Understanding Consumer Preferences from Social Media Data

AbstractConsumers produce enormous amounts of textual data of product reviews online. Artificial intelligence (AI) can help analyze this data and generate insights about consumer preferences and decision-making. A GfK research project tested how we can use AI to learn consumer preferences and predict choices from publicly available social media and review data. The common AI tool “Word Embeddings” was used and has shown to be a powerful way to analyze the words people use. It helped reveal consumers’ preferred brands, favorite features and main benefits. Language biases uncovered by the analysis can indicate preferences. Compared to actual sales data from GfK panels, they fit reasonably within various categories. Especially when data volumes were large, the method produced very accurate results. By using free, widespread online data it is completely passive, without affecting respondents or leading them into ranking or answering questions they would otherwise not even have thought of. The analysis is fast to run and no fancy processing power is needed.

Download Full-text

Tackling social media data analysis

Qualitative Market Research An International Journal ◽

10.1108/qmr-01-2017-0021 ◽

2019 ◽

Vol 22 (2) ◽

pp. 94-113 ◽

Cited By ~ 10

Author(s):

Violetta Wilk ◽

Geoffrey N. Soutar ◽

Paul Harrigan

Keyword(s):

Social Media ◽

Data Analysis ◽

Exploratory Research ◽

Online Data ◽

Social Media Data ◽

Content Type ◽

Advantages And Disadvantages ◽

Brand Advocacy ◽

Qualitative Data Analysis Software ◽

Media Data

PurposeThis paper aims to offer insights into the ways two computer-aided qualitative data analysis software (CAQDAS) applications (QSR NVivo and Leximancer) can be used to analyze big, text-based, online data taken from consumer-to-consumer (C2C) social media communication.Design/methodology/approachThis study used QSR NVivo and Leximancer, to explore 200 discussion threads containing 1,796 posts from forums on an online open community and an online brand community that involved online brand advocacy (OBA). The functionality, in particular, the strengths and weaknesses of both programs are discussed. Examples of the types of analyses each program can undertake and the visual output available are also presented.FindingsThis research found that, while both programs had strengths and weaknesses when working with big, text-based, online data, they complemented each other. Each contributed a different visual and evidence-based perspective; providing a more comprehensive and insightful view of the characteristics unique to OBA.Research limitations/implicationsQualitative market researchers are offered insights into the advantages and disadvantages of using two different software packages for research projects involving big social media data. The “visual-first” analysis, obtained from both programs can help researchers make sense of such data, particularly in exploratory research.Practical implicationsThe paper provides practical recommendations for analysts considering which programs to use when exploring big, text-based, online data.Originality/valueThis paper answered a call to action for further research and demonstration of analytical programs of big, online data from social media C2C communication and makes strong suggestions about the need to examine such data in a number of ways.

Download Full-text

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

Volume 2B: 33rd Computers and Information in Engineering Conference ◽

10.1115/detc2013-12661 ◽

2013 ◽

Cited By ~ 20

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.

Download Full-text

Bi-Lingual (English, Punjabi) Sarcastic Sentiment Analysis by using Classification Methods

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.i8053.078919 ◽

2019 ◽

Vol 8 (9) ◽

pp. 1383-1388

Keyword(s):

Social Media ◽

Sentiment Analysis ◽

Main Step ◽

Online Data ◽

Social Media Data ◽

The Social ◽

Negative Comments ◽

Regional Languages ◽

Media Data ◽

Day By Day

Sentiment analysis is one of the heated topic in the field of text mining. As the social media data is increased day by day the main need of the data scientists is to classify the data so that it can be further used for decision making or knowledge discovery. Now –a-days everything and everyone available online so to check the latest trends in business or in daily life one must consider the online data. The main focus of sentiment analysis is to focus on positive or negative comments so that a well define picture is created that what is trending or not but the sarcasm manipulates the data as in sarcastic comment negative comment consider as positive because of the presence of positive words in the comment or data so it is necessary to detect the sarcasm in online data . The data on social media is available in various languages so sentiment analysis in regional languages is also a main step . In the proposed work we focus on two languages i.e Punjabi and English. Here we use deep learning based neural networks for the sarcasm detection in English as well as Punjabi language. In the proposed work we consider three datasets i.e. balanced English dataset, Balanced Punjabi Dataset and unbalanced Punjabi dataset. We used six different models to check the accuracy of the classified data the models we used are LSTM with word embedding layer, BiLSTM with , LSTM+LSTM, BiLSTM+BiLSTM, LSTM+BiLSTM, CNN respectively. LSTM provide better accuracy for balanced Punjabi and English dataset i.e. 95.63% and 94.17% respectively. The accuracy for unbalanced Punjabi dataset is provided by BiLSTM i.e.96.31%.

