As described in the steps we outlined for our approach, let us use a sentiment dictionary to score our initially extracted tweets. We are going to leverage the sentimentr R package to learn the sentiments of the tweets we have collected.
Let us see how to score using the sentiment function from the sentimentr package:
> library(sentimentr, quietly = TRUE) > sentiment.score <- sentiment(tweet.df$text) > head(sentiment.score) element_id sentence_id word_count sentiment 1: 1 1 8 0.0000000 2: 2 1 8 0.3535534 3: 3 1 3 0.0000000 4: 3 2 4 0.0000000 5: 3 3 7 0.0000000 6: 4 1 14 -0.8418729
The sentiment function in sentimentr calculates a score between -1 and 1 for each of the tweets. In fact, if a tweet has multiple sentences, it will calculate the score for each sentence. A score of -1 indicates...