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Given the growing assortment of sentiment measuring instruments, it isimperative to understand which aspects of sentiment dictionaries contribute toboth their classification accuracy and their ability to provide richerunderstanding of texts. Here, we perform detailed, quantitative tests andqualitative assessments of 6 dictionary-based methods applied, and brieflyexamine a further 20 methods. We show that while inappropriate for sentences,dictionary-based methods are generally robust in their classification accuracyfor longer texts.  Stories often following distinct emotional trajectories, forming patternsthat are meaningful to us. By classifying the emotional arcs for a filteredsubset of 4,803 stories from Project Gutenberg's fiction collection, we find aset of six core trajectories which form the building blocks of complexnarratives. Of profound scientific interest will be the degree to which we caneventually understand the full landscape of human stories, and data drivenapproaches will play a crucial role.  Finally, we utilize web-scale data from Twitter to study the limits of whatsocial data can tell us about public health, mental illness, discourse aroundthe protest movement of #BlackLivesMatter, discourse around climate change, andhidden networks. We conclude with a review of published works in complexsystems that separately analyze charitable donations, the happiness of words in10 languages, 100 years of daily temperature data across the United States, andAustralian Rules Football games.

Towards a science of human stories: using sentiment analysis and emotional arcs to understand the building blocks of complex social systems