Metti in pratica il BoW

Dati i seguenti testi:

t1 <- c("Today will be an awesome day. The best day of the week")
t2 <- c("Yesterday was an awesome day. Better than today.")
t3 <- c("Tomorrow will be the best day. Better than yesterday and today.")

Hai creato un vettore di parole:

word_vector <- c("today", "awesome", "day", "best", "week", "yesterday", "better", "tomorrow").

Qual è una possibile rappresentazione bag-of-words di t3?

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Introduzione all'Elaborazione del Linguaggio Naturale in R

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Introduzione all'Elaborazione del Linguaggio Naturale in R

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Chapter 1 of Introduction to Natural Langauge Processing prepares you for running your first analysis on text. You will explore regular expressions and tokenization, two of the most common components of most analysis tasks. With regular expressions, you can search for any pattern you can think of, and with tokenization, you can prepare and clean text for more sophisticated analysis. This chapter is necessary for tackling the techniques we will learn in the remaining chapters of this course.

Exercise 1: Regular expression basics Exercise 2: Practicing syntax with grep Exercise 3: Exploring regular expression functions.Exercise 4: Tokenization Exercise 5: tidytext functions Exercise 6: Tokenization: sentences Exercise 7: Text cleaning basics Exercise 8: Text preprocessing: remove stop words Exercise 9: Text preprocessing: Stemming

In this chapter, you will learn the most common and studied ways to analyze text. You will look at creating a text corpus, expanding a bag-of-words representation into a TFIDF matrix, and use cosine-similarity metrics to determine how similar two pieces of text are to each other. You build on your foundations for practicing NLP before you dive into applications of NLP in chapters 3 and 4.

Exercise 1: Capire un corpus in R Exercise 2: Esplora un corpus in R Exercise 3: Creare una tibble da un corpus Exercise 4: Creare un corpus Exercise 5: La rappresentazione bag-of-words Exercise 6: Metti in pratica il BoW

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Exercise 7: Esempio BoW Exercise 8: Matrici sparse Exercise 9: Il TFIDF Exercise 10: Calcoli manuali Exercise 11: Esercizio su TFIDF Exercise 12: Similarità coseno Exercise 13: Un esempio di fallimento nell'analisi del testo Exercise 14: Esempio di similarità coseno

Chapter 3 focuses on two common text analysis approaches, classification modeling, and topic modeling. If you are working on text analysis projects, you will inevitably use one or both of these methods. This chapter teaches you how to perform both techniques and provides insight into how to approach these techniques from a practical point of you.

Exercise 1: Preparing text for modeling Exercise 2: Data preparation Exercise 3: Removing sparse terms Exercise 4: Classification modeling Exercise 5: Classification modeling example Exercise 6: Confusion matrices Exercise 7: TFIDF tibble vs dtm Exercise 8: Introduction to topic modeling Exercise 9: LDA practice Exercise 10: Assigning topics to documents Exercise 11: LDA in practice Exercise 12: Testing perplexity Exercise 13: Reviewing LDA results

In chapter 4 we cover two staples of natural language processing, sentiment analysis, and word embeddings. These are two analysis techniques that are a must for anyone learning the fundamentals of text analysis. Furthermore, you will briefly learn about BERT, part-of-speech tagging, and named entity recognition. Almost 15 different analysis techniques were covered in this course, so chapter 4 ends by recapping all of the great techniques you will learn about in this course.

Exercise 1: Sentiment analysis Exercise 2: tidytext lexicons Exercise 3: Sentiment scores Exercise 4: Sentiment and emotion Exercise 5: Word embeddings Exercise 6: h2o practice Exercise 7: word2vec Exercise 8: Additional NLP analysis Exercise 9: Reviewing methods #1 Exercise 10: Review methods #2 Exercise 11: Conclusion