  With the proliferation of its applications in various industries, sentiment
analysis by using publicly available web data has become an active research
area in text classification during these years. It is argued by researchers
that semi-supervised learning is an effective approach to this problem since it
is capable to mitigate the manual labeling effort which is usually expensive
and time-consuming. However, there was a long-term debate on the effectiveness
of unlabeled data in text classification. This was partially caused by the fact
that many assumptions in theoretic analysis often do not hold in practice. We
argue that this problem may be further understood by adding an additional
dimension in the experiment. This allows us to address this problem in the
perspective of bias and variance in a broader view. We show that the well-known
performance degradation issue caused by unlabeled data can be reproduced as a
subset of the whole scenario. We argue that if the bias-variance trade-off is
to be better balanced by a more effective feature selection method unlabeled
data is very likely to boost the classification performance. We then propose a
feature selection framework in which labeled and unlabeled training samples are
both considered. We discuss its potential in achieving such a balance. Besides,
the application in financial sentiment analysis is chosen because it not only
exemplifies an important application, the data possesses better illustrative
power as well. The implications of this study in text classification and
financial sentiment analysis are both discussed.
