Tracking Dynamics of Opinion Behaviors with a Content-based Sequential Opinion Influence Model

ABSTRACT:

These days, online life has turned into a well known channel for individuals to trade suppositions through the client created content. Investigating the components about how clients’ suppositions towards items are impacted by companions, and further anticipating their future sentiments have pulled in extraordinary consideration from corporate directors and specialists.

Different impact models have just been proposed for the conclusion expectation issue. In any case, they to a great extent define feelings as inferred assessment classifications or qualities however disregard the job of the substance data. In addition, existing models just make utilization of the most as of late gotten data without mulling over the long haul verifiable correspondence.

To monitor client feeling practices and induce client supposition impact from the chronicled traded literary data, we build up a substance based successive conclusion impact system. In light of this structure, two assessment opinion expectation models with elective forecast procedures are proposed. In the tests directed on three Twitter datasets, the proposed models beat other well known impact models. A fascinating finding dependent on a further investigation of client trademark is that a person’s impact is connected to her/his style of articulations

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