Performance Analysis of Sentiment Fusion Network for Social Media Services

Recent advancements in various social media platforms allow fusion of different sentiments on various online platforms. Thus, multimedia aspects contribute the common set of sentiment transfer, sharing opinions and other sentiment resources over various online channels. The sentiment fusion offers online users a sentiment access through a various unified interfaces. Since Sentiment Transfer Mode (STM) support sentiment transfer through various text and data files therefore, STM has been chosen for the use in the Sentiment Fusion Networks (S-FSN). This paper proposed a Quantitative Sentiment model for sentiment transfer which includes text, and visuals data collected from various online channels. This model can help to analyse single sentiment detection over single-multimedia (SD/1/1) heavy network. The waiting time for sentiment detection for the selected parameters in each channel is derived explicitly. This study also formulated equations to describe the fusion function responsible for generating sentiment within the multimedia system, as well as the delay associated with detecting sentiment for the specific parameters examined in the research.