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PARKER, C.J., 2010. An Exploration of Volunteered Geographic Information Stakeholders, M. HAKLAY, J. MORLEY and H. RAHEMTULLA, eds. In: Proceedings of the GIS Research UK 18th Annual Conference, 14-16 April 2010 2010, UCL pp137-142.
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Social networks have become one of the primary sources of big data, where a variety of posts related to brands are liked, shared, and commented, which are collectively called as brand metadata. Due to the increased boom in E/Mcommerce, buyers often refer the brand metadata as a valuable source of information to make their purchasing decision. From the literature study, we found that there are not many works on predicting the popularity of the brand based on the combination of brand metadata and comment’s thoughtfulness analysis. This paper proposes a novel framework to classify the comment’s as thoughtful favored or disfavored comment’s, and later combines them with the brand metadata to forecast the popularity of the brand in near future. The performance of the proposed framework is compared with some of the recent works w.r.t. thoughtful comment’s identification accuracy, execution time, prediction accuracy and prediction time, the results obtained are found to be very encouraging.
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PARKER, C.J., 2010. An Exploration of Volunteered Geographic Information Stakeholders, M. HAKLAY, J. MORLEY and H. RAHEMTULLA, eds. In: Proceedings of the GIS Research UK 18th Annual Conference, 14-16 April 2010 2010, UCL pp137-142.
This slideshare highlights 40 mini case studies of businesses in Singapore that have stood out by implementing creative social media marketing campaigns.
Framework to Analyze Customer’s Feedback in Smartphone Industry Using Opinion...IJECEIAES
In the present age, cellular phones are the largest selling products in the world. Big Data Analytics is a method used for examining large and varied data, which we know as big data. Big data analytics is very useful for understanding the world of cellphone business. It is important to understand the requirements, demands, and opinions of the customer. Opinion Mining is getting more important than ever before, for performing analysis and forecasting customer behavior and preferences. This study proposes a framework about the key features of cellphones based on which, customers buy them and rate them accordingly. This research work also provides balanced and well researched reasons as to why few companies enjoy dominance in the market, while others do not make as much of an impact.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
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The goal of this research project is to analyze the dynamics of social networks using machine learning techniques to locate maximal cliques and to find clusters for the purpose of identifying a target demographic. Unsupervised machine learning techniques are designed and implemented in this project to analyze a dataset from YouTube to discover communities in the social network and find central nodes. Different clustering algorithms are implemented and applied to the YouTube dataset. The well-known Bron-Kerbosch algorithm is used effectively in this research to find maximal cliques. The results obtained from this research could be used for advertising purposes and for building smart recommendation systems. All algorithms were implemented using Python programming language. The experimental results show that we were able to successfully find central nodes through clique-centrality and degree centrality. By utilizing clique detection algorithms, the research shown how machine learning algorithms can detect close knit groups within a larger network.
Novel Machine Learning Algorithms for Centrality and Cliques Detection in You...gerogepatton
The goal of this research project is to analyze the dynamics of social networks using machine learning techniques to locate maximal cliques and to find clusters for the purpose of dentifying a target
demographic. Unsupervised machine learning techniques are designed and implemented in this project to analyze a dataset from YouTube to discover communities in the social network and find central nodes. Different clustering algorithms are implemented and applied to the YouTube dataset. The well-known Bron-Kerbosch algorithm is used effectively in this research to find maximal cliques. The results obtained
from this research could be used for advertising purposes and for building smart recommendation systems.
All algorithms were implemented using Python programming language. The experimental results show that
we were able to successfully find central nodes through clique-centrality and degree centrality. By utilizing
clique detection algorithms, the research shown how machine learning algorithms can detect close knit
groups within a larger network.
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Amazon Product Review Sentiment Analysis with Machine Learningijtsrd
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IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Online review mining for forecasting saleseSAT Journals
Abstract The growing popularity of online product review forums invites people to express opinions and sentiments toward the products .It gives the knowledge about the product as well as sentiment of people towards the product. These online reviews are very important for forecasting the sales performance of product. In this paper, we discuss the online review mining techniques in movie domain. Sentiment PLSA which is responsible for finding hidden sentiment factors in the reviews and ARSA model used to predict sales performance. An Autoregressive Sentiment and Quality Aware model (ARSQA) also in consideration for to build the quality for predicting sales performance. We propose clustering and classification based algorithm for sentiment analysis. Index Terms: Online Review mining, Text mining,reviews, S-PLSA, ARSA, Clustering, Classification.
