to know?” “You would never tell me.” She looked at him. She said, “It was good.” She thought about him dismissing the veal again, veal she had felt a little pride in presenting. She added, “But not really good.” He didn’t say anything. “Not good enough, maybe—” He actually shrugged. It was Margaret who exclaimed. “Monahan! What is the matter with you? Where is the careerist lowlife, the money-grubbing, arrogant, narrow-minded, narcissistic, sexy, exuberant, happy guy I used to know?” “So, tell me the number, then.” “I can’t tell you the number! But, lower than seven, higher than five!” He shrugged again. The old Tim would have leapt out of his chair at how weak his recommendation was, how iffy its passage through the provost’s office would undoubtedly prove. She would have been and the children had been raking and mulching the flower beds, tying old daffodil stems out of sight, heading the tulips so that only the last perfect ones were on display. They had uncovered the roses and pruned them, fertilized the fruit trees, planted broccoli, cauliflower, peas, lettuce, chard, onions, and leeks, thinned the daylily bed, pruned the privet hedge, tied together the peonies, planted marigolds and nasturtiums and gladioli and baby’s breath. For the wedding, the wedding, the wedding, the children had done more work with more enthusiasm than during any spring he could remember. Even the eldest capered from bed to bed, dressed entirely in black but wreathed entirely in smiles. Now, while the Lady X was inside getting dressed, he was ambling here and there, choosing blooms for her bouquet. Emperor tulips, white and red, Dutch irises, yellow with purple stamens. Branches of lilacs, white and lavender, apple blossoms, plum blossoms, cherry blossoms. The mingling fragrances lifted from the basket he was carrying and made him dizzy with delight. A wedding! How was it that he had never done this before? He set down the basket in the green grass and knelt beside it, then took the flowers in his two hands and pressed them against his face. He could feel their soft petals and rough stems, smell their sweetness. He closed his eyes. Then she was near. He sensed that before he looked and saw the hem of her new yellow dress. “Hey,” she said. “People

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 3025.86 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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