@inproceedings{a42f56f1641842508a33d14fa17890fb,
title = "Contextual sentiment analysis",
abstract = "This study examines the role of context in evaluating responses to social media posts online. Current sentiment analysis tools evaluate the content of posts without considering the broader context that the post comes from. Utilizing data from an in-person study, we examine differences between perceived sentiment evaluation when social media response posts are viewed in isolation and perceived sentiment evaluation when social media responses are viewed in the context of the original post. We find that evaluations of responses viewed in context change over 50 \% of the time. We validate this finding by utilizing simulated data to show the result is not simply a result of data manipulation or noisy data; furthermore, we explore results of this finding with current sentiment analysis tools, examining this result with subsets of our data with high and low kappa values.",
keywords = "Affect control theory, Sentiment analysis, Social media, Twitter",
author = "Will Frankenstein and Kenneth Joseph and Carley, \{Kathleen M.\}",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; 9th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction and Behavior Representation in Modeling and Simulation, SBP-BRiMS 2016 ; Conference date: 28-06-2016 Through 01-07-2016",
year = "2016",
doi = "10.1007/978-3-319-39931-7\_28",
language = "English",
isbn = "9783319399300",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "291--300",
editor = "Nathaniel Osgood and Xu, \{Kevin S.\} and David Reitter and Dongwon Lee",
booktitle = "Social, Cultural, and Behavioral Modeling - 9th International Conference, SBP-BRiMS 2016, Proceedings",
address = "Germany",
}