Sentiment Analysis is one of the most wanted and used NLP techniques. Companies like to see what their customers are talking about – like if there’s a new product launch then what’s the feedback about it. Whereever you’ve got Natural Language – like Social Media, Community Pages, Customer Support – Sentiment Analysis as a technique has found its home there.
While the technique itself is highly wanted, Sentiment Analysis is one of the NLP fields that’s far from super-accurate and the reason being is a lot of ways Humans talk. One of the aspects of it is called Valence Shifters like Negation that can flip the polarity of a sentence with one word.
“I’m happy” -> Positive “I’m not happy” -> Negative
Because of this, a lot of out-of-box Sentiment analysis packages and libraries fail at tasks like this. Kudos to Tyler Rinker’s
sentimentr R package that handles this scenario very well.
sentimentr is a lexicon-based Sentiment Analysis Package that’s one of the best out-of-box sentiment analysis solution (given you don’t want to build a Sentiment Classification or you don’t want to use a Paid API like Google Cloud API).
Code to get started:
library(sentimentr) library(tidyverse) text <- "This tutorial is awesome. The creator is not boring" sentiment() sentiment_by() sentiment(text) sentiment_by(text, by = NULL) profanity(text) debates <- presidential_debates_2012 debates_with_pol <- debates %>% get_sentences() %>% sentiment() %>% mutate(polarity_level = ifelse(sentiment < 0.2, "Negative", ifelse(sentiment > 0.2, "Positive","Neutral"))) debates_with_pol %>% filter(polarity_level == "Negative") %>% View() debates_with_senti %>% ggplot() + geom_boxplot(aes(y = person, x = sentiment)) debates$dialogue %>% get_sentences() %>% sentiment_by() %>% #View() highlight() debates %>% get_sentences() %>% sentiment_by(by = NULL) %>% #View() ggplot() + geom_density(aes(ave_sentiment))