Analyzing target user group¡¦s preferences and product form design specificat...Waqas Tariq
In the modern market where consumerism is running higher and the product life span is getting shorter, it is one of the challenges for the marketing and design departments in enterprises to know how to get a thorough grasp of the consumer¡¦s preference and potential target user group. With the wide spread and growth of the internet, a web-based survey is not influenced by time and space factors, making it easier for designers to have an in-depth understanding of the consumer¡¦s preferences towards products. Based upon the 2-dimensional image scale, 120 college students from Taiwan and Japan were invited to evaluate 27 pencil sharpener samples in terms of their preferences and intention of purchase. From the survey, competitive portable pencil sharpeners were identified for the references of new product design and development. The results indicated that such a web-based 2-dimensional image survey system could offer real time help in product segmentation and the selection of competition products as well as the target user group with the output systematic diagrams and tables. Furthermore, morphological analysis for product form elements and quantification type I analysis could help designers and marketing managers set up proper policies for product form design for the target user groups in the design and marketing of new product development.
NOVEL MACHINE LEARNING ALGORITHMS FOR CENTRALITY AND CLIQUES DETECTION IN YOU...ijaia
The goal of this research project is to analyze the dynamics of social networks using machine learning techniques to locate maximal cliques and to find clusters for the purpose of identifying a target demographic. Unsupervised machine learning techniques are designed and implemented in this project to analyze a dataset from YouTube to discover communities in the social network and find central nodes. Different clustering algorithms are implemented and applied to the YouTube dataset. The well-known Bron-Kerbosch algorithm is used effectively in this research to find maximal cliques. The results obtained from this research could be used for advertising purposes and for building smart recommendation systems. All algorithms were implemented using Python programming language. The experimental results show that we were able to successfully find central nodes through clique-centrality and degree centrality. By utilizing clique detection algorithms, the research shown how machine learning algorithms can detect close knit groups within a larger network.
NOVEL MACHINE LEARNING ALGORITHMS FOR CENTRALITY AND CLIQUES DETECTION IN YOU...gerogepatton
The goal of this research project is to analyze the dynamics of social networks using machine learning techniques to locate maximal cliques and to find clusters for the purpose of identifying a target demographic. Unsupervised machine learning techniques are designed and implemented in this project to analyze a dataset from YouTube to discover communities in the social network and find central nodes. Different clustering algorithms are implemented and applied to the YouTube dataset. The well-known Bron-Kerbosch algorithm is used effectively in this research to find maximal cliques. The results obtained from this research could be used for advertising purposes and for building smart recommendation systems. All algorithms were implemented using Python programming language. The experimental results show that we were able to successfully find central nodes through clique-centrality and degree centrality. By utilizing clique detection algorithms, the research shown how machine learning algorithms can detect close knit groups within a larger network.
Novel Machine Learning Algorithms for Centrality and Cliques Detection in You...gerogepatton
The goal of this research project is to analyze the dynamics of social networks using machine learning techniques to locate maximal cliques and to find clusters for the purpose of dentifying a target
demographic. Unsupervised machine learning techniques are designed and implemented in this project to analyze a dataset from YouTube to discover communities in the social network and find central nodes. Different clustering algorithms are implemented and applied to the YouTube dataset. The well-known Bron-Kerbosch algorithm is used effectively in this research to find maximal cliques. The results obtained
from this research could be used for advertising purposes and for building smart recommendation systems.
All algorithms were implemented using Python programming language. The experimental results show that
we were able to successfully find central nodes through clique-centrality and degree centrality. By utilizing
clique detection algorithms, the research shown how machine learning algorithms can detect close knit
groups within a larger network.
User experience can be drastically elevated by combining data science insights with user-based insights from research. Data analytics on its own can make themes and correlations difficult to explain and to provide accurate recommendations. For example, themes identified via large global surveys and usage data can be better understood with UX insights from focused user research, such as user interviews and/or cognitive walkthroughs. This presentation will highlight the complimentary nature of data science and UX and will focus on the benefits of bringing the two disciplines together. This will be buttressed with practical examples of enterprise projects and applications that combined data and skills from the two disciplines, guidance on how the two disciplines can better work together, and the skills needed to improve as a UX professional when working with data science teams.
FLUX·3D - Forward Looking User eXperienceMario Guillo
FLUX·3D is a tool for making possible to the people participating actively -and in a sustainable way- in the design of products, services and processes. This tool makes possible to approach a complex problem (such as the evaluation -in absolute and relative terms- of an innovation/prototype) in a simple and systematic way, which can be very helpful for making decisions (and defining future strategies) and/or improving the development of an innovation. FLUX·3D has been designed and developed by FUTURLAB - University of Alicante (Spain) together with Aalto University (Finland).
Extensive research from TNS proves that social media and search data can accurately predict the results of brand tracker surveys months in advance. The implications for market research are enormous.
