Estimating Public Opinion in Social Media Content Using Aspect-Based Opinion Mining.

With the development of the Internet, social media has been the main platform for human to express opinions about products/services, key figures, socio-political and economic events. Besides the benefits that the platform offers, there are still various security threats relating to the fact that most extremist groups have been abusing social media to spread distorted beliefs, to incite the act of terrorism, politics, religions, to recruit, to raise funds and much more. These groups tend to include sentiment leading to illegal affairs such as terrorism, cyber-attacks, etc. when sharing their opinions and comments. Therefore, it is necessary to capture public opinions and social behaviors in social media content. This is a challenging research topic related to aspect-based opinion mining, which is the problem of determining what the exact opinions on specific aspects are rather than getting an overall positive or negative sentiment at the document level. For an entity, the main task is to detect all mentioned aspects of the entity and then produce a summary of each aspect's sentiment orientation. This paper proposes an aspect-based opinion mining model to address the problem of estimating public opinion in social media content. The model has two phases: 1 - extracting aspects based on double propagation techniques, and 2 - classifying opinions about the detected aspects with the consideration of the context of review sentences using the hybrid approach of machine learning and lexicon-based method.