# Detection and Prediction of Users Attitude Based on Real-Time and Batch Sentiment Analysis of Facebook Comments

## TL;DR

This paper introduces a novel sentiment analysis method for Facebook comments that detects and predicts user attitudes over time by combining real-time analysis with batch data processing, achieving high accuracy.

## Contribution

It presents a new two-step algorithm that clusters sentiment patterns and forecasts opinion trends, enhancing understanding of user attitude dynamics on social media.

## Key findings

- Achieved average MAE of 0.008 in trend prediction
- Discovered three distinct user attitude patterns
- Demonstrated practical applicability of the method

## Abstract

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.