sentiment_scores_r.RdCalculate sentiment indicators from
TextBlob and
vaderSentiment.
sentiment_scores_r(x)
| x | Data frame. The text to run sentiment analysis on. |
|---|
Data frame. All indicators produced by TextBlob (polarity and
subjectivity) and vaderSentiment (positive, negative and neutral
sentiments, and compound score).
This function complements existing sentiment analysis packages in R
(e.g. tidytextor quanteda.sentiment) with the popular Python
sentiment analysis libraries TextBlob and vaderSentiment.
TextBlob calculates two indicators, namely polarity and
subjectivity. The polarity score is a float within the range [-1, 1],
where -1 is for very negative sentiment, +1 is for very positive
sentiment, and 0 is for neutral sentiment. The subjectivity is a float
within the range [0, 1], where 0 is very objective and 1 is very
subjective.
vaderSentiment assigns to the given text three sentiment proportions
(positive, negative and neutral) whose scores sum to 1. It also
calculates a compound score that is a float in [-1, 1], similar to
TextBlob's polarity.
sentiments <- pxtextmineR::text_data %>% dplyr::select(feedback) %>% pxtextmineR::sentiment_scores_r() head(sentiments)#> text_blob_polarity text_blob_subjectivity vader_neg vader_neu vader_pos #> 1 0.00000 0.0000000 0.000 1.000 0.000 #> 2 -0.09375 0.4000000 0.265 0.735 0.000 #> 3 0.20000 0.2625000 0.000 1.000 0.000 #> 4 0.23125 0.4669643 0.199 0.625 0.176 #> 5 0.02500 0.3250000 0.029 0.913 0.058 #> 6 -0.27500 0.5750000 0.091 0.813 0.096 #> vader_compound #> 1 0.0000 #> 2 -0.2040 #> 3 0.0000 #> 4 0.0490 #> 5 0.3400 #> 6 0.2967#> text_blob_polarity text_blob_subjectivity vader_neg vader_neu vader_pos #> [1,] -1 0 0 0 0 #> [2,] 1 1 1 1 1 #> vader_compound #> [1,] -0.9916 #> [2,] 0.9971