  There has been a good amount of progress in sentiment analysis over the past
10 years, including the proposal of new methods and the creation of benchmark
datasets. In some papers, however, there is a tendency to compare models only
on one or two datasets, either because of time restraints or because the model
is tailored to a specific task. Accordingly, it is hard to understand how well
a certain model generalizes across different tasks and datasets. In this paper,
we contribute to this situation by comparing several models on six different
benchmarks, which belong to different domains and additionally have different
levels of granularity (binary, 3-class, 4-class and 5-class). We show that
Bi-LSTMs perform well across datasets and that both LSTMs and Bi-LSTMs are
particularly good at fine-grained sentiment tasks (i. e., with more than two
classes). Incorporating sentiment information into word embeddings during
training gives good results for datasets that are lexically similar to the
training data. With our experiments, we contribute to a better understanding of
the performance of different model architectures on different data sets.
Consequently, we detect novel state-of-the-art results on the SenTube datasets.
