@article{M7E1A88CE,
title = "Measuring Semantic Orientation of Words using Temporal Difference Learning",
journal = "Journal of KIISE, JOK",
year = "2018",
issn = "2383-630X",
doi = "10.5626/JOK.2018.45.12.1287",
author = "Youngsam Kim,Hyopil Shin",
keywords = "temporal-difference learning,reinforcement learning,semantic orientation of words,incremental processing,asynchronous parallel processing",
abstract = "Temporal-difference(TD) learning is a core algorithm of reinforcement learning, which employs models of Markov process. In the TD methods, rewards are always discounted by a discount factor and states receive these discounted values as their rewards. In this paper, we attempted to estimate a semantic orientation of words in texts using the TD-based methods and examined the effectiveness of the proposed methods by comparing them to existing feature selection methods (indirect approach) and Bayes probabilities (direct approach). The TD-based estimation would be useful for tasks of social opinion mining, since TD learning is inherently an on-line method. In order to show our approach is scalable to huge data, the estimation method is also evaluated using asynchronous parallel processing."
}