  Stock market prediction has been an important topic for investors,
researchers, and analysts. Because it is affected by too many factors, stock
market prediction is a difficult task to handle. In this study, we propose a
novel method that is based on deep reinforcement learning methodologies for the
direction prediction of stocks using sentiments of community and knowledge
graph. For this purpose, we firstly construct a social knowledge graph of users
by analyzing relations between connections. After that, time series analysis of
related stock and sentiment analysis is blended with deep reinforcement
methodology. Turkish version of Bidirectional Encoder Representations from
Transformers (BerTurk) is employed to analyze the sentiments of the users while
deep Q-learning methodology is used for the deep reinforcement learning side of
the proposed model to construct the deep Q network. In order to demonstrate the
effectiveness of the proposed model, Garanti Bank (GARAN), Akbank (AKBNK),
T\"urkiye \.I\c{s} Bankas{\i} (ISCTR) stocks in Istanbul Stock Exchange are
used as a case study. Experiment results show that the proposed novel model
achieves remarkable results for stock market prediction task.
