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- W4382053321 abstract "In recent years, the popularity of social networking sites has skyrocketed. Social media allows individuals to express their opinions on almost every topic. These opinions take variety of ways, such as tweets, Instagram posts, blogs, Facebook posts, and so on. The process of describing and categorizing the opinions represented in the form of post, document or comment is known as sentiment analysis. The goal of sentiment analysis is typically to determine the attitude of customers toward a good or service. Customer's feedback is critical for organizations and social media that can serve as a promising tool to enhance and improve business opportunities if reviews have been evaluated on social media in a timely manner. As a result, the purpose of this study is to examine online restaurant reviews given by customers. The proposed research makes two contributions. First, it conducts sentiment analysis (SA) and categorizes each and every comment given in the text into five labels i.e.; positive, negative, neutral, highly positive and highly negative. Second, comments are automatically characterized using text categorization techniques based on the feedback from the customers about taste, service, ambiance, food, and value for money. For training and testing, dataset available from Kaggle was used. For classification, numerous algorithms were employed, such as the Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), and Naive Bayes Classifier (NB). This method evaluates the performance of these algorithms and random forest algorithm produced the best results, with highest accuracy." @default.
- W4382053321 created "2023-06-27" @default.
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- W4382053321 date "2023-04-05" @default.
- W4382053321 modified "2023-09-23" @default.
- W4382053321 title "Feature level Fine Grained Sentiment Analysis for Classifying Online Restaurant Reviews" @default.
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- W4382053321 doi "https://doi.org/10.1109/iceeict56924.2023.10157159" @default.
- W4382053321 hasPublicationYear "2023" @default.
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