Designing a Recommendation System of Detergent Products with Fuzzy Sentiment Analysis Approach

Today, social media has become a place for users to comment on various topics, including consumer products, and these comments have become a valuable resource for sentiments analyzing and extracting customer expectations of products. This subject provides companies with a good opportunity to redesign their products based on user feedback. In this study, to provide recommendations to the design units of detergent products, we used sentiment analysis of customers and consumers of this products on social media. More than 4200 tweets were extracted from Twitter in 2019 based on the research topic and refined and tagged during the pre-processing of the texts. Afterwards, we analyzed the emotions using fuzzy logic and topic modeling. We have used topic modeling to find the features mentioned in the comments for a better approach in the design units, and fuzzy logic to obtain the degree of polarity of ideas into 5 categories: very positive, positive, neutral, negative and very negative. We used confusion matrix for evaluating research model and an accuracy of 86.15 % has been recorded.In this research Python libraries are used for data gathering, cleansing and analysis.