Discovering High-Profit Product Feature Groups by mining High Utility Sequential Patterns from Feature-Based Opinions

Extracting a group of features together instead of a single feature from the mined opinions, such as “{battery, camera, design} of a smartphone,” may yield higher profit to the manufactures and higher customer satisfaction, and these can be called High Profit Feature Groups (HPFG). The accuracy of Opinion-Feature Extraction can be improved if more complex sequential patterns of customer reviews are learned and included in the user-behavior analysis to obtain relevant frequent feature groups. Existing Opinion-Feature Extraction systems that use Data Mining techniques with some sequences include those referred to in this thesis as Rashid13OFExt, Rana18OFExt, and HPFG19_HU. Rashid13OFExt and Rana18OFExt systems use Sequential Pattern Mining, Association Rule Mining, and Class Sequential Rules to obtain frequent product features and opinion words from reviews. However, these systems do not discover the frequent high profit features considering utility values (internal and external) such as cost, profit, quantity, or other user preferences. HPFG19_HU system uses High Utility Itemset Mining and Aspect-Based Sentiment Analysis to extract High Utility Aspect groups based on feature-opinion sets. It works on transaction databases of itemsets formed using aspects by considering the high utility values (e.g., are more profitable to the seller?) from the extracted frequent patterns from a set of opinion sentences. However, the HPFG19_HU system does not consider the order of occurrences (sequences) of product features formed in customer opinion sentences that help distinguish similar users and identifying more relevant and related high profit product features. This thesis proposes a system called High Profit Sequential Feature Group based on High Utility Sequences (HPSFG_HUS), which is an extension to the HPFG19_HU system. The proposed system combines Feature-Based Opinion Mining and High Utility Sequential Pattern Mining to extract High Profit Feature Groups from product reviews. The input to the proposed system is the product reviews corpus. The output is the High Profit Sequential Feature Groups in sequence databases that identify sequential patterns in the features extracted from opinions by considering the order of occurrences of features in the review. This method improves on existing system\u27s accuracy in extracting relevant frequent feature groups. The results on retailer’s graphs of extracted High Profit Sequential Feature Groups show that the proposed HPSFG_HUS system provides more accurate high feature groups, sales profit, and user satisfaction. Experimental results evaluating execution time, accuracy, precision, and comparison show higher revenue than the tested existing systems

Extracting High Profit Sequential Feature Groups of Products Using High Utility Sequential Pattern Mining

Creating a set of product features obtained through mining users’ opinions helps retailers identify the attributes (features or aspects) more accurately and discover the most preferred features of a certain product. High Profit Feature Groups are created by extracting such product feature groups such as ‘{batterylife, camera} of a smartphone,’ which results in higher profit for manufacturers and increased consumer satisfaction. The accuracy of opinion-feature extraction systems can be improved if more complex sequential patterns of customer reviews are included in the user-behavior analysis to obtain relevant feature groups. An existing system referred to in this paper as HPFG19_HU uses High Utility Itemset Mining and Aspect-Based Sentiment Analysis to obtain high profit aspects considering the high utility values, but it does not consider the order of occurrences (sequences) of features formed in customers’ opinion sentences that help distinguish similar users and identify more relevant and related high profit product features. This paper proposes a High Profit Sequential Feature Groups based on the High Utility Sequences (HPSFG_HUS) system, which identifies sequential patterns in features. It combines Opinion Mining with High Utility Sequential Pattern Mining. This approach provides more accurate high feature groups, sales profit, and customer satisfaction, as shown by the retailer’s graphs of extracted High Profit Sequential Feature Groups. Experiments with evaluation results of execution time and evaluation metrics show that this system generates higher revenue than the tested existing systems

Network

The goal of Digital forensics process is to preserve any evidence in its most original form while performing a structured investigation by collecting, identifying and validating the digital information for the investigation of particular digital crime. Today we are living in the information age, all the information which is transferred over the internet is through the digital devices. With the advent of world-wide web, advanced forms of digital crimes came into picture. Criminal uses the Digital devices to commit Digital crime, so for the investigation forensic Experts have to adopt practical frameworks and methods to recover data for analysis which can comprise as evidence. Investigation of Digital forensics adopts three essential processes: Data Generation, Data Preparation and Data warehousing. Data Mining has unlimited potential in the field of Digital Forensics. Computer forensics is an emerging discipline investigating the computer crime. Computer /Cybe