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Rating Prediction based on Social Sentiment from Textual Reviews
  • Rating Prediction based on Social Sentiment from Textual Reviews

Rating Prediction based on Social Sentiment from Textual Reviews

Sold By Final year Student Project
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Rating Prediction based Social Sentiment Textual Reviews

In recent years, for the purchased products, we can share the viewpoints. Rating Prediction based Social Sentiment Textual Reviews However, we face an information overloading problem. To make an accurate recommendation, there is necessity to mine valuable information from reviews to understand a user’s preferences which is crucial task.

Some factors like user’s purchase records, product category, and geographic location are considered in the Traditional recommender systems (RS).Rating Prediction based Social Sentiment Textual Reviews. The proposed system provides a sentiment-based rating prediction method (RPS) to improve prediction accuracy in recommender systems.

Firstly, a social user sentimental measurement approach to calculate each user’s sentiment on items/products is proposed. Not only the users own sentimental attributes but also the interpersonal sentimental influence is taken into consideration.

Then, we consider product reputation, which can be inferred by the sentimental distributions of a user set that reflect customers’ comprehensive evaluation.Rating Prediction based Social Sentiment Textual Reviews. At last, the three factors-user sentiment similarity, interpersonal sentimental influence, and item’s reputation similarity-into our recommend system to make an accurate rating prediction are combined.

By the performance evaluation of the three sentimental factors on a real-world dataset collected from Yelp shows that sentiment can well characterize user preferences, which helps to improve the recommendation performance.

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