Sentiments # A tibble: 23,165 × 4 # word sentiment lexicon score # 1 abacus trust nrc NA # 2 abandon fear nrc NA # 3 abandon negative nrc NA # 4 abandon sadness nrc NA # 5 abandoned anger nrc NA # 6 abandoned fear nrc NA # 7 abandoned negative nrc NA # 8 abandoned sadness nrc NA # 9 abandonment anger nrc NA # 10 abandonment fear nrc NA #. The tidytext package contains three sentiment lexicons in the sentiments dataset. There are a variety of dictionaries that exist for evaluating the opinion or emotion in text. Yet Harry Potter was still there, asleep at the moment, but no. The room held no sign at all that another boy lived in the house, # too. Ten years ago, # there had been lots of pictures of what looked like a large pink beach ball wearing different-colored bonnets - but Dudley Dursley was # no longer a baby, and now the photographs showed a large blond boy riding his first bicycle, on a carousel at the fair, playing a # computer game with his father, being hugged and kissed by his mother. Only the photographs on the mantelpiece really showed how much time had passed. Dursley had seen # that fateful news report about the owls. The sun rose on the same tidy front gardens and lit up the brass number four on the Dursleys' # front door it crept into their living room, which was almost exactly the same as it had been on the night when Mr. # "THE VANISHING GLASS Nearly ten years had passed since the Dursleys had woken up to find their nephew on the front step, but # Privet Drive had hardly changed at all. They didn't think they could bear it if anyone found out # about the Potters. The Dursleys had everything they wanted, but they also # had a secret, and their greatest fear was that somebody would discover it. The Dursleys had a # small son called Dudley and in their opinion there was no finer boy anywhere. Dursley was thin and blonde and had nearly twice the usual amount of neck, # which came in very useful as she spent so much of her time craning over garden fences, spying on the neighbors. He was a big, beefy man with hardly # any neck, although he did have a very large mustache. Dursley was the director of a firm called Grunnings, which made drills. They were the last people you'd expect to be involved in anything strange or mysterious, because they just didn't hold # with such nonsense.
Dursley, of number four, Privet Drive, were proud to say that they were perfectly normal, thank # you very much. Philosophers_stone # "THE BOY WHO LIVED Mr. For instance, the following illustrates the raw text of the first two chapters of the philosophers_stone:
HARRY POTTER BOOK 1 WORD COUNT SERIES
Library ( tidyverse ) # data manipulation & plotting library ( stringr ) # text cleaning and regular expressions library ( tidytext ) # provides additional text mining functions library ( harrypotter ) # provides the first seven novels of the Harry Potter series You can load the harrypotter package with the following: I constructed this package to supply the first seven novels in the Harry Potter series to illustrate text mining and analysis capabilities. This tutorial leverages the data provided in the harrypotter package.
Replication requirements: What you’ll need to reproduce the analysis in this tutorial.This tutorial builds on the tidy text tutorial so if you have not read through that tutorial I suggest you start there.
This tutorial serves as an introduction to sentiment analysis. This is considered sentiment analysis and this tutorial will walk you through a simple approach to perform sentiment analysis. Once we have cleaned up our text and performed some basic word frequency analysis, the next step is to understand the opinion or emotion in the text.
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