![]() ![]() # word screen_name n total n_norm tf idf tf_idf range # calculate range per word (status_id indicates individual tweets) We can also add range, to decide what words to keep and understand tf-idf a little better. # word screen_name n total n_norm tf idf tf_idf # calculate tf-idf based on n, providing the word column and the category col The goal in using tf-idf is to decrease the weight for commonly used words (i.e., words used across all documents) and increase the weight for words that are less frequent in other documents in that collection. We can calculate term frequency inverse document frequency ( tf-idf) instead of normalized frequency. Y = reorder_within(word, n_norm, screen_name))) + Plot the data again, but by normalized frequency. We create a new column with normalized frequency using mutate(). We can now join subcorpora_size with our word_frequency_per_user data frame by the column they have in common, which is screen_name. # 2 SpeakerPelosi 89003 # change the n in the column name to total We calculated the size of each sub-corpora, so we can normalize the frequencies. Y = reorder_within(word, n, screen_name))) +įacet_wrap(~screen_name, scales = "free_y") + Plotting the data makes it easier to compare frequent tokens across different users. # 6 american SpeakerPelosi 316 # looks good, create word_frequency_per_user data frame ![]() # arrange count so we see most frequent words first tokens_to_remove %Ĭount words again, see if it’s better. We first need to create a list of tokens to remove. Mmmmmm… the most frequent words are related to urls and other symbols. We also use count() to count the frequency of individual words per screen_name. # remove stop words from pencil reviews tokenizedĪnti_join(my_stop_words) # Joining, by = "word" # inspect data We now use this filtered data frame with an anti_join to keep only words that are not in the stop words list. Let’s filter the stop words to keep only words from the snowball lexicon. # … with 718 more rows # the smallest lexicon is snowball Stop words often include pronouns as well, modals, and frequent adverbs. Stop words are words that are very common in a language, but might not carry a lot of meaning, like function words. Unnest_tokens(ngram, text, token = "ngrams", n = 2) In addition to individual word tokenization, unnest_tokens() offers a number of tokenization formatis, including ngrams. # … with 3 more variables: favorite_count, retweet_count , # user_id status_id created_at screen_name source display_text_wi… We are keeping only the first 7 columns, favorite count and retweet count. Let’s reduce the number of variables in our data, so it’s more manageable. # loaded via a namespace (and not attached): # stats graphics grDevices utils datasets methods base # LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib # BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib If you have R and RStudio already installed in your computer, make sure your R version is greater than 4.0 by entering sessionInfo() in your console. Make sure you install both R and RStudio for this workshop.ĭownload and install R from (If you are a Windows user, first determine if you are running the 32 or the 64 bit version) ![]()
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