  Aspiring to achieve an accurate Bitcoin price prediction based on people's
opinions on Twitter usually requires millions of tweets, using different text
mining techniques (preprocessing, tokenization, stemming, stop word removal),
and developing a machine learning model to perform the prediction. These
attempts lead to the employment of a significant amount of computer power,
central processing unit (CPU) utilization, random-access memory (RAM) usage,
and time. To address this issue, in this paper, we consider a classification of
tweet attributes that effects on price changes and computer resource usage
levels while obtaining an accurate price prediction. To classify tweet
attributes having a high effect on price movement, we collect all
Bitcoin-related tweets posted in a certain period and divide them into four
categories based on the following tweet attributes: $(i)$ the number of
followers of the tweet poster, $(ii)$ the number of comments on the tweet,
$(iii)$ the number of likes, and $(iv)$ the number of retweets. We separately
train and test by using the Q-learning model with the above four categorized
sets of tweets and find the best accurate prediction among them. Especially, we
design several reward functions to improve the prediction accuracy of the
Q-leaning. We compare our approach with a classic approach where all
Bitcoin-related tweets are used as input data for the model, by analyzing the
CPU workloads, RAM usage, memory, time, and prediction accuracy. The results
show that tweets posted by users with the most followers have the most
influence on a future price, and their utilization leads to spending 80\% less
time, 88.8\% less CPU consumption, and 12.5\% more accurate predictions
compared with the classic approach.
