Sentiment ed emozioni

All'interno del dataset sentiments, il lessico nrc contiene un dizionario di parole e un'emozione associata a ciascuna parola. In questo dataset trovi emozioni come joy, trust, anticipation e altre.

Nel dataset dei tweet bot russi che hai esplorato, hai analizzato i tweet inviati sia da un bot con orientamento di sinistra sia da uno di destra. Esplora il contenuto dei tweet inviati dal bot di sinistra (democratico) usando il lessico nrc. I tweet di sinistra, left, sono stati tokenizzati in parole, con le stop word rimosse.

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Introduzione all'Elaborazione del Linguaggio Naturale in R

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Istruzioni dell'esercizio

Crea una tibble con solo le parole di tipo anticipation dal lessico nrc.
Crea una tibble con solo le parole di tipo joy dal lessico nrc.
Stampa le parole principali di anticipation trovate in left_tokens.
Stampa le parole principali di joy trovate in left_tokens.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

left_tokens <- left %>%
  unnest_tokens(output = "word", token = "words", input = content) %>%
  anti_join(stop_words)
# Dictionaries 
anticipation <- ___("nrc") %>% 
  ___(sentiment == "anticipation")
joy <- ___("nrc") %>% 
  ___(sentiment == "joy")
# Print top words for Anticipation and Joy
left_tokens %>%
  ___(anticipation, by = "word") %>%
  ___(word, sort = TRUE)
left_tokens %>%
  ___(joy, by = "word") %>%
  ___(word, sort = TRUE)

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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: 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: Analisi del sentiment Exercise 2: lessici di tidytext Exercise 3: Punteggi di sentiment Exercise 4: Sentiment ed emozioni

Esercizio in corso

Exercise 5: Word embeddings Exercise 6: Esercizio su h2o Exercise 7: word2vec Exercise 8: Analisi NLP aggiuntive Exercise 9: Ripasso dei metodi #1 Exercise 10: Ripasso dei metodi #2 Exercise 11: Conclusione