Sentiment analysis is a topic I cover regularly, for instance, with regard to Harry PlotterStranger Things, or Facebook. Usually I stick to the three sentiment dictionaries (i.e., lexicons) included in the tidytext R package (Bing, NRC, and AFINN) but there are many more one could use. Heck, I’ve even tried building one myself using a synonym/antonym network (unsuccessful, though a nice challenge). Two lexicons that did become famous are SentiWordNet, accessible via the lexicon R package, and the Loughran lexicon, designed specifically for the analysis of shareholder reports.

Josh Yazman did the world a favor and compared the quality of the five lexicons mentioned above. He observed their validity in relation to the millions of restaurant reviews in the Yelp dataset. This dataset includes both textual reviews and 1 to 5 star ratings. Here’s a summary of Josh’s findings, including two visualizations (read Josh’s full blog + details here):

  • NRC overestimates the positive sentiment.
  • AFINN also provides overly positive estimates, but to a lesser extent.
  • Loughran seems unreliable altogether (on Yelp data).
  • Bing estimates are accurate as long as texts are long enough (e.g., 200+ words).
  • SentiWordNet‘s estimates are mostly valid and precise, also on shorter texts, but may include minor outliers.

Sentiment scores by Yelp rating, estimated using each lexicon. [original]
The average sentiment score estimated using lexicons, where words are randomly sampled from the Yelp dataset. Note that, although both NRC and Bing scores are relatively positive on average, they also demonstrate a larger spread of scores (which is a good thing if you assume that reviews vary in terms of sentiment). [original]
On a more detailed level, David Robinson demonstrated how to uncover performance errors or quality issues in lexicons, in his 2016 blog on the AFINN lexicon. Using only the most common words (i.e., used in 200+ reviews for at least 10 businesses) of the same Yelp dataset, David visualized the inconsistencies between the AFINN sentiment lexicon and the Yelp ratings in two very smart and appealing ways:

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Words’ AFINN sentiment score by the average rating of the reviews they used in [original]
As the figure above shows, David found a strong positive correlations between the sentiment score assigned to words in the AFINN lexicon and the way they are used in Yelp reviews. However, there are some exception – words that did not have the same meaning in the lexicon and the observed data. Examples of words that seem to cause errors are die and bomb (both negative AFINN scores but used in positive Yelp reviews) or, the other way around, joke and honor (positive AFINN scores but negative meanings on Yelp).

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A graph of the frequency with which words are used in reviews, by the average rating of the reviews they occur in, colored for their AFINN sentiment score [original]
With the graph above, it is easy to see what words cause inaccuracies. Blue words should be in the upper section of this visual while reds should be closer to the bottom. If this is not the case, a word likely has a different meaning in the lexicon respective to how it’s used on Yelp. These lexicon-data differences become increasingly important as words are located closer to the right side of the graph, which means they more frequently screw up your sentiment estimates. For instance, fine, joke, fuck and hope cause much overestimation of positive sentiment while fresh is not considered in the positive scores it entails and die causes many negative errors.

TL;DR: Sentiment lexicons vary in terms of their quality/performance. If your texts are short (few hundred words) you might be best off using Bing (tidytext). In other cases, opt for SentiWordNet (lexicon), which considers a broader vocabulary. If possible, try to evaluate inaccuracies, outliers, and/or prediction errors via data visualizations.