  This work investigates the application of sampling methods for sentiment
analysis on two different highly imbalanced datasets. One dataset contains
online user reviews from the cooking platform Epicurious and the other contains
comments given to the Planned Parenthood organization. In both these datasets,
the classes of interest are rare. Word n-grams were used as features from these
datasets. A feature selection technique based on information gain is first
applied to reduce the number of features to a manageable space. A number of
different sampling methods were then applied to mitigate the class imbalance
problem which are then analyzed.
