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Sentimental Analysis to Rank Web Products
"... Abstract — Nowadays, there is a trend of online shopping as more and more products are being sold by the manufacturers on the internet and customers are expressing their views and reviews about a product on the internet. Traditionally, individuals collect feedback from their friends or relatives bef ..."
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Abstract — Nowadays, there is a trend of online shopping as more and more products are being sold by the manufacturers on the internet and customers are expressing their views and reviews about a product on the internet. Traditionally, individuals collect feedback from their friends or relatives before purchasing an item, but nowadays user generated reviews give the information which helps customers in buying their favorable products. So reviews of huge number of individuals around the globe can be analyzed. For that, various review mining techniques and sentiment analysis techniques are currently being used and products are being ranked using various ranking algorithms. We present a product ranking system using review mining techniques. Users can specify product information and get the ranking results of all matched products in return. We consider three issues while calculating product scores: Product reviews, Product popularity and Product release month. We are going to rank the products specified by user not all the products.
On Deriving Indicators from Texts
"... Abstract. This paper presents and explores the idea of deriving numerical indicators from texts, that is, converting text data to numerical data that has predictive or diagnostic value. One application of such a general capability is to the provisional identification of networks, or rather, of assoc ..."
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Abstract. This paper presents and explores the idea of deriving numerical indicators from texts, that is, converting text data to numerical data that has predictive or diagnostic value. One application of such a general capability is to the provisional identification of networks, or rather, of associations within networks. Conversely, given a network structure among entities that are associated with various texts, the network structure can itself contribute usefully to construction of indicators derived from texts. The focus of the paper is on basic concepts and methods for deriving indicators from texts. Much research remains to be done. Key words: text mining, text data mining, data mining, mashing, economic indicators, social indicators
__________________________________________________________________________ _ THE WHARTON RISK MANAGEMENT AND DECISION PROCESSES CENTER Established in 1984, the Wharton Risk Management and Decision Processes
, 2011
"... Center develops and promotes effective corporate and public policies for low‐probability events with potentially catastrophic consequences through the integration of risk assessment, and risk perception with risk management strategies. Natural disasters, technological hazards, and national and inter ..."
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Center develops and promotes effective corporate and public policies for low‐probability events with potentially catastrophic consequences through the integration of risk assessment, and risk perception with risk management strategies. Natural disasters, technological hazards, and national and international security issues (e.g., terrorism risk insurance markets, protection of critical infrastructure, global security) are among the extreme events that are the focus of the Center’s research. The Risk Center’s neutrality allows it to undertake large‐scale projects in conjunction with other researchers and organizations in the public and private sectors. Building on the disciplines of economics, decision sciences, finance, insurance, marketing and psychology, the Center supports and undertakes field and experimental studies of risk and uncertainty to better understand how individuals and organizations make choices under conditions of risk and uncertainty. Risk Center research also investigates the effectiveness of strategies such as risk communication, information sharing, incentive systems, insurance, regulation and public‐private collaborations at a national and international scale. From these findings, the Wharton Risk Center’s research team – over