Identifying User Needs from Social Media
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With the rise of social media, writings by ordinary people are becoming increasingly available for linguistic analysis. Such analyses offer great opportunities to identify individual users' needs from user-generated content, so that better tailored products or services can be recommended. Literature suggests that several types of human needs are universal and directly influence consumer purchase behavior. In this paper, we investigate the use of social media to identify such fundamental needs for individuals. We developed psychometric measures of universal needs through a crowd-sourced study. We also built several models to predict people's needs based on their writings. We conducted a detailed analysis of the models and showed that our models can effectively identify users' needs based on their social media data. Our results also confirm that some inferred needs correlate well with the actual product purchases and suggest a great potential for our models to significantly increase effectiveness of product recommendations.
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... Identifying one's true needs, however, is a challenging task. Yang and Li (2013) took inspiration from Maslow's theory of motivation to predict consumer's needs and purchasing behavior using social media. Ko et al. (2020) used Korean twitter and blogs to discover customer's unmet needs through Hierarchical Concept Search Space algorithm. ...
In this paper, we present a minimally-supervised approach to identify human needs expressed in tweets. Taking inspiration from Frustration-Aggression theory, we trained RoBERTa model to classify tweets expressing frustration which serves as an indicator of un-met needs. Although the notion of frustration is highly subjective and complex, the findings support the use of pretrained language model in identifying tweets with unmet needs. Our study reveals the major causes behind feeling frustrated during the lockdown and the second wave of the COVID-19 pandemic in India. Our proposed approach can be useful in timely identification and prioritization of emerging human needs in the event of a crisis.
... Identifying one's true needs, however, is a challenging task. Yang and Li (2013) took inspiration from Maslow's theory of motivation to predict consumer's needs and purchasing behavior using social media. Ko et al. (2020) used Korean twitter and blogs to discover customer's unmet needs through Hierarchical Concept Search Space algorithm. ...
In this paper, we present a minimally-supervised approach to identify human needs expressed in tweets. Taking inspiration from Frustration-Aggression theory, we trained RoBERTa model to classify tweets expressing frustration which serves as an indicator of un-met needs. Although the notion of frustration is highly subjective and complex, the findings support the use of pretrained language model in identifying tweets with unmet needs. Our study reveals the major causes behind feeling frustrated during the lockdown and the second wave of the COVID-19 pandemic in India. Our proposed approach can be useful in timely identification and prioritization of emerging human needs in the event of a crisis. Accepted @ SocialNLP, NAACL 2022.
... We will explain each work in details SN Computer Science clarifying their limitations and shortcomings in both the theoretical background and the recognition method. IBM Research group [18] identified individual needs based on consumer behavior using Ford's model with the aim of enhancing the quality of direct marketing and influencing purchasing behavior. The model includes 12 need categories that correlate and explain consumer behavior, namely structure, practicality, challenge, self-expression, excitement, curiosity, liberty, ideal, harmony, love, closeness and stability. ...
We propose an automatic, low-cost, large-scale, nonintrusive human need recognition framework that utilized a multi-layered psychological-based reference model and designed with different modules including data collection, preprocessing, feature extraction and contextualization module. The reference model comprises several classification and regression models to identify human psychological needs, measure their satisfaction levels, evaluate their surrounding environment around different life aspects during any subjective event or towards emerging topics at any time, and in any location, using their publicly available social media content. We evaluate the predictive powers of various textual, psychological, semantic, lexicon-based and Twitter-specific features. To provide benchmark results, we compare and evaluate the performance of diverse machine learning algorithms. Our results confirm the effectiveness of the developed reference model. The framework is used to recognize citizen needs in response to the New Zealand terror attacks which occurred on March 15th, 2019.
Social media platforms are often used by people to express their needs and desires. Such data offer great opportunities to identify users’ consumption intention from user-generated contents, so that better tailored products or services can be recommended. However, there have been few efforts on mining commercial intents from social media contents. In this paper, we investigate the use of social media data to identify consumption intentions for individuals. We develop a Consumption Intention Mining Model (CIMM) based on convolutional neural network (CNN), for identifying whether the user has a consumption intention. The task is domain-dependent, and learning CNN requires a large number of annotated instances, which can be available only in some domains. Hence, we investigate the possibility of transferring the CNN mid-level sentence representation learned from one domain to another by adding an adaptation layer. To demonstrate the effectiveness of CIMM, we conduct experiments on two domains. Our results show that CIMM offers a powerful paradigm for effectively identifying users’ consumption intention based on their social media data. Moreover, our results also confirm that the CNN learned in one domain can be effectively transferred to another domain. This suggests that a great potential for our model to significantly increase effectiveness of product recommendations and targeted advertising.
