<![CDATA[Social media plays a significant role in enhancing communication among organizations, communities, and individuals. Besides being a mode of communication, the data generated from these interactions can also be leveraged to assess the performance of an institution or organization. People may evaluate public companies based on the opinions of their users. However, user-supplied information is brief and written in natural language. In addition to being brief, the process of sending messages or engaging in other social media interactions contains a great deal of context information. This multiplicity of context can be utilized to conduct a more in-depth analysis of user opinion. This study presents a new approach to opinion mining for social media microblogging data by applying an affective model and contextual analyses. The affective model is applied for sentiment analysis to measure the degree of each adjective from user opinion by evaluating adjectives according to their varying levels of pleasure and arousal. The contextual analysis in this paper is modeled based on topic, user, adjective, and personal characteristics. The contextual analysis has four main features: (1) Temporal keyword sentiment context, (2) Temporal user sentiment context, (3) User impression context, and (4) Temporal user character context. Our affective model outperformed 75.6% the accuracy and 74.98% of F1-score, rather than SVM. In the experiment, the contextual analysis performed graph visualization of output results for each query feature for future development. Feature one to four successfully processes the query to produce a visualization graph.]]>
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