论文标题
阿拉伯观点采矿使用混合建议系统方法
Arabic Opinion Mining Using a Hybrid Recommender System Approach
论文作者
论文摘要
如今，推荐系统在向用户提供服务和信息方面发挥了重要作用。情感分析（也称为意见挖掘）是确定文本意见态度的过程，无论它们是正面的，消极的还是中立的。由于用户评级或缺少有关用户或项目的数据的不足，数据稀少度代表了推荐系统的大问题。这项研究提出了一种混合方法，结合了情感分析和推荐系统，以通过预测使用文本挖掘和NLP技术从用户评论中的产品评估来解决数据稀疏问题的问题。这项研究尤其着重于阿拉伯语评论，其中使用阿拉伯语（OCA）数据集评估模型。我们的系统效率很高，它在预测评论的评级方面表现出近85％的良好精度
Recommender systems nowadays are playing an important role in the delivery of services and information to users. Sentiment analysis (also known as opinion mining) is the process of determining the attitude of textual opinions, whether they are positive, negative or neutral. Data sparsity is representing a big issue for recommender systems because of the insufficiency of user rating or absence of data about users or items. This research proposed a hybrid approach combining sentiment analysis and recommender systems to tackle the problem of data sparsity problems by predicting the rating of products from users reviews using text mining and NLP techniques. This research focuses especially on Arabic reviews, where the model is evaluated using Opinion Corpus for Arabic (OCA) dataset. Our system was efficient, and it showed a good accuracy of nearly 85 percent in predicting rating from reviews
