Who's Who with Big-Five: Analyzing and Classifying Personality Traits with Smartphones

Chittaranjan, Gokul; Blom, Jan; Gática-Pérez, Daniel

doi:10.1109/iswc.2011.29

Cited by 187 publications

(158 citation statements)

References 18 publications

(34 reference statements)

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“…In contrast to emotion prediction based on typing behavior (5,6), smartphone usage (7,8), and smartphone speech recordings (9), we focus on movement data and heart rate data. The EmotionSense system does use accelerometer data, but not for emotion recognition (9).…”

Section: Comparison With Prior Workmentioning

confidence: 99%

“…Prior work on emotion detection from smartphone data includes the analysis of typing behavior on a smartphone (5,6) and smartphone usage (7,8). The EmotionSense system performed emotion detection directly on mobile phones via the analysis of speech, with additional sensors collecting information about the user and his environment including a Bluetooth sensor as a proxy for proximity information, GPS location, and accelerometer for inferring movement vs nonmovement (9).…”

Section: Introductionmentioning

confidence: 99%

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Emotion Recognition Using Smart Watch Sensor Data: A Mixed-Design Study (Preprint)

Quiroz¹,

Geangu²,

Yong³

2018

Preprint

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Background: Research in psychology has shown that the way a person walks reflects that person's current mood (or emotional state). Recent studies have started using smartphones to detect emotional states from movement data. Objective: This study investigates the use of movement sensor data from a smart watch to infer an individual's emotional state. We present our findings on a user study with 50 participants. Methods: The experimental design is a mixed-design study; within-subjects (emotions; happy, sad, neutral) and between-subjects (stimulus type: audio visual "movie clips", audio "music clips"). Each participant experienced both emotions in a single stimulus type. All participants walked 250m while wearing a smart watch on one wrist and a heart rate monitor strap on their chest. They also had to answer a short questionnaire (20 items; PANAS) before and after experiencing each emotion. The heart rate monitor serves as a supplementary information to our data. We performed time-series analysis on the data from the smart watch and a t-test on the questionnaire items to measure the change in emotional state. The heart rate data was analyzed using one-way ANOVA. We extracted features from the time-series using sliding windows and used the features to train and validate classifiers that determine an individual's emotion. Results: We had 50 young adults participate in our study, with 49 included for the affective PANAS questionnaire and all for the feature extraction. Participants reported feeling less negative affect after watching sad videos or after listening to the sad music, P < .006. For the task of emotion recognition using classifiers, our results show that the personal models outperformed personal baselines, and achieve median accuracies higher than 78% for all conditions of the design study for the binary classification of happiness vs sadness. Conclusions: Our findings show that we are able to detect the changes in emotional state with data obtained from the smartwatch as well as behavioral responses. Together with the high accuracies achieved across all users for the classification of happy vs sad emotional states, this is further evidence for the hypothesis that movement sensor data can be used for emotion recognition.

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Section: Comparison With Prior Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Emotion Recognition Using Smart Watch Sensor Data: A Mixed-Design Study (Preprint)

Quiroz¹,

Geangu²,

Yong³

2018

Preprint

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“…Presently, predicting personality traits based on smart phone usage data accesses call logs, SMS logs, GPS logs, social networking services logs, use of internet, use of Bluetooth and battery [2], [4], [6]. In a previous work, authors have analyzed the relationship between smart phone usage and self-perceived personality with the help of applications usage logs, call logs and SMS logs.…”

Section: Related Workmentioning

confidence: 99%

“…In a previous work, authors have analyzed the relationship between smart phone usage and self-perceived personality with the help of applications usage logs, call logs and SMS logs. Their feature set was enriched with features extracted from call data, SMS data [2]. Oliveira et al [4] suggested that, variables derived from the users' mobile phone call behavior as captured by call detail records and social network analysis of the call graph can be used to automatically infer the users' personality traits as defined by the Big Five model.…”

Section: Related Workmentioning

confidence: 99%

“…Recent research devoted towards predicting user personality is based on smart phone usage data, such as information extracted from call detail records (CDRs), the usage of short message services (SMS) and the usage of web, music, video, maps, proximity information derived from bluetooth, usage of internet etc [2]. These research focused only on how to use smart phone usage data to predict user personality.…”

Section: Introductionmentioning

confidence: 99%

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Predicting User Attitude by Using GPS Location Clustering

Sokasane

Kim

2015

IEICE Trans. Inf. & Syst.

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SUMMARYIn these days, recognizing a user personality is an important issue in order to support various personalized services. Besides the conventional phone usage such as call logs, SMS logs and application usages, smart phones can gather the behavior of users by polling various embedded sensors such as GPS sensors. In this paper, we focus on how to predict user attitude based on GPS log data by applying location clustering techniques and extracting features from the location clusters. Through the evaluation with one month-long GPS log data, it is observed that the location-based features, such as number of clusters and coverage of clusters, are correlated with user attitude to some extent. Especially, when SVM is used as a classifier for predicting the dichotomy of user attitudes of MBTI, over 90% F-measure is achieved.

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Recognizing and characterizing dynamics of cellular devices in cellular data network through massive data analysis

Liu

Nan

Zhang

et al. 2014

Int J Communication

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The user clients for accessing Internet are increasingly shifting from desktop computers to cellular devices. To be competitive in the rapidly changing market, operators, Internet service providers and application developers are required to have the capability of recognizing the models of cellular devices and understanding the traffic dynamics of cellular data network. In this paper, we propose a novel Jaccard measurement-based method to recognize cellular device models from network traffic data. This method is implemented as a scalable paralleled MapReduce program and achieves a high accuracy, 91.5%, in the evaluation with 2.9 billion traffic records collected from the real network. Based on the recognition results, we conduct a comprehensive study of three characteristics of network traffic from device model perspective, the network access time, the traffic volume, and the diurnal patterns. The analysis results show that the distribution of network access time can be modeled by a two-component Gaussian mixture model, and the distribution of traffic volumes is highly skewed and follows the power law. In addition, seven distinct diurnal patterns of cellular device usage are identified by applying unsupervised clustering algorithm on the collected massive traffic data. RECOGNIZING AND CHARACTERIZING DYNAMICS OF CELLULAR DEVICES 1885 a three-step method to identify an appropriate keyword from unformatted textual HTTP headers to represent a cellular device model. The first step is extracting all keywords that are possibly to be the description of a device model. The second step is selecting a small set of candidate keywords by evaluating the conditional probability of each keyword given a device model to decrease the computational workload. At the last step, a Jaccard coefficient [6] value of each candidate keyword is calculated, and the keyword with the largest value is selected to represent a device model. To meet the requirements of performance and scalability to handle billions of HTTP records, the algorithm is implemented as a paralleled program based on MapReduce programming model. Evaluation result in the real network shows that the proposed method can achieve pretty high recognition accuracy.With the capability of recognizing cellular device models, we study the traffic of cellular data network from a new perspective, the device model perspective. Our goal is to understand whether and how the traffic differs from cellular device models. In particular, we investigate the following three basic characteristics of different device models, the distribution of network access time, the distribution of traffic volume, and the diurnal usage patterns.The following key contributions of this paper are twofold:

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Who's Who with Big-Five: Analyzing and Classifying Personality Traits with Smartphones

Cited by 187 publications

References 18 publications

Emotion Recognition Using Smart Watch Sensor Data: A Mixed-Design Study (Preprint)

Emotion Recognition Using Smart Watch Sensor Data: A Mixed-Design Study (Preprint)

Predicting User Attitude by Using GPS Location Clustering

Recognizing and characterizing dynamics of cellular devices in cellular data network through massive data analysis

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