A Crowdsourcing Based Framework for Sentiment Analysis: A Product Reputation
Abstract
As social networking has spread, people started sharing their personal opinions and thoughts widely via these online platforms. The resulting vast valuable data represent a rich source for companies to deduct their products’ reputation from both social media and crowds’ judgments. To exploit this wealth of data, a framework was proposed to collect opinions and rating scores respectively from social media and 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 that ensures that the results obtained from social media are supported by opinions extracted directly from real-life consumers. Finally, experiments are carried out to validate this model (Twitter and Facebook were used as data sources). The obtained results show that this approach is more efficient and accurate than existing solutions thanks to our two-layer validity check design.
Keywords
Crowdsourcing, Product Reputation, Sentiment analysis, Social Media, Subjectivity ClassificationThis work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
F. Ennaji, A. El Fazziki, H. El Alaoui El Abdallaoui and H. El Kabtane, "A Crowdsourcing Based Framework for Sentiment Analysis: A Product Reputation," in Journal of Communications Software and Systems, vol. 16, no. 4, pp. 285-295, October 2020, doi: 10.24138/jcomss.v16i4.935
@article{ennaji2020crowdsourcingbased, author = {Fatima Zohra Ennaji and Abdelaziz El Fazziki and Hasna El Alaoui El Abdallaoui and Hamada El Kabtane}, title = {A Crowdsourcing Based Framework for Sentiment Analysis: A Product Reputation}, journal = {Journal of Communications Software and Systems}, month = {10}, year = {2020}, volume = {16}, number = {4}, pages = {285--295}, doi = {10.24138/jcomss.v16i4.935}, url = {https://doi.org/10.24138/jcomss.v16i4.935} }