  Given the growing assortment of sentiment measuring instruments, it is
imperative to understand which aspects of sentiment dictionaries contribute to
both their classification accuracy and their ability to provide richer
understanding of texts. Here, we perform detailed, quantitative tests and
qualitative assessments of 6 dictionary-based methods applied, and briefly
examine a further 20 methods. We show that while inappropriate for sentences,
dictionary-based methods are generally robust in their classification accuracy
for longer texts.
  Stories often following distinct emotional trajectories, forming patterns
that are meaningful to us. By classifying the emotional arcs for a filtered
subset of 4,803 stories from Project Gutenberg's fiction collection, we find a
set of six core trajectories which form the building blocks of complex
narratives. Of profound scientific interest will be the degree to which we can
eventually understand the full landscape of human stories, and data driven
approaches will play a crucial role.
  Finally, we utilize web-scale data from Twitter to study the limits of what
social data can tell us about public health, mental illness, discourse around
the protest movement of #BlackLivesMatter, discourse around climate change, and
hidden networks. We conclude with a review of published works in complex
systems that separately analyze charitable donations, the happiness of words in
10 languages, 100 years of daily temperature data across the United States, and
Australian Rules Football games.