Download Full-text

Visual Sentiment Analysis on Social Media Data

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit2174101 ◽

2021 ◽

pp. 366-372

Author(s):

Harshala Bhoir ◽

K. Jayamalini

Keyword(s):

Social Media ◽

Sentiment Analysis ◽

State Of The Art ◽

Social Media Data ◽

Media Image ◽

The Social ◽

Textual Data ◽

Art Works ◽

Negative Sentiment ◽

Media Data

Visual sentiment analysis is the way to automatically recognize positive and negative emotions from images, videos, graphics, stickers etc. To estimate the polarity of the sentiment evoked by images in terms of positive or negative sentiment, most of the state-of-the-art works exploit the text associated to a social post provided by the user. However, such textual data is typically noisy due to the subjectivity of the user which usually includes text useful to maximize the diffusion of the social post. Proposed system will extract and employ an Objective Text description of images automatically extracted from the visual content rather than the classic Subjective Text provided by the user. The proposed System will extract three views visual view, subjective text view and objective text view of social media image and will give sentiment polarity positive, negative or neutral based on hypothesis table.

Download Full-text

Reports of the Workshops Held at the 2018 International AAAI Conference on Web and Social Media

AI Magazine ◽

10.1609/aimag.v39i4.2835 ◽

2018 ◽

Vol 39 (4) ◽

pp. 36-44

Author(s):

Managing Editor ◽

Jisun An ◽

Rumi Chunara ◽

David J. Crandall ◽

Darian Frajberg ◽

...

Keyword(s):

Social Media ◽

Well Being ◽

Ecological Monitoring ◽

Online Data ◽

Social Media Data ◽

Ethical Trade ◽

Making Sense ◽

Trade Offs ◽

Informal Setting ◽

Media Data

The Workshop Program of the Association for the Advancement of Artificial Intelligence’s 12th International Conference on Web and Social Media (AAAI-18) was held at Stanford University, Stanford, California USA, on Monday, June 25, 2018. There were fourteen workshops in the program: Algorithmic Personalization and News: Risks and Opportunities; Beyond Online Data: Tackling Challenging Social Science Questions; Bridging the Gaps: Social Media, Use and Well-Being; Chatbot; Data-Driven Personas and Human-Driven Analytics: Automating Customer Insights in the Era of Social Media; Designed Data for Bridging the Lab and the Field: Tools, Methods, and Challenges in Social Media Experiments; Emoji Understanding and Applications in Social Media; Event Analytics Using Social Media Data; Exploring Ethical Trade-Offs in Social Media Research; Making Sense of Online Data for Population Research; News and Public Opinion; Social Media and Health: A Focus on Methods for Linking Online and Offline Data; Social Web for Environmental and Ecological Monitoring and The ICWSM Science Slam. Workshops were held on the first day of the conference. Workshop participants met and discussed issues with a selected focus — providing an informal setting for active exchange among researchers, developers, and users on topics of current interest. Organizers from nine of the workshops submitted reports, which are reproduced in this report. Brief summaries of the other five workshops have been reproduced from their website descriptions.

Download Full-text

Analysis of Mining Social Media – A Learners Perspective

International Journal for Research in Engineering Application & Management ◽

10.35291/2454-9150.2020.0017 ◽

2020 ◽

pp. 91-96

Keyword(s):

Data Mining ◽

Social Media ◽

Performance Metrics ◽

Research Focus ◽

Social Media Data ◽

Data Mining Techniques ◽

Huge Data ◽

The Social ◽

The Common ◽

Media Data

In the world of digitalization the data play a key role. The data may be in structured or unstructured. The structured data uses data mining techniques to find the unknown pattern from the known data. But, the social media has huge data due to its rapid growth, the data were dynamic and unstructured. Due to this traditional data mining techniques will not be appropriate. The combinational approach of data mining and social media will provide the user to gain an insight and prominent idea how can be mined. Social media provides each individual to connect with the others depending on their interest. Every individual are accessing Face book, Twitter, LinkedIn, cademicia.edu, Google+ for sharing their views and thoughts, day-to-day happenings with any one or more of the above sites. This paper give an idea of the how those sites are classified based on their size, data, research focus, design issues and the types of the sites, types of users and the common approaches on social networks which will help the researchers how the social media, social networking websites structurally classified, studies the existing data mining techniques along with the performance metrics used in past researches and tools for retrieving social media data.

Download Full-text