Research On Method And Technology Of Product Optimization Assistant Decision Information Acquisition Based On Online Comment Data

In the era of network data,with the intensification of personalized and global market competition,customer's commentary information is of great value to the company's operation and management,and is of great significance for product update and upgrade.Enterprises can better understand customer needs and their changing trends according to customer's commentary information,and get feedback information such as product quality and product defects,so that products can be optimized and upgraded in a targeted manner.However,the current online comment data is mostly unstructured data.In order to obtain important information related to products in online reviews,the thesis uses in-depth learning and machine learning related technologies to conduct in-depth research on the acquisition of product optimization information in online reviews.Proposes a product optimization strategy based on comment data,which aims to provide enterprises with optimized decision-making reference.The work of this paper mainly includes the following parts:The thesis proposes a technique and method for customer demand acquisition and evaluation based on comment data.Firstly,the feature elements of the product are extracted from the online comments through the method of TF and word vector;next we use product feature mining and semantics analysis to express customer's requirement structurally;then,the importance of customer needs is assessed through qualitative and quantitative analysis methods;finally,the results of the example analysis show that the method has a good effect,can effectively obtain and evaluate customer needs,and find important customer opinions.The thesis uses online comment data for product optimization,providing a new data support for product optimization and improving the scientific decision-making.This thesis proposes a trend analysis and application method of customer demandbased on comment information,which is used to explore the development law of customer demand and master the important factors affecting customer consumption at different stages of development.Firstly,we introduce the theory of hierarchy needs and the evolution trend of human needs;next,based on the above theoretical basis,the hierarchical analysis of customer demand in the review is carried out,and the hierarchical model of customer demand in online commentary is proposed;then,according to the construction The hierarchical model of customer demand divides the product attributes of customers at different levels of demand,and carries out verification analysis on customer demand hierarchy and advantage demand through data analysis;finally,the application research of customer demand hierarchy in the review is carried out,and a product optimization analysis method based on customer demand hierarchy is proposed.The thesis puts forward the automatic acquisition technology of product optimization information,and which can automatically obtain the optimization information needed for product optimization from the comments.Firstly,calculate the indicators such as customer attention and satisfaction in online reviews,and construct a weighting algorithm model for customer opinions;next,the word pairs of product characteristics and customer opinions are extracted,and the weight of customer opinions is calculated according to the weight algorithm model;then,the corresponding product optimization information is found through the correlation matrix;finally,the feasibility of the method has been verified by an example.