We show that by combining the text and image information, we can build a machine learning approach that accurately distinguishes between the relationship types.
Vulgar words are employed in language use for several different functions, ranging from expressing aggression to signaling group identity or the informality of the communication.
User demographic inference from social media text has the potential to improve a range of downstream applications, including real-time passive polling or quantifying demographic bias.
Vulgarity is a common linguistic expression and is used to perform several linguistic functions.
Personality plays a decisive role in how people behave in different scenarios, including online social media.
Automatic political orientation prediction from social media posts has to date proven successful only in distinguishing between publicly declared liberals and conservatives in the US.
Inferring the emotional content of words is important for text-based sentiment analysis, dialogue systems and psycholinguistics, but word ratings are expensive to collect at scale and across languages or domains.
News sources frame issues in different ways in order to appeal or control the perception of their readers.
Streaming media provides a number of unique challenges for computational linguistics.