Automated Discovery of Lead Users and Latent Product Features by Mining Large Scale Social Media Networks

2015 ◽  
Vol 137 (7) ◽  
Author(s):  
Suppawong Tuarob ◽  
Conrad S. Tucker
Keyword(s):  
Social Media ◽  
Large Scale ◽  
User Preferences ◽  
Social Media Data ◽  
Social Media Networks ◽  
Lead User ◽  
Product Features ◽  
Latent Features ◽  
Lead Users ◽  
Media Data

Lead users play a vital role in next generation product development, as they help designers discover relevant product feature preferences months or even years before they are desired by the general customer base. Existing design methodologies proposed to extract lead user preferences are typically constrained by temporal, geographic, size, and heterogeneity limitations. To mitigate these challenges, the authors of this work propose a set of mathematical models that mine social media networks for lead users and the product features that they express relating to specific products. The authors hypothesize that: (i) lead users are discoverable from large scale social media networks and (ii) product feature preferences, mined from lead user social media data, represent product features that do not currently exist in product offerings but will be desired in future product launches. An automated approach to lead user product feature identification is proposed to identify latent features (product features unknown to the public) from social media data. These latent features then serve as the key to discovering innovative users from the ever increasing pool of social media users. The authors collect 2.1 × 109 social media messages in the United States during a period of 31 months (from March 2011 to September 2013) in order to determine whether lead user preferences are discoverable and relevant to next generation cell phone designs.

Discovering Next Generation Product Innovations by Identifying Lead User Preferences Expressed Through Large Scale Social Media Data

2014 ◽  
Author(s):  
Suppawong Tuarob ◽  
Conrad S. Tucker
Keyword(s):  
Social Media ◽  
Large Scale ◽  
User Preferences ◽  
Next Generation ◽  
Social Media Data ◽  
The Public ◽  
Lead User ◽  
Product Features ◽  
Latent Features ◽  
Media Data

An innovative consumer (a.k.a. a lead user) is a consumer of a product that faces needs unknown to the public. Innovative consumers play important roles in the product development process as their ideas tend to be innovatively unique and can be potentially useful for development of next generation, innovative products that better satisfy the market needs. Oftentimes, consumers portray their usage experience and opinions about products and product features through social networks such as Twitter and Facebook, making social media a viable, rich in information, and large-scale source for mining product related information. The authors of this work propose a data mining methodology to automatically identify innovative consumers from a heterogeneous pool of social media users. Specifically, a mathematical model is proposed to identify latent features (product features unknown to the public) from social media data. These latent features then serve as the key to discover innovative users from the ever increasing pool of social media users. A real-world case study, which identifies smartphone lead users in the pool of Twitter users, illustrates promising success of the proposed models.

Automated Discovery of Product Feature Inferences Within Large-Scale Implicit Social Media Data

2018 ◽  
Vol 18 (2) ◽  
Author(s):  
Suppawong Tuarob ◽  
Sunghoon Lim ◽  
Conrad S. Tucker
Keyword(s):  
Social Media ◽  
Large Scale ◽  
Efficient Manner ◽  
Social Media Data ◽  
Product Features ◽  
Potential Applications ◽  
Cost Efficient ◽  
Automated Discovery ◽  
Social Messages ◽  
Media Data

Recently, social media has emerged as an alternative, viable source to extract large-scale, heterogeneous product features in a time and cost-efficient manner. One of the challenges of utilizing social media data to inform product design decisions is the existence of implicit data such as sarcasm, which accounts for 22.75% of social media data, and can potentially create bias in the predictive models that learn from such data sources. For example, if a customer says “I just love waiting all day while this song downloads,” an automated product feature extraction model may incorrectly associate a positive sentiment of “love” to the cell phone's ability to download. While traditional text mining techniques are designed to handle well-formed text where product features are explicitly inferred from the combination of words, these tools would fail to process these social messages that include implicit product feature information. In this paper, we propose a method that enables designers to utilize implicit social media data by translating each implicit message into its equivalent explicit form, using the word concurrence network. A case study of Twitter messages that discuss smartphone features is used to validate the proposed method. The results from the experiment not only show that the proposed method improves the interpretability of implicit messages, but also sheds light on potential applications in the design domains where this work could be extended.

Quantifying Product Favorability and Extracting Notable Product Features Using Large Scale Social Media Data

2015 ◽  
Vol 15 (3) ◽  
Author(s):  
Suppawong Tuarob ◽  
Conrad S. Tucker
Keyword(s):  
Social Media ◽  
Large Scale ◽  
Heterogeneous Data ◽  
Product Launch ◽  
Physical Interaction ◽  
Next Generation ◽  
Social Media Data ◽  
Product Features ◽  
Effective Manner ◽  
Media Data

Some of the challenges that designers face in getting broad external input from customers during and after product launch include geographic limitations and the need for physical interaction with the design artifact(s). Having to conduct such user-based studies would require huge amounts of time and financial resources. In the past decade, social media has emerged as an increasingly important medium of communication and information sharing. Being able to mine and harness product-relevant knowledge within such a massive, readily accessible collection of data would give designers an alternative way to learn customers' preferences in a timely and cost-effective manner. In this paper, we propose a data mining driven methodology that identifies product features and associated customer opinions favorably received in the market space which can then be integrated into the design of next generation products. Two unique product domains (smartphones and automobiles) are investigated to validate the proposed methodology and establish social media data as a viable source of large scale, heterogeneous data relevant to next generation product design and development. We demonstrate in our case studies that incorporating suggested features into next generation products can result in favorable sentiment from social media users.

