Visualizing Book Sentiments

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Sentiment analysis of social media content has become pretty popular of late, and a few days ago, as I lay in bed, I wondered if we could do the same thing to books – and see how sentiments vary through the story.

The answer, of course, was that yes, we could. And if you’d rather just jump to an implementation you can try yourself, here’s the link: Upload a book (in plaintext format), and the variation of sentiment as you scroll through the pages is computed and plotted.

Here are a couple of graphs that help visualize the flow of sentiments through one of my favourite novels, A Tale of Two Cities:

The values above zero indicate ‘positive’ emotions, and the values below zero indicate ‘negative’ emotions

Red is negative, green is positive, yellow is neutral

The text is freely available via Project Gutenberg. The code was written in R, and deployed using the shiny package. The app itself is hosted by the generous people at RStudio. The code is available on github at, and a basic description of the functions used to generate the scores can also be found in this post.

So how does the sentiment analysis really work? We use a dictionary that maps a given word to its ‘valence’ – a single integer score in the range -5 to +5. One such freely available mapping is the AFINN-111 list. 

I read the AFINN file into R, and used it to look up the score for each word in the book file…


…divided up the scores into the desired number of parts and averages the scores for each part…

 RollUpScores <-function(scores, parts=100){   
     batch.size <- round(length(scores)/parts,0)   
     s <- sapply(seq(batch.size/2, length(scores) - batch.size/2, batch.size), function(x){   
         low <- x - (batch.size/2)   
         high <- x + (batch.size/2)   

…And plotted the resulting data frame using ggplot2
Complete code available here. There's a version to run on a standalone R window, and a Shiny deployment version. Python files provide an alternative implementation.

As a side note, I'd like to comment on a drawback of using a lookup table for sentiment analysis – this completely overlooks the context of a keyword (“happy” in “I am not happy” certainly has a different valence than in most other scenarios). This method cannot capture such patterns. An even more difficult task is to be able to capture sarcasm. There are a number of papers on how to capture sarcasm in text in case you're interested, but our current approach ignores these cases. 

Finally, there may or may not be an upcoming post on  author prediction using sentiment analysis in book texts. In the meantime, do play around with the app/code and suggest improvements.

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