“Happiness” in 20th Century English books

Just to give an idea of the analysis mentioned in the previous post, the plot below shows the trend for a rough measure of the “happiness” of the books present in the Google Books database. For WordNet-Affect (WNA) this is obtained, simplifying a little, by subtracting the cumulative scores of the categories of “Joy” and “Sadness”, while for Linguistic Inquiry and Word Count (LIWC) the two (equivalent) categories are called “Positive emotions” and, again, “Sadness”. Values above zero indicate generally ‘happy’ periods, and values below the zero indicate generally ‘sad’ periods.


This result is interesting for me not much because we can discover something new about the last century (even though I wonder why the 80s seems to be so sad), but because if (i) two independent ways to score the emotional content of texts (ii) trough a quite rough analysis of (iii) an enormous database of books, give highly correlated trends, this means that there is a meaningful “signal” that we can extract (which can not be taken for granted).

We also performed an analogous analysis using a tool called “Hedonometer“ (HED – see the plot below). In this case the results are quite different, even though some similarities are present, e.g. the 20s positive peak, the negative peak corresponding to Second World War, the post-80s increase in happiness. The reason is probably that LIWC and WNA are conceptually quite different from HED. LIWC and WNA are basically “lists” of words related to specific emotions (so, for example, the first – alphabetically – 5 words in LIWC’s category of “Sadness” are: abandon*, ache*, aching, agoniz*, agony), while HED uses a list of generic words not directly related to emotional states, but evaluated by human subjects as particularly happy or sad. So, for example, HED scores in texts the presence of words such as “terrorism” or “Christmas”.


One interesting things to notice regarding HED is that it is the only index that “tracks” the effect of the First World War. Also, comparing the absolute values of our results (the right y-axis in the plot above) with the the values obtained for contemporary twitter messages (see here), it seems that, in general, books tend to be slightly more “sad” than tweets.

If you are interested in more details, and in the other analysis, the preprint of our contribution can be found here.

Robustness of emotion extraction from 20th Century English books

I’ll give today a short talk at the Big Humanities Workshop, held in conjunction with the 2013 IEEE International Conference on Big Data, on our research on the emotional content of English-language books.

In a previous work we analysed the usage of emotion-related words using the Google Books database. We reported there three main findings:

  1. the existence of distinct periods of positive and negative “moods” detectable trough automatic analysis of the texts.
  2. a steady decrease in the usage of emotion-related words throughout the century.
  3. a divergence between American English and British English books, with the former getting more “emotional” starting from 1960s.

The next step has been to perform additional analysis to check the robustness of these results. In details, we re-run the same analysis with the last (2012) version of the Google Books corpus (which contains approximately 3 millions more books than the one we used originally), we compare the results of different, independent, ways to score the emotional content of the texts (originally we used WordNet-Affect, that now we compare with Linguistic Inquiry and Word Count and “Hedonometer“), we run more detailed statistical analysis (to check the effect of high-frequency mood-words that might determine on their own the trends for specific emotions, obscuring the role of the numerous low-frequency terms), and, finally, we compare our original results with trends obtained by considering only terms tagged as adjectives or adverbs, which are considered reliable indicators of emotional content (Part-Of-Speech information was not present in the first version of the Google corpus).

Overall, we were happy to see that the original results demonstrated to be quite robust (especially results #2 and #3). The next step would be now to understand what they mean – to me, especially interesting is the decrease in the emotional content – assuming that they do not derive from some idiosyncrasy of the Google database. Apparently the official Proceedings of the IEEE Big Data Conference are not around yet, but here you can find a preprint of our contribution (thanks to Bill, coauthor together with Alex Bentley).

Unfortunately I will not be physically in some room in Santa Clara, California, to present my talk. It would have been very interesting for me to get to know more of the “Digital Humanities” world (to me, books are just one kind of artefact useful to study more general cultural dynamics, and it happens they are convenient to quantify, have temporal depth – someone talks, in this regard, of long data), hopefully there will be other occasions. Also my distant-talk will end up to be after 11 pm Bristol-time, and after a Puccini’s La Bohème, so if you, reader, are in the workshop, I apologise in advance…