论文标题
使用多项式幼稚的贝叶斯在孟加拉书评中的情感极性检测
Sentiment Polarity Detection on Bengali Book Reviews Using Multinomial Naive Bayes
论文作者
论文摘要
最近，由于客户在在线平台上的观点或评论的大量供应，情感极性检测增加了对NLP研究人员的关注。由于电子商务网站的持续扩展，包括书籍在内的各种产品的购买速度在人们之间的巨大增长。在大多数情况下，读者的意见/评论会影响客户的购买决定。这项工作介绍了一种基于机器学习的技术，以确定孟加拉书评中的情感极性（正面或负面类别）。为了评估拟议技术的有效性，开发了一本关于孟加拉书籍的评论的语料库。还通过分别考虑了米格拉姆，bigram和trigram特征，对各种方法（例如逻辑回归，天真的贝叶斯，SVM和SGD）进行了比较分析。实验结果表明，具有UMIGRAM特征的多项式天真贝叶斯在测试集上的精度为84％的其他技术。
Recently, sentiment polarity detection has increased attention to NLP researchers due to the massive availability of customer's opinions or reviews in the online platform. Due to the continued expansion of e-commerce sites, the rate of purchase of various products, including books, are growing enormously among the people. Reader's opinions/reviews affect the buying decision of a customer in most cases. This work introduces a machine learning-based technique to determine sentiment polarities (either positive or negative category) from Bengali book reviews. To assess the effectiveness of the proposed technique, a corpus with 2000 reviews on Bengali books is developed. A comparative analysis with various approaches (such as logistic regression, naive Bayes, SVM, and SGD) also performed by taking into consideration of the unigram, bigram, and trigram features, respectively. Experimental result reveals that the multinomial Naive Bayes with unigram feature outperforms the other techniques with 84% accuracy on the test set.
