  Researchers commonly perform sentiment analysis on large collections of short
texts like tweets, Reddit posts or newspaper headlines that are all focused on
a specific topic, theme or event. Usually, general-purpose sentiment analysis
methods are used. These perform well on average but miss the variation in
meaning that happens across different contexts, for example, the word "active"
has a very different intention and valence in the phrase "active lifestyle"
versus "active volcano". This work presents a new approach, CIDER (Context
Informed Dictionary and sEmantic Reasoner), which performs context-sensitive
linguistic analysis, where the valence of sentiment-laden terms is inferred
from the whole corpus before being used to score the individual texts. In this
paper, we detail the CIDER algorithm and demonstrate that it outperforms
state-of-the-art generalist unsupervised sentiment analysis techniques on a
large collection of tweets about the weather. CIDER is also applicable to
alternative (non-sentiment) linguistic scales. A case study on gender in the UK
is presented, with the identification of highly gendered and sentiment-laden
days. We have made our implementation of CIDER available as a Python package:
https://pypi.org/project/ciderpolarity/.
