Threshold-based Naïve Bayes classifier

IF 1.4 4区计算机科学 Q2 STATISTICS & PROBABILITY Advances in Data Analysis and Classification Pub Date : 2023-03-14 DOI:10.1007/s11634-023-00536-8

Maurizio Romano, Giulia Contu, Francesco Mola, Claudio Conversano

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Abstract

The Threshold-based Naïve Bayes (Tb-NB) classifier is introduced as a (simple) improved version of the original Naïve Bayes classifier. Tb-NB extracts the sentiment from a Natural Language text corpus and allows the user not only to predict how much a sentence is positive (negative) but also to quantify a sentiment with a numeric value. It is based on the estimation of a single threshold value that concurs to define a decision rule that classifies a text into a positive (negative) opinion based on its content. One of the main advantage deriving from Tb-NB is the possibility to utilize its results as the input of post-hoc analysis aimed at observing how the quality associated to the different dimensions of a product or a service or, in a mirrored fashion, the different dimensions of customer satisfaction evolve in time or change with respect to different locations. The effectiveness of Tb-NB is evaluated analyzing data concerning the tourism industry and, specifically, hotel guests’ reviews from all hotels located in the Sardinian region and available on Booking.com. Moreover, Tb-NB is compared with other popular classifiers used in sentiment analysis in terms of model accuracy, resistance to noise and computational efficiency.

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基于阈值的奈夫贝叶斯分类器

基于阈值的奈夫贝叶斯（Tb-NB）分类器是原始奈夫贝叶斯分类器的（简单）改进版。Tb-NB 从自然语言文本语料库中提取情感，用户不仅可以预测句子的正面（负面）程度，还可以用数值量化情感。它的基础是对单一阈值的估计，该阈值可以定义一条决策规则，根据文本内容将其归类为正面（负面）观点。Tb-NB 的主要优势之一是可以利用其结果作为事后分析的输入，目的是观察与产品或服务的不同维度相关的质量，或者以镜像方式观察客户满意度的不同维度是如何随时间演变或随不同地点变化的。对 Tb-NB 的有效性进行了评估，分析了与旅游业相关的数据，特别是撒丁岛地区所有酒店（Booking.com 上提供）的客人评价。此外，Tb-NB 还在模型准确性、抗干扰性和计算效率方面与情感分析中使用的其他流行分类器进行了比较。

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来源期刊

Advances in Data Analysis and Classification STATISTICS & PROBABILITY-

CiteScore

3.40

自引率

6.20%

发文量

审稿时长

>12 weeks

期刊介绍： The international journal Advances in Data Analysis and Classification (ADAC) is designed as a forum for high standard publications on research and applications concerning the extraction of knowable aspects from many types of data. It publishes articles on such topics as structural, quantitative, or statistical approaches for the analysis of data; advances in classification, clustering, and pattern recognition methods; strategies for modeling complex data and mining large data sets; methods for the extraction of knowledge from data, and applications of advanced methods in specific domains of practice. Articles illustrate how new domain-specific knowledge can be made available from data by skillful use of data analysis methods. The journal also publishes survey papers that outline, and illuminate the basic ideas and techniques of special approaches.