Галерея 3010807

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Sloan 3010807 N/A Royal Exposed, Battery Powered, Sensor Activated, Royal Optima SMOOTH- Water Closet Flushometer… Check Ability Consumption of Your Water Heater to Retain Performance at Superior Levels All those who have made use of aged h2o heaters often presume that they will have to invest a good deal on power intake. Even so, folks are unsuccessful to comprehend that the most up-to-date developments in technological know-how have produced the extent of use of electricity. This suggests that the energy intake of the heater can be made use of as a sensible and rational foundation to ascertain its performance. Sloan 3010807 N/A Royal Exposed, Battery Powered, Sensor Activated, Royal Optima SMOOTH- Water Closet Flushometer… An Easy Alternative to Standing Water With the massive snowstorms that are transpiring throughout the place, coupled with the usual Spring rains that will be coming in the subsequent few months, probabilities are the floor all around your property is heading to be overly saturated with drinking water. This over saturation could trigger you equally drainage and standing drinking water issues. Which, in turn, could induce destruction to your household and yard. The Advantage Of Applying Forged Iron Guttering To Acquire Rain Water Report about forged iron gutters and how vital it is to use the gutters about your residence if you have a yard that needs to be watered in summer months when there may perhaps be a absence of tap water. This also asks the problem about regardless of whether it is possible to consume rainwater especially in polluted metropolitan areas. Sloan 3010807 N/A Royal Exposed, Battery Powered, Sensor Activated, Royal Optima SMOOTH- Water Closet Flushometer…
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Abstract: This work studies how we can obtain feature-level ratings of the mobile products from the customer reviews and review votes to influence decision-making, both for new cus... View more
This work studies how we can obtain feature-level ratings of the mobile products from the customer reviews and review votes to influence decision-making, both for new customers and manufacturers. Such a rating system gives a more comprehensive picture of the product than what a product-level rating system offers. While product-level ratings are too generic, feature-level ratings are particular; we exactly know what is good or bad about the product. There has always been a need to know which features fall short or are doing well according to the customer’s perception. It keeps both the manufacturer and the customer well-informed in the decisions to make in improving the product and buying, respectively. Different customers are interested in different features. Thus, feature-level ratings can make buying decisions personalized. We analyze the customer reviews collected on an online shopping site (Amazon) about various mobile products and the review votes. Explicitly, we carry out a feature-focused sentiment analysis for this purpose. Eventually, our analysis yields ratings to 108 features for 4000+ mobiles sold online. It helps in decision-making on how to improve the product (from the manufacturer’s perspective) and in making the personalized buying decisions (from the buyer’s perspective) a possibility. Our analysis has applications in recommender systems, consumer research, and so on.
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With the rise of the Internet and the kind of busy lifestyles people have today, online shopping has become a norm. Customers often rely on the online ratings of the previous customers to make their decisions. However, most of these ratings on the online websites are product-level ratings and lack specificity. Although products can be compared based on the product-level ratings available, there is always a class of people who prefer buying the items based on particular features. Such people have to generally go through the entire comments section to know previous customers’ perceptions [1], [2] of the product’s features in which they are interested. Considering the number of products present for an item (such as mobile), it becomes a tedious job for a customer to arrive at the best product for himself. Moreover, from a manufacturer’s perspective, such product-level ratings hardly specify what is good or bad about the product [3]. Thus, if feature-level ratings are available, it gives more clarity to the manufacturer on how to improve the product. Given all these benefits, our goal is to develop a feature-level rating system.
2012 IEEE Conference on Technology and Society in Asia (T&SA)
IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans
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This work studies how we can obtain feature-level ratings of the mobile products from the customer reviews and review votes to influence decision-making, both for new customers and manufacturers. Such a rating system gives a more comprehensive picture of the product than what a product-level rating system offers. While product-level ratings are too generic, feature-level ratings are particular; we exactly know what is good or bad about the product. There has always been a need to know which features fall short or are doing well according to the
customer’s perception. It keeps both the manufacturer and the customer well-informed in the decisions to make in improving the product and buying, respectively. Different customers are interested in different features. Thus, feature-level ratings can make buying decisions personalized. We analyze the customer reviews collected on an online shopping site (Amazon) about various mobile products and the review votes. Explicitly, we carry out a feature-focused sentiment analysis for this purpose. Eventually, our analysis yields ratings to 108 features for 4000+ mobiles sold online. It helps in decision-making on how to improve the product (from the manufacturer’s perspective) and in making the personalized buying decisions (from the buyer’s perspective) a possibility. Our analysis has applications in recommender systems, consumer research, and so on.
Word cloud for our features (except "phone" feature).
All figure content in this area was uploaded by Koteswar Rao Jerripothula
Content may be subject to copyright.
Content may be subject to copyright.
This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.
IEEE TRANSACTIONS ON COMPUTA TIONAL SOCIAL SYSTEMS 1
Feature-Lev el Rating System Using Customer
Koteswar Rao Jerripot hula , Member , IEEE , Ankit Rai, Kanu Garg, and Y ashvardhan Singh Rautela
Abstract — This work studies ho w we can obtain featur e-level
ratings of the mobile products from the custo mer reviews
and review vo tes to inﬂuence decision-making, both for new
customers and manufacturers. Such a rating system gives a more
comprehensiv e picture of the product than what a product-leve l
rating system offers. Whi le pro duct-lev el ratings are too generic,
feature-level ratings are particular; we exactly k no w what is good
or bad about the product. The re has always been a need to
know which features fall short or are doing well according to the
customer’s perception. It keeps both the manufacturer and the
customer well-informed in the decisions to make in improving
the product and buyi ng, respectiv ely. Different customers are
interested in different features. Thus, featur e-level ratings can
make buying decisions personalized. We analyze the customer
reviews collected on an onl ine shopping site (Amazon) about
various mobile pro ducts and the review votes. Explicitly , we carry
out a feature-focuse d sentiment analysis for this purpose. Ev entu-
ally, our analysis yields ratings to 108 f eatures f or 4000 + mobiles
sold online. It helps in decision-making on how to impr ove the
product (from the manufacturer’s perspective) and in making
the personalized buying decisions (from the buyer’s perspective)
a possibility. Our a nalysis has applications in recommender
systems, consumer research, and so on.
Index Terms —Cellular phones, decision-making, natural lan-
guage processing , recommender syste ms, reviews, sentiment
W ITH the rise of the Internet and the kind of busy
lifestyles people hav e today, onl ine shopping has
become a norm. Customers often rely on the online ratings of
the previous customers to mak e their decisions. Howev er, most
of these ratings on the online websites are product-leve l ratings
and lack speciﬁcity . Although products can be compared
based on the product-level ratings available, there is alw ays
a class of people who prefer buying the items based on
particular features. Such people ha ve to generally go through
the entire comments section to know previous customers’
perceptions [1], [2] of the product’s features in which they
are interested. Considering t he number of products present
for an item (such as mo bile), it becomes a tedious job for a
customer to arrive at the best product for himself. Moreover ,
Manuscript received September 11, 2019; revised February 29, 2020;
accepted July 9, 2020. This work was supported by the Initiation Research
Grant (IRG) of Indraprastha Institute of Information Technolog
Опытная проститутка сделала мексиканцу глубокий минет своим маленьким ртом
Занятие йогой переросло в хороший секс втроем
Дама в очках сняла синее платье

Галерея 3010807

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