A crowdsourcing based framework for sentiment analysis: A product reputation

Abstract

As social media has spread, people started sharing their personal opinions and thoughts widely via these online platforms. This valuable data represents a rich data source for companies to deduct their products' reputation from both social media and crowds' judgments. To exploit this data, a framework was proposed to collect opinions and rating scores respectively from social media and a crowdsourcing platform to perform sentiment analysis, provide insights about a product and give consumers' tendencies. During the analysis process, a consumer category (strict) is excluded from the process of reaching a majority consensus. To overcome this, a fuzzy clustering is used to compute consumers' credibility. The key novelty of our approach is the new layer of validity check using a crowdsourcing component which collects the opinions expressed by the participants of the crowd. Finally, experiments are carried out to validate this model (Twitter and Facebook were used as data sources). The results show that this approach is more efficient and accurate than existing solutions thanks to our two-layer validity check design.