scholarly journals Semantic-Aware Visual Abstraction of Large-Scale Social Media Data With Geo-Tags

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 114851-114861 ◽  
Author(s):  
Zhiguang Zhou ◽  
Xinlong Zhang ◽  
Xiaoyun Zhou ◽  
Yuhua Liu
Keyword(s):  
Social Media ◽  
Large Scale ◽  
Social Media Data ◽  
Media Data

scholarly journals Communication Sentiment Analyzer using Machine Learning with Naive Bayes Bernoullinb

2019 ◽  
Vol 9 (1) ◽  
pp. 5976-5979
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Major Part ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
User Preferences ◽  
Social Media Data ◽  
Machine Learning Model ◽  
The World ◽  
Media Data

In this never-ending social media era it is estimated that over 5 billion people use smartphones. Out of these, there are over 1.5 billion active users in the world. In which we all are a major part and before opening our messages we all are curious about what message we have received. No doubt, we all always hope for a good message to be received. So Sentiment analysis on social media data has been seen by many as an effective tool to monitor user preferences and inclination. Finally, we propose a scalable machine learning model to analyze the polarity of a communicative text using Naive Bayes’ Bernoulli classifier. This paper works on only two polarities that is whether the sentence is positive or negative. Bernoulli classifier is used in this paper because it is best suited for binary inputs which in turn enhances the accuracy of up to 97%.

Who’s Tweeting About the President? What Big Survey Data Can Tell Us About Digital Traces?

2019 ◽  
Vol 38 (5) ◽  
pp. 633-650 ◽  
Author(s):  
Josh Pasek ◽  
Colleen A. McClain ◽  
Frank Newport ◽  
Stephanie Marken
Keyword(s):  
Social Media ◽  
Survey Data ◽  
Large Scale ◽  
Presidential Approval ◽  
Social Phenomena ◽  
Social Media Data ◽  
Complex Picture ◽  
Large Scale Survey ◽  
Media Data

Researchers hoping to make inferences about social phenomena using social media data need to answer two critical questions: What is it that a given social media metric tells us? And who does it tell us about? Drawing from prior work on these questions, we examine whether Twitter sentiment about Barack Obama tells us about Americans’ attitudes toward the president, the attitudes of particular subsets of individuals, or something else entirely. Specifically, using large-scale survey data, this study assesses how patterns of approval among population subgroups compare to tweets about the president. The findings paint a complex picture of the utility of digital traces. Although attention to subgroups improves the extent to which survey and Twitter data can yield similar conclusions, the results also indicate that sentiment surrounding tweets about the president is no proxy for presidential approval. Instead, after adjusting for demographics, these two metrics tell similar macroscale, long-term stories about presidential approval but very different stories at a more granular level and over shorter time periods.

Fad or Here to Stay: Predicting Product Market Adoption and Longevity Using Large Scale, Social Media Data

2013 ◽  
Author(s):  
Suppawong Tuarob ◽  
Conrad S. Tucker
Keyword(s):  
Social Media ◽  
Knowledge Discovery ◽  
Large Scale ◽  
Product Information ◽  
Product Market ◽  
Knowledge Discovery In Databases ◽  
Product Reviews ◽  
Product Model ◽  
Social Media Data ◽  
Media Data

The authors of this work propose a Knowledge Discovery in Databases (KDD) model for predicting product market adoption and longevity using large scale, social media data. Social media data, available through sites such as Twitter® and Facebook®, have been shown to be leading indicators and predictors of events ranging from influenza spread, financial stock market prices, and movie revenues. Being ubiquitous and colloquial in nature allows users to honestly express their opinions in a unified, dynamic manner. This makes social media a relatively new data gathering source that can potentially appeal to designers and enterprise decision makers aiming to understand consumers response to their upcoming/newly launched products. Existing design methodologies for leveraging large scale data have traditionally relied on product reviews available on the internet to mine product information. However, such web reviews often come from disparate sources, making the aggregation and knowledge discovery process quite cumbersome, especially reviews for poorly received products. Furthermore, such web reviews have not been shown to be strong indicators of new product market adoption. In this paper, the authors demonstrate how social media can be used to predict and mine information relating to product features, product competition and market adoption. In particular, the authors analyze the sentiment in tweets and use the results to predict product sales. The authors present a mathematical model that can quantify the correlations between social media sentiment and product market adoption in an effort to compute the ability to stay in the market of individual products. The proposed technique involves computing the Subjectivity, Polarity, and Favorability of the product. Finally, the authors utilize Information Retrieval techniques to mine users’ opinions about strong, weak, and controversial features of a given product model. The authors evaluate their approaches using the real-world smartphone data, which are obtained from www.statista.com and www.gsmarena.com.

scholarly journals Reuters tracer: Toward automated news production using large scale social media data

2017 ◽  
Author(s):  
Xiaomo Liu ◽  
Armineh Nourbakhsh ◽  
Quanzhi Li ◽  
Sameena Shah ◽  
Robert Martin ◽  
...  
Keyword(s):  
Social Media ◽  
Large Scale ◽  
News Production ◽  
Social Media Data ◽  
Media Data

Visual abstraction and exploration of large-scale geographical social media data

2020 ◽  
Vol 376 ◽  
pp. 244-255 ◽  
Author(s):  
Zhiguang Zhou ◽  
Xinlong Zhang ◽  
Zhiyong Guo ◽  
Yuhua Liu
Keyword(s):  
Social Media ◽  
Large Scale ◽  
Social Media Data ◽  
Media Data
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