Master thesis exploring the emerging field of Mobile App Analytics. We explore the potentials of the mobile app as a data source and the current stage within mobile app analytics
Amazon Product Review Sentiment Analysis with Machine Learningijtsrd
Users of Amazons online shopping service are allowed to leave feedback for the items they buy. Amazon makes no effort to monitor or limit the scope of these reviews. Although the amount of reviews for various items varies, the reviews provide easily accessible and abundant data for a variety of applications. This paper aims to apply and expand existing natural language processing and sentiment analysis research to data obtained from Amazon. The number of stars given to a product by a user is used as training data for supervised machine learning. Since more people are dependent on online products these days, the value of a review is increasing. Before making a purchase, a buyer must read thousands of reviews to fully comprehend a product. In this day and age of machine learning, however, sorting through thousands of comments and learning from them would be much easier if a model was used to polarize and learn from them. We used supervised learning to polarize a massive Amazon dataset and achieve satisfactory accuracy. Ravi Kumar Singh | Dr. Kamalraj Ramalingam "Amazon Product Review Sentiment Analysis with Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42372.pdf Paper URL: https://www.ijtsrd.comcomputer-science/data-processing/42372/amazon-product-review-sentiment-analysis-with-machine-learning/ravi-kumar-singh
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involves recognizing relationships between elements of the marketing mix (e.g.,
price and product quality), as well as assessing competitive and market conditions
(i.e., industry structure in the language of economics).
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2. Company: The Social Studies Group
Headquarters: Washington, DC
Audience: Consumer Brands
Industry: Social Media Research
Website: www.socialstudiesgroup.com/
Services: Social Media Research,
Marketing & Branding Strategy
Brandwatch Services: Social Media Monitoring and
Analytics
The Goal/
Gain Social Intelligence to Drive
New Product Designs
Referring to themselves as “Nerds with Panache,” The
Social Studies Group (SSG) is a social media research firm
with a seasoned team that brings over five decades of
marketing and branding strategy to each engagement. This
enables the firm to gather the right intelligence and deliver
meaningful results for companies seeking more knowledge
in the area of consumer research.
Among SGG’s clients is a global personal technology device
company that wanted to use social media to delve into
consumer opinions regarding the user experience (UX) features
for several of its new models. Prior to engaging SSG for this
project, the company had investigated consumer insights via
focus groups and normal customer feedback loops. However,
customer feedback around some features of newer models
was minimal, and focus groups yielded inconclusive data.
The company looked to SSG to perform a netnography – a
study of individuals, communities, and cultures online – focused
on UX preferences of its consumer base and target market.
Through the research, the manufacturer hoped to gain a deeper
understanding of consumers’ opinions in two critical areas:
1. Acceptance levels of the features found in each new
model’s UX
2. Specific likes and dislikes of the different new models’ UXs
The company hoped that SSG would obtain enough social
data to inform feature designs for the next product
development cycle of these newer models.
The Challenge/
Yielding Significance from
a Sea of Data
As SSG began to scope its netnography research for the
consumer electronics company, project leaders knew they
had to overcome several significant data challenges. First,
a staggering number of related consumer electronic
devices are sold online, which generates a massive volume
of social discussion.
This would make isolating consumer reviews and discussions
about the new models, and their UXs in particular, very difficult.
“It would be harder than finding a needle in a haystack,” said
Megan Evans of SSG. “It was more like finding several needles,
of several specific types, within a stack of needles.”
A second challenge facing SSG was the inescapable fact that
the social media search queries would need to be both flexible
and complex. Only the use of complex search queries, with
varying “includes” and “excludes,” could ensure that the
research surfaced the most relevant consumer discussions and
then isolated them around specific models and UX features.
A third challenge involved the need to accurately capture
consumer sentiment about the newer models and their UXs.
Online conversations would reveal those UX features being
discussed the most, but SSG would need to understand how
consumers felt about models and features at a statistically
significant certainty.
The Social Studies Group Gets Results
with New ‘Netnography’ Research
Key Results/
Case Study/ The Social Studies Group
About/
The Social Studies Group
• Obtained online consumer views of
products when offline data was
unavailable
• Informed product design by capturing
likes and dislikes of specific product
features
• Isolated results around individual
product models and features
‘netnography’
an online study of
individuals, cultures
and communities
3. Leveraging the detailed data break-downs provided by
Brandwatch search queries, SSG created a dashboard that
isolated each model, visualized its attributes, and revealed
the ways in which consumers viewed them. To develop
chart descriptions for each model, SSG had to ensure that
the dashboard could specifically reference an individual
model only.
To achieve this, they had the Brandwatch tool perform a “check”
of the mentions discussing other models. SSG then created
model-specific charts based on filtered dates and features.
Figure 2 shows a visualization of consumer views about the
ease of use for one product model.
SSG also delivered numerous model comparisons to their
client. These included comparisons of purchase drivers,
qualities, emotions, and opinions across all models. They
further broke down results and performed model-to-model
comparisons, as demonstrated in Figure 3.
The charts presented a quick read about the qualities
most discussed by consumers. Since users can actually
drill into the data directly from the charts, they became a
great discovery tool for diving deeper into the individual
qualities and lessons.
