Product Aspect Ranking and Its Applications

PRODUCT ASPECT RANKING AND ITS APPLICATIONS

ABSTRACT:

Numerous consumer reviews of products are now available on the Internet. Consumer reviews contain rich and valuable knowledge for both firms and users. However, the reviews are often disorganized, leading to difficulties in information navigation and knowledge acquisition. This article proposes a product aspect ranking framework, which automatically identifies the important aspects of products from online consumer reviews, aiming at improving the usability of the numerous reviews. The important product aspects are identified based on two observations: (a) the important aspects are usually commented by a large number of consumers; and (b) consumer opinions on the important aspects greatly influence their overall opinions on the product. In particular, given the consumer reviews of a product, we first identify product aspects by a shallow dependency parser and determine consumer opinions on these aspects via a sentiment classifier. We then develop a probabilistic aspect ranking algorithm to infer the importance of aspects by simultaneously considering aspect frequency and the influence of consumer opinions given to each aspect over their overall opinions. The experimental results on a review corpus of 21 popular products in eight domains demonstrate the effectiveness of the proposed approach. Moreover, we apply product aspect ranking to two real-world applications, i.e., document-level sentiment classification and extractive review summarization, and achieve significant performance improvements, which demonstrate the capacity of product aspect ranking in facilitating real-world applications.

EXISTING SYSTEM:

Generally, a product may have hundreds of aspects. For example, iPhone 3GS has more than three hundred aspects (see Fig. 1), such as “usability,” “design,” “application,” “3G network.” We argue that some aspects are more important than the others, and have greater impact on the eventual consumers’ decision making as well as firms’ product development strategies. For example, some aspects of iPhone 3GS, e.g., “usability” and “battery,” are concerned by most consumers, and are more important than the others such as “usb” and “button.” For a camera product, the aspects such as “lenses” and “picture quality” would greatly influence consumer opinions on the camera, and they are more important than the aspects such as “a/v cable” and “wrist strap.” Hence, identifying important product aspects will improve the usability of numerous reviews and is beneficial to both consumers and firms. Consumers can conveniently make wise purchasing decision by paying more attentions to the important aspects, while firms can focus on improving the quality of these aspects and thus enhance product reputation effectively.

DISADVANTAGES OF EXISTING SYSTEM:

v Identifying aspects in the free text reviews, a straightforward solution is to employ an existing aspect identification approach.

v The network lifetime may even be reduced.

v They do not necessarily minimize the energy consumption for E2E packet traversal.

PROPOSED SYSTEM:

We in this paper propose a product aspect ranking framework to automatically identify the important aspects of products from online consumer reviews. Our assumption is that the important aspects of a product possess the following characteristics: (a) they are frequently commented in consumer reviews; and (b) consumers’ opinions on these aspects greatly influence their overall opinions on the product. A straightforward frequency-based solution is to regard the aspects that are frequently commented in consumer reviews as important. However, consumers’ opinions on the frequent aspects may not influence their overall opinions on the product, and would not influence their purchasing decisions.

ADVANTAGES OF PROPOSED SYSTEM:

v It first identifies the nouns and noun phrases in the documents. The occurrence frequencies of the nouns and noun phrases are counted, and only the frequent ones are kept as aspects.

v The language model was built on product reviews, and used to predict the related scores of the candidate aspects. The candidates with low scores were then filtered out.

SYSTEM ARCHITECTURE:

SYSTEM CONFIGURATION:-

HARDWARE REQUIREMENTS:-

Processor                  -        Pentium –IV

Speed                        -        1.1 Ghz

RAM                         -        512 MB(min)

Hard Disk                  -        40 GB

Key Board                -        Standard Windows Keyboard

Mouse                       -        Two or Three Button Mouse

Monitor                     -        LCD/LED

SOFTWARE REQUIREMENTS:

Operating system      :         Windows XP.

Coding Language      :         .Net

Data Base                 :         SQL Server 2005

Tool                          :         VISUAL STUDIO 2008.

REFERENCE:

Zheng-Jun Zha Member, IEEE Jianxing Yu, Meng Wang and Tat-Seng Chua, “Product Aspect Ranking and Its Applications” IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 26, NO. 5, MAY 2014