Abstract: Recent years have seen an increasing number of patent disputes due to excessive competition in the global market and a reduced technology life-cycle; this has increased the risk of investment in technology development. While many global companies have started developing a methodology to identify promising technologies and assess for decisions, the existing methodology still has some limitations. Post hoc assessments of the new technology are not being performed, especially to determine whether the suggested technologies turned out to be promising. For example, in existing quantitative patent analysis, a patent’s citation information has served as an important metric for quality assessment, but this analysis cannot be applied to recently registered patents because such information accumulates over time. Therefore, we propose a new technology assessment model that can replace citation information and positively affect technological development based on post hoc analysis of the patents for promising technologies. Additionally, we collect customer reviews on a target technology to extract keywords that show the customers’ needs, and we determine how many keywords are covered in the new technology. Finally, we construct a portfolio (based on a technology assessment from patent information) and a customer-based marketability assessment (based on review data), and we use them to visualize the characteristics of the new technologies.
Abstract: Opinion extraction about products from customer
reviews is becoming an interesting area of research. Customer
reviews about products are nowadays available from blogs and
review sites. Also tools are being developed for extraction of opinion
from these reviews to help the user as well merchants to track the
most suitable choice of product. Therefore efficient method and
techniques are needed to extract opinions from review and blogs. As
reviews of products mostly contains discussion about the features,
functions and services, therefore, efficient techniques are required to
extract user comments about the desired features, functions and
services. In this paper we have proposed a novel idea to find features
of product from user review in an efficient way. Our focus in this
paper is to get the features and opinion-oriented words about
products from text through auxiliary verbs (AV) {is, was, are, were,
has, have, had}. From the results of our experiments we found that
82% of features and 85% of opinion-oriented sentences include AVs.
Thus these AVs are good indicators of features and opinion
orientation in customer reviews.