Case Study/ The Social Studies Group
SSG had leveraged Brandwatch social media analytics in
the past for some of the firm’s most challenging research
projects, and project leaders felt the netnography research
required the depth, breadth, and flexibility of Brandwatch’s
search capabilities.
Setting the Scope
To create the data universe it would analyze, SSG defined
broad searches in Brandwatch for the different device
models. They set the time frame to begin at product launch,
limiting it to three months. This would reduce the universe
size and ensure all models were analyzed over the same time
period. It was a critical step, because the consumer
electronics company had launched different models at
different times during the 12-month period prior to the study.
With so much online discussion about devices, SSG included in
the Brandwatch search queries an extensive series of excludes.
These would isolate reviews and social content specifically
pertaining to the UX features. For example, they captured online
comments related to the category of “User Friendly.” These would
include such terms as ”easy use”~3, “simple use”~3, etc. “It was
easy to create tags for the UX features in Brandwatch,” said
Evans. “We even created tags to ensure that misspellings and
abbreviated terms were included in the rules.” SSG employed
tags across the multiple devices for consistency which enabled
them to see what people were comparing across devices.
The Fun Part – Flexible Search
Pinpoints Critical Consumer Views
With the broad search completed, SSG moved on to a
further refined set of search queries to uncover statistically
significant consumer sentiment within the data set.
With the broad search completed, SSG moved on to a further
refined set of search queries to uncover statistically significant
consumer sentiment within the data set. “That’s the fun part and
what gets people excited,” said Wendy Scherer, Social Studies
Group Managing Partner. “It’s amazing all the ways you can
slice and dice data in Brandwatch versus other systems.” SSG
created multiple complex searches in Brandwatch to understand
consumer sentiment from a wide range of angles. Not only could
they analyze consumer sentiment as a whole for the different
models, but Brandwatch enabled them to isolate consumers by
detailed groupings, such as the following:
1. Purchase Location/ Consumer opinions were categorized by
the location where they purchased a model, such as online, at
a specific retailer, etc.
2. Stage of Buying Cycle/ Considering a model, weighing options,
thinking of buying, etc.
3. Focus of UX Qualities/ User friendly, intuitive, etc.
4. Emotions and Opinions/ Love it, hate it, fantastic, etc., opinion,
and emotion terms. It was key to create these categories so that
the results would show a positive (or negative) point of view and
not both.
Brandwatch can provide researchers with an endless set of
options for gaining in-depth views into consumer views. “We have
a best-practice tip for all Brandwatch users,” Scherer said with a
smile. “Anticipate terms that will likely be significant, then leverage
Brandwatch’s flexibility to scan for alternative words, spellings,
and colloquial terms that are related. That paid huge dividends on
our netnography.” Figure 1 demonstrates an example of this.
Big Data with
Detailed Refinement.
The Results/ Surfacing the “So What”The Solution/
Fig. 3
Purchase Drivers: Model X vs. Model Y, 3-Month Period
0
5K
10K
15K
20K
features
battery
life
ads
graphic
interface
m
anual
reliability
sound
style
Fig. 2
Consumer Opinion: Ease-of-Use for Model Z, 3-Month Period
Intuitive Difficult Unintuitive Easy
60%
18%
12%
10%
Model X Model Y
((device name) NEAR/7 (awkward)) NOT
("Not awkward" OR “find awkward”~5)
Fig. 1
Actual search query used by SSG in their product research
4. About/
About The Social
Studies Group
The Social Studies Group
(www.socialstudiesgroup.com) is a
market research firm that
specializes in using social media
conversations and visual content to
help companies better understand
their customers, competitors,
markets and industries.
Custom service offerings include
netnography (virtual ethnography),
identifying and analyzing niche
communities and influencers;
comparative linguistic analyses of
social media used for organizational
and brand messaging; creating
“universes” that can be monitored and
analyzed over time; and in-depth social
media monitoring for knowledge
accumulation and analyses.
For more information, please visit
www.socialstudiesgroup.com/
About Brandwatch
Brandwatch is a leading provider of
social media monitoring and
analytics solutions. More than 700
global brands and agencies use
Brandwatch, relying on a broad
range of social coverage and highly
reliable, spam-free data to monitor
online conversations.
As a result, organizations can glean
insights around their brand interests,
conduct market research, predict
market trends, and more actively
engage influencers, customers and
prospects.
A global company, Brandwatch is
headquartered in Brighton, UK and
has offices in the United States and
Germany.
For more information, please visit
www.brandwatch.com
@Brandwatch | Brandwatch Blog | Brandwatch Fan Page
“That's the fun part and
what gets people excited.
It's amazing all the ways
you can slice and dice
data in Brandwatch
versus other systems.”
Wendy Scherer,
Managing Partner,
The Social Studies Group