Analyzing Text Corpora to Determine the Emotional State of Humans

People's feelings are often reflected in the way they write. For this project, texts were used to characterize the emotional state of people both throughout the decades and in different parts of society in the present time. For temporal analysis, an online Corpus of Historical American English was used (400 million words, 1810-2000). For the present-day analysis, a collection of bloggers' posts from Kaggle (grouped by gender, age, and occupation) was put through a sentiment analysis tool. It was found that in the course of 200 years, energetic words decreased in frequency, and less energetic words increased. Negative and positive words decreased, and neutral words increased, indicating that there may have been a rise of apathy in society. Further, it was found that in present day's common usage, females, younger people, and those with a background in the arts exhibit more negative emotions than the other groups. These findings indicate that mathematical and computational analysis can be used to detect not only long-term societal trends, but also to study the emotional characteristics of different groups of people. In particular, methods of data science could be a valid tool to identify vulnerable populations that can be targeted for depression evaluation.