Matrici di confusione

Hai appena finito di creare un modello di classificazione. Questo modello predice se i tweet sono stati creati da un bot con orientamento a sinistra (democratico) o a destra (repubblicano). Hai effettuato le previsioni sui dati di test e hai ottenuto il seguente risultato:

Previsioni	Left	Right
Left	350	157
Right	57	436

Usa la matrice di confusione qui sopra per rispondere alle domande sull'accuratezza del modello.

Questo esercizio fa parte del corso

Introduzione all'Elaborazione del Linguaggio Naturale in R

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Istruzioni dell'esercizio

Quale percentuale di tweet hai etichettato correttamente come Left?
Quale percentuale di tweet hai etichettato correttamente come Right?
Quale percentuale di tweet hai etichettato correttamente in totale?

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Percentage correctly labeled "Left"
left <- (___) / (350 + 157)
left

# Percentage correctly labeled "Right"
right <- (___) / (___ + ___)
right

# Overall Accuracy:
accuracy <- (___ + ___) / (___ + ___ + ___ + ___)
accuracy

Modifica ed esegui il codice

Questo esercizio fa parte del corso

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: Understanding an R corpus Exercise 2: Explore an R corpus Exercise 3: Creating a tibble from a corpus Exercise 4: Creating a corpus Exercise 5: The bag-of-words representation Exercise 6: Practice BoW Exercise 7: BoW Example Exercise 8: Sparse matrices Exercise 9: The TFIDF Exercise 10: Manual calculations Exercise 11: TFIDF Practice Exercise 12: Cosine Similarity Exercise 13: An example of failing at text analysis Exercise 14: Cosine similarity example

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: Preparare il testo per il modeling Exercise 2: Preparazione dei dati Exercise 3: Rimozione dei termini sparsi Exercise 4: Modellazione di classificazione Exercise 5: Esempio di classificazione Exercise 6: Matrici di confusione

Esercizio in corso

Exercise 7: Confronto: tibble TFIDF vs dtm Exercise 8: Introduzione al topic modeling Exercise 9: Esercizio su LDA Exercise 10: Assegnare argomenti ai documenti Exercise 11: LDA in pratica Exercise 12: Verifica della perplexity Exercise 13: Rivedere i risultati LDA

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