Our goals of this work are to (1) explore the gender bias in the use of subjective language in social media, and (2) incorporate this bias into models to improve sentiment analysis for English, Spanish, and Russian.
in this paper we: • investigate multilingual lexical variations in the use of subjective language, and cross-cultural emoticon and hashtag usage on a large scale in Twitter data; 2 • show that gender bias in the use of subjective language can be used to improve sentiment analysis for multiple languages in Twitter.