Predictive based hybrid ranker to yield significant features in writer identification

The contribution of writer identification (WI) towards personal identification in biometrics traits is known because it is easily accessible, cheaper, more reliable and acceptable as compared to other methods such as personal identification based DNA, iris and fingerprint. However, the production of high dimensional datasets has resulted into too many irrelevant or redundant features. These unnecessary features increase the size of the search space and decrease the identification performance. The main problem is to identify the most significant features and select the best subset of features that can precisely predict the authors. Therefore, this study proposed the hybridization of GRA Features Ranking and Feature Subset Selection (GRAFeSS) to develop the best subsets of highest ranking features and developed discretization model with the hybrid method (Dis-GRAFeSS) to improve classification accuracy. Experimental results showed that the methods improved the performance accuracy in identifying the authorship of features based ranking invariant discretization by substantially reducing redundant features

Ma et al.: An LDA and Synonym Lexicon Based Approach to Product Feature Extraction AN LDA AND SYNONYM LEXICON BASED APPROACH TO PRODUCT FEATURE EXTRACTION FROM ONLINE CONSUMER PRODUCT REVIEWS

ABSTRACT Consumers are increasingly relying on other consumers' online reviews of features and quality of products while making their purchase decisions. However, the rapid growth of online consumer product reviews makes browsing a large number of reviews and identifying information of interest time consuming and cognitively demanding. Although there has been extensive research on text review mining to address this information overload problem in the past decade, the majority of existing research mainly focuses on the quality of reviews and the impact of reviews on sales and marketing. Relatively little emphasis has been placed on mining reviews to meet personal needs of individual consumers. As an essential first step toward achieving this goal, this study proposes a product feature-oriented approach to the analysis of online consumer product reviews in order to support feature-based inquiries and summaries of consumer reviews. The proposed method combines LDA (Latent Dirichlet Allocation) and a synonym lexicon to extract product features from online consumer product reviews. Our empirical evaluation using consumer reviews of four products shows higher effectiveness of the proposed method for feature extraction in comparison to association rule mining

Predictive Based Hybrid Ranker To Yield Significant Features In Writer Identification

The contribution of writer identification (WI) towards personal identification in biometrics traits is known because it is easily accessible, cheaper, more reliable and acceptable as compared to other methods such as personal identification based DNA, iris and fingerprint. However, the production of high dimensional datasets has resulted into too many irrelevant or redundant features. These unnecessary features increase the size of the search space and decrease the identification performance. The main problem is to identify the most significant features and select the best subset of features that can precisely predict the authors. Therefore, this study proposed the hybridization of GRA Features Ranking and Feature Subset Selection (GRAFeSS) to develop the best subsets of highest ranking features and developed discretization model with the hybrid method (Dis-GRAFeSS) to improve classification accuracy. Experimental results showed that the methods improved the performance accuracy in identifying the authorship of features based ranking invariant discretization by substantially reducing redundant features

Integrating Consumer Feedback Into Business Marketing Strategies

Consumer feedback and reviews are critical to the success of businesses because 49% of consumers trust online reviews more than other sources. The purpose of this multicase study was to explore marketing managers\u27 strategies for using consumer reviews to improve marketing success, brand awareness, and their clients\u27 profitability. The conceptual framework for this study was built upon organizational theory and disruptive innovation theory. The participants were recruited through local events, social media, and e-mail. Data were collected from public online records and semistructured telephone interviews using 1 marketing manager from each of 5 marketing agencies in North Texas. Thematic analysis and methodological triangulation of the data revealed themes of marketing objective, response, and reputation management. Based on the findings, the successful businesses focused on building relationships with consumers, and the business leaders prepared responses to reviews to ensure appropriateness. Other marketing managers can review the findings\u27 relevance to create or enhance successful marketing strategies, which could help to increase profitability. The implication for social change is that jobs would be created for enhancing the local economy and residents\u27 standards of living