Sellers readily obtain consumer product evaluations from online reviews in order to identify competitive products in detail and predict sales. Firstly, we collect product review data from shopping websites, social media, product communities, and other online platforms to identify product competitors with the help of word-frequency cooccurrence technology. We take mobile phones as an example to mine and analyze product competition information. Then, we calculate the product review quantity, review emotion value, product-network heat, and price statistics and establish the regression model of online product review forecasts. In addition, the neural-network model is established to suggest that the relationships among factors are linear. On the basis of analyzing and discussing the impact of product sales of the competitors, product price, the emotional value of the reviews, and product-network popularity, we construct the sales forecast model. Finally, to verify the validity of the factor analysis affecting the sales and the rationality of the established model, actual sales data are used to further analyze and verify the model, showing that the model is reasonable and effective.

中文翻译：

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基于消费者评论的竞争产品识别和销售预测

卖家很容易从在线评论中获得消费品评估，以便详细识别竞争产品并预测销售情况。首先，我们从购物网站、社交媒体、产品社区等网络平台收集产品评论数据，借助词频共现技术识别产品竞争对手。我们以手机为例，对产品竞争信息进行挖掘和分析。然后，我们计算产品评论数量、评论情感值、产品网络热度和价格统计，并建立在线产品评论预测的回归模型。此外，建立的神经网络模型表明因素之间的关系是线性的。在分析和讨论竞争对手产品销售、产品价格、评论的情感价值和产品网络流行度，我们构建了销售预测模型。最后，为验证影响销售的因素分析的有效性和所建立模型的合理性，利用实际销售数据对模型进行了进一步的分析和验证，表明模型的合理性和有效性。