Generating Business Intelligence Through Social Media Analytics: Measuring Brand Personality with Consumer-, Employee-, and Firm-Generated Content

Hu, Yuheng; Xu, Anbang; Hong, Yili; Gal, David; Sinha, Vibha Singhal; Akkiraju, Rama

doi:10.1080/07421222.2019.1628908

Cited by 81 publications

(37 citation statements)

References 73 publications

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“…Social media analytics are classified as Business Intelligence & Analytics 2.0, which includes information retrieval and extraction as well as opinion mining, used in this paper to extract valuable information from Twitter (Chen et al, 2012). Typically, social media analytics can be used in business to measure brand personality, e.g., if the brand is young or old (Hu et al, 2019), to deepen the understanding of technological discontinuities and changes (Bullini Orlandi et al, 2020), to facilitate business to business sustainability (Sivarajah et al, 2020) or even the relationship between investor sentiment and stock returns (McGurk et al, 2020). An overview of Big Data analytics in social media is presented in Figure 1.…”

Section: Research Model and Methodsmentioning

confidence: 99%

Social Media Users’ Opinions on Remote Work during the COVID-19 Pandemic. Thematic and Sentiment Analysis

Wrycza

Maślankowski

2020

Information Systems Management

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COVID-19 raised the interest in remote work tremendously. In this article, the phenomenon of this increase was assessed, by analyzing tweets on Twitter. It turned out, that the topic of remote work at epidemic peak in March 2020 increased almost 15 times during a year. The sentiment analysis confirmed the approval of remote work by over 60% of its users. The study proved the opinion that it will permanently stay in the post-COVID time.

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Section: Research Model and Methodsmentioning

confidence: 99%

Social Media Users’ Opinions on Remote Work during the COVID-19 Pandemic. Thematic and Sentiment Analysis

Wrycza

Maślankowski

2020

Information Systems Management

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“…We have computed count, means, standard deviation, min, for all platforms which are given in Table 7. The researcher's work [26], they have shown that organizations can mine business intelligence from social media data behavior on significant business application, assessing brand behavior. Specifically, they developed a text analytics framework that assimilates different separate social media data sources that consumers, employees, and organizations generated to measure brand behavior.…”

Section: Resultsmentioning

confidence: 99%

Machine learning-based mathematical modelling for prediction of social media consumer behavior using big data analytics

et al. 2021

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Social media is popular in our society right now. People are using social media platforms to purchase various products. We collected the data from various social media platforms. We analyzed the data for prediction of the consumer behavior on the social media platform. We considered the consumer data from Facebook, Twitter, Linked In and YouTube, Instagram, and Pinterest, etc. There are diverse and high-speed, high volume data which are coming from social media platform, so we used predictive big data analytics. In this paper, we have used the concept of big data technology to process data and analyze it to predict consumer behavior on social media. We have analyzed consumer behavior on social media platforms based on some parameters and criteria. We analyzed the consumer perception, attitude towards the social media platform. To get good quality of result, we pre-process data using various data pre-processing to detect outlier, noises, error, and duplicate record. We developed mathematical modeling using machine learning to predict consumer behavior on the social media platform. This model is a predictive model for predicting consumer behavior on the social media platform. 80% of data are used for training purposes and 20% for testing.

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“…In sum, as illustrated by the case of Cambridge Analytica, the idea that marketers are able to use people's digital traces to gain deep insights into their psychology and thereby to accurately predict and manipulate their behavior as if with a digital “voodoo doll” (Johnson, 2019) is fanciful. To be sure, marketers are able to infer some information about consumers’ psychological traits and preferences from online behavior, such as from the text they write (Berger et al., 2020), the images they post (Hartmann et al., 2020; Liu et al., 2020), and the brands they follow or like (Culotta & Cutler, 2016; Hu, Xu, et al., 2019; Kosinski et al., 2013; Schoenmueller et al., 2020), but these are very crude measures. Moreover, for the most part—as in the case of attempting to use personality measures to predict political preferences—these measures are not very relevant for predicting consumer preferences, and therefore don't much increase the absolute accuracy of prediction.…”

Section: Predicting Future Choices From Past Choices (And Other Behavmentioning

confidence: 99%

Predicting consumers’ choices in the age of the internet, AI, and almost perfect tracking: Some things change, the key challenges do not

Gal

Simonson

2020

Consumer Psychology Review

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Recent technology advances (e.g., tracking and “AI”) have led to claims and concerns regarding the ability of marketers to anticipate and predict consumer preferences with great accuracy. Here, we consider the capabilities of both traditional techniques (e.g., conjoint analysis) and more recent tools (e.g., advanced machine learning methods) for predicting consumer choices. Our main conclusion is that for most of the more interesting consumer decisions, those that are “new” and non‐habitual, prediction remains hard. In fact, in many cases, prediction has become harder due to the increasing influence of just‐in‐time information (user reviews, online recommendations, new options, etc.) at the point of decision that can neither be measured nor anticipated ex ante. Sophisticated methods and “big data” can in certain contexts improve predictions, but usually only slightly, and prediction remains very imprecise—so much so that it is often a waste of effort. We suggest marketers focus less on trying to predict consumer choices with great accuracy and more on how the information environment affects the choice of their products. We also discuss implications for consumers and policymakers.

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Generating Business Intelligence Through Social Media Analytics: Measuring Brand Personality with Consumer-, Employee-, and Firm-Generated Content

Cited by 81 publications

References 73 publications

Social Media Users’ Opinions on Remote Work during the COVID-19 Pandemic. Thematic and Sentiment Analysis

Social Media Users’ Opinions on Remote Work during the COVID-19 Pandemic. Thematic and Sentiment Analysis

Machine learning-based mathematical modelling for prediction of social media consumer behavior using big data analytics

Predicting consumers’ choices in the age of the internet, AI, and almost perfect tracking: Some things change, the key challenges do not

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