TY  - JOUR
T1  - Measuring Semantic Orientation of Words using Temporal Difference Learning
AU  - Kim, Youngsam 
AU  - Shin, Hyopil 
JO  - Journal of KIISE, JOK
PY  - 2018
DA  - 2018/1/14
DO  - 10.5626/JOK.2018.45.12.1287
KW  - temporal-difference learning
KW  - reinforcement learning
KW  - semantic orientation of words
KW  - incremental processing
KW  - asynchronous parallel processing
AB  - 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.