2 research outputs found
Development of an Entropy-Based Feature Selection Method and Analysis of Online Reviews on Real Estate
In recent years, data posted about real estate on the Internet is currently
increasing. In this study, in order to analyze user needs for real estate, we
focus on "Mansion Community" which is a Japanese bulletin board system
(hereinafter referred to as BBS) about Japanese real estate. In our study,
extraction of keywords is performed based on the calculation of the entropy
value of each word, and we used them as features in a machine learning
classifier to analyze 6 million posts at "Mansion Community". As a result, we
achieved a 0.69 F-measure and found that the customers are particularly
concerned about the facility of apartment, access, and price of an apartment
Automatic Domain Adaptation Outperforms Manual Domain Adaptation for Predicting Financial Outcomes
In this paper, we automatically create sentiment dictionaries for predicting
financial outcomes. We compare three approaches: (I) manual adaptation of the
domain-general dictionary H4N, (ii) automatic adaptation of H4N and (iii) a
combination consisting of first manual, then automatic adaptation. In our
experiments, we demonstrate that the automatically adapted sentiment dictionary
outperforms the previous state of the art in predicting the financial outcomes
excess return and volatility. In particular, automatic adaptation performs
better than manual adaptation. In our analysis, we find that annotation based
on an expert's a priori belief about a word's meaning can be incorrect --
annotation should be performed based on the word's contexts in the target
domain instead.Comment: Accepted at ACL201