利用基于特征的意见购买最佳产品的决策框架

Ankur Ratmele, Ramesh Thakur
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引用次数: 0

摘要

随着越来越多的人在各种网络购物平台上表达他们对产品的想法,这些意见所表达的感受正成为营销人员和买家的重要信息来源。这些意见对消费者购买最优质产品的决定有很大影响。当需要分析的功能太多或记录太少时,决策过程就会变得困难。在这种情况下,最近的一项研究采用了传统的量化星级评分和文本内容评论。本研究提出了一个决策框架,该框架依靠基于特征的意见来分析评论的文本内容并对买家的意见进行分类,从而帮助消费者进行长期购买。本文提出了基于特征意见和深度学习的产品购买决策框架。该框架由四个部分组成:i) 预处理;ii) 特征提取;iii) 基于特征的意见分类;iv) 决策制定。通过网络搜刮获取智能手机评论数据集,然后使用标记化和 POS 标记对数据集进行清理和预处理。从标记的数据集中检索名词标签词,然后提取可能的产品特征。这些基于特征的句子或评论通过词嵌入处理,生成可识别上下文信息的评论向量。使用分层关注法,这些单词向量可用于构建单词和句子级别的隐藏向量。就每个特征而言,评论被分为五个等级:极度正面、正面、极度负面、负面和中性。所提出的方法可以很容易地检测出客户基于某一属性对产品质量的看法,这有利于客户做出购买选择。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A framework for decision making to purchase the best product using feature-based opinions
As more people express their thoughts on products on various online shopping platforms, the feelings expressed in these opinions are becoming a significant source of information for marketers and buyers. These opinions have a big impact on consumers’ decision to buy the best quality product. When there are too many features or a small number of records to analyze, the decision-making process gets difficult. A recent stream of study has used the conventional quantitative star score ratings and textual content reviews in this context. In this research, a decision-making framework is proposed that relies on feature-based opinions to analyze the textual content of reviews and classify buyer’s opinions, thereby assisting consumers in making long-term purchases. The framework is proposed in this paper for product purchase decision making based on feature-based opinions and deep learning. Framework consists of four components: i) Pre-processing, ii) Feature extraction, iii) Feature-based opinion classification, and iv) Decision-making. Web scraping is used to obtain the dataset of Smartphone reviews, which is subsequently clean and pre-processed using tokenization and POS tagging. From the tagged dataset, noun labeled words are retrieved, and then the probable product’s features are extracted. These feature-based sentences or reviews are processed using a word embedding to generate review vectors that identify contextual information. These word vectors are used to construct hidden vectors at the word and sentence levels using a hierarchical attention method. With respect to each feature, reviews are divided into five classes: extremely positive, positive, extremely negative, negative, and neutral. The proposed method may readily detect a customer’s opinion on the quality of a product based on a certain attribute, which is beneficial in making a purchase choice.
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