  The most of the people have their account on social networks (e.g. Facebook,
Vkontakte) where they express their attitude to different situations and
events. Facebook provides only the positive mark as a like button and share.
However, it is important to know the position of a certain user on posts even
though the opinion is negative. Positive, negative and neutral attitude can be
extracted from the comments of users. Overall information about positive,
negative and neutral opinion can bring the understanding of how people react in
a position. Moreover, it is important to know how attitude is changing during
the time period. The contribution of the paper is a new method based on
sentiment text analysis for detection and prediction negative and positive
patterns for Facebook comments which combines (i) real-time sentiment text
analysis for pattern discovery and (ii) batch data processing for creating
opinion forecasting algorithm. To perform forecast we propose two-steps
algorithm where: (i) patterns are clustered using unsupervised clustering
techniques and (ii) trend prediction is performed based on finding the nearest
pattern from the certain cluster. Case studies show the efficiency and accuracy
(Avg. MAE = 0.008) of the proposed method and its practical applicability.
Also, we discovered three types of users attitude patterns and described them.