Augmented Reality (AR) is one of the most modern and attractive information visualization technologies. Despite the proliferation of AR with the spread of mobile devices, the technology is still limited in providing relevant and personalized experiences. This limitation inspired the idea to incorporate human needs in an augmented reality system to enable personalized and focused suggestions which is a step closer to achieving pervasiveness. The main question is: How to detect human needs from sensed data and provide augmented reality experiences to satisfy the needs? The research gives an overview of the analysis of data elements and sensor requirements for the fundamental human needs defined by Manfred Max-Neef; a proof-of-concept prototype that enables need detection; prediction of the subsistence, protection, and leisure needs from analyzed data; and recommendations on augmented reality experiences based on human needs. An experiment is conducted, data analysis and predictive modelling techniques are applied to the Context-Aware Personalization for Augmented Reality (CAP-AR) dataset to achieve the research objectives. A reflection on the data requirements to predict human needs and implications on planning and design of pervasive applications to detect and satisfy human needs constitutes the research results.KeywordsDataAnalyticsHuman needsNeeds detectionAugmented reality
User needs inform designers and developers of essential functionalities for requirements engineering. In this work, we summarize key concepts and challenges relating to manual and automatic user needs detection methods. We discuss six challenges with manual and eight challenges with automated methods. Despite the promise of automated methods, the challenges imply that artificial intelligence and machine learning are not yet mature enough to replace manual methods, such as interviews and focus groups, for discovering user needs in requirements engineering.
The Juster Scale is a purchase probability scale used to predict the actual purchase rate in a population from a sample of consumers from that population. Several overseas studies have demonstrated the predictive ability of the Juster Scale, particularly for cars. This paper reports the findings of two New Zealand studies involving the Juster Scale. The first confirms the superior predictive ability of the Juster Scale compared to buying intentions scales, and both studies confirm the practicality of using the Juster Scale for predicting purchases of a range of items including durables, services and fast-moving consumer goods. The ability of the Juster Scale to accurately predict car purchases was corroborated, but the accuracy of predictions for other items was variable.
Perceptual mapping is widely used in marketing to analyze market structure, design new products, and develop advertising strategies. This article presents theoretical arguments and empirical evidence which suggest that factor analysis is superior to discriminant analysis and similarity scaling with respect to predictive ability, managerial interpretability, and ease of use.
Thesupport-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space. In this feature space a linear decision surface is constructed. Special properties of the decision surface ensures high generalization ability of the learning machine. The idea behind the support-vector network was previously implemented for the restricted case where the training data can be separated without errors. We here extend this result to non-separable training data.High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated. We also compare the performance of the support-vector network to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
This new edition retains ints authoritative presentation of marketing theory while still maintaining an interesting and engaging writing style. Stewart Adam, Deakin University; Sara Denize, University of Western Sydney, Australia. <br /
Perceptual mapping is widely used in marketing to analyze market structure, design new products, and develop advertising strategies. This article presents theoretical arguments and empirical evidence which suggest that factor analysis is superior to discriminant analysis and similarity scaling with respect to predictive ability, managerial interpretability, and ease of use.
The support-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space. In this feature space a linear decision surface is constructed. Special properties of the decision surface ensures high generalization ability of the learning machine. The idea behind the support-vector network was previously implemented for the restricted case where the training data can be separated without errors. We here extend this result to non-separable training data. High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated. We also compare the performance of the support-vector network to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
In the era of social commerce, users often connect from e-commerce websites to social networking venues such as Facebook and Twitter. However, there have been few efforts on understanding the correlations between users' social media profiles and their e-commerce behaviors. This paper presents a system for predicting a user's purchase behaviors on e-commerce websites from the user's social media profile. We specifically aim at understanding if the user's profile information in a social network (for example Facebook) can be leveraged to predict what categories of products the user will buy from (for example eBay Electronics). The paper provides an extensive analysis on how users' Facebook profile information correlates to purchases on eBay, and analyzes the performance of different feature sets and learning algorithms on the task of purchase behavior prediction.