  Increasing attention has been drawn to the sentiment analysis of financial
documents. The most popular examples of such documents include analyst reports
and economic news, the analysis of which is frequently used to capture the
trends in market sentiments. On the other hand, the significance of the role
sentiment analysis plays in the financial domain has given rise to the efforts
to construct a financial domain-specific sentiment lexicon. Sentiment lexicons
lend a hand for solving various text mining tasks, such as unsupervised
classification of text data, while alleviating the arduous human labor required
for manual labeling. One of the challenges in the construction of an effective
sentiment lexicon is that the semantic orientation of a word may change
depending on the context in which it appears. For instance, the word ``profit"
usually conveys positive sentiments; however, when the word is juxtaposed with
another word ``decrease," the sentiment associated with the phrase ``profit
decreases" now becomes negative. Hence, the sentiment of a given word may shift
as one begins to consider the context surrounding the word. In this paper, we
address this issue by incorporating context when building sentiment lexicon
from a given corpus. Specifically, we construct a lexicon named Senti-DD for
the Sentiment lexicon composed of Direction-Dependent words, which expresses
each term a pair of a directional word and a direction-dependent word.
Experiment results show that higher classification performance is achieved with
Senti-DD, proving the effectiveness of our method for automatically
constructing a context-aware sentiment lexicon in the financial domain.
