A Neural Network Approach for Predicting Personality From Facebook Data

Başaran, Seren; Ejimogu, Obinna H.

doi:10.1177/21582440211032156

Cited by 23 publications

(10 citation statements)

References 41 publications

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“…Therefore, we aim at finding novel engaging ways to collect such individual features, without bothering the user too much. New methods proposed in recent studies include, for example, asking the individual to select some pictures [34], [89], [90], inviting the user to play a game [91], [92], predicting personality traits from static facial images [93], [94], [95] or exploiting social media data [96], [97], [98], [99].…”

Section: Discussionmentioning

confidence: 99%

How Personality Traits can be Used to Shape Itinerary Factors in Recommender Systems for Young Travellers

et al. 2023

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Planning an itinerary is a complex activity, which includes the choice of a few places to see, coupled with information on timing, transferring methods and related activities. Intelligent tools such as recommender systems have been used in order to support these activities. While decisions regarding the type of trip to undertake are strongly influenced by tourists' personalities, currently only a few recommenders exploit information about this aspect. Our aim is to provide itinerary recommender system designers with some guidance on the integration of knowledge on personality traits and itinerary factors in a recommender. To do so, first we modeled the most important aspects of an itinerary, starting from the state-of-the-art literature on recommender systems for tourism. We identified thirteen factors, from the variety (in type and topic) of Points-of-Interest (POIs) to the expected duration of transfer times, grouped into three broader dimensions (POIs, time and choice modality). Then, we carried out a survey-based study on Generation Z (namely, the generation of people born between 1996/1997 and 2012) to investigate if the Big Five personality traits can affect the user's decision-making process when planning an itinerary, and, in particular if they are related to user preferences for the itinerary factors in our model. Finally, we used our findings to define some guidelines for the design of advanced itinerary recommender systems.

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Section: Discussionmentioning

confidence: 99%

How Personality Traits can be Used to Shape Itinerary Factors in Recommender Systems for Young Travellers

et al. 2023

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“…The label for defining the Big Five traits has been annotated by physiological experts. On the other hand, Youtube dataset was an open-source dataset from ChaLearn [7]. Before processing in the development phase, both datasets were preprocessed and augmented to get better-quality datasets.…”

Section: Methodsmentioning

confidence: 99%

“…The second dataset, first impression Youtube, consists of 10.000 short videos with text-based english transcripts. This dataset is public and downloaded from ChaLearn [7]. Both datasets are already labeled with Big Five Personality traits so they can be processed with the supervised learning method [17].…”

Section: Datasetmentioning

confidence: 99%

“…Table 1 below describes the advantages of the Big Five Personality approach compared to the other two. The Big Five Personality consists of five personality traits that are usually called OCEAN (Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism) [7]. This research uses two social media datasets to build a prediction model.…”

Section: Introductionmentioning

confidence: 99%

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Personality Identification from Social Media Using Ensemble Bert and Roberta

Tsani,

Suhartono

2023

IJCAI

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Social media growth was fast because many people used it to express their feelings, share information, and interact with others. With the growth of social media, many researchers are interested in using social media data to conduct research about personality identification. The identification result can be used as a parameter to screen candidate attitudes in the company's recruitment process. Some approaches were used for research about personality; one of the most popular is the Big Five Personality Model. In this research, an ensemble model between BERT and RoBERTa was introduced for personality prediction from the Twitter and Youtube datasets. The data augmentation method also introduces to handling the imbalance class for each dataset. Pre-trained model BERT and RoBERTa was used as the feature extraction method and modeling process. To predict each trait in the Big Five Personality, the voting ensemble from BERT and RoBERTa achieved an average f1 score 0,730 for Twitter dataset and 0,741 for Youtube dataset. Using the proposed model, we conclude that data augmentation can increase average performance compared to the model without data augmentation process.Povzetek: Članek uvaja model združevanja siestemov BERT in RoBERTa za napovedovanje osebnosti iz podatkov Twitterja (X) in Youtube, z izboljšanjem s pomočjo podatkovne augmentacije.

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“…Such linear models as autoregressive integrated moving average (ARIMA; Li, Han, et al, 2018) and gray model (GM; Li, 2014) have achieved significant predictive results. However, nonlinear models, including the BP-neural network (Dantas et al, 2017), recurrent neural network (RNN; Başaran and Ejimogu, 2021), long-and short-term memory network (LSTM; Greff et al, 2017;Kong et al, 2017;Li & Cao, 2018), and support vector regression (SVR; Liang et al, 2015;Li, Ni, et al, 2018;Sun et al, 2011Sun et al, , 2014Tuo, 2012) were found to be more robust than linear ones due to their strong fault-tolerance levels.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach to Air Passenger Index Prediction: Based on Mutual Information Principle and Support Vector Regression Blended Model

et al. 2022

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Air passenger traffic prediction is crucial for the effective operation of civil aviation airports. Despite some progress in this field, the prediction accuracy and methods need further improvement. This paper proposes an integrated approach to the prediction of air passenger index as follows. Firstly, the air passenger index is defined and classified by the K-means clustering method. And then, based on mutual information (MI) principle, the information entropy is used to analyze and select the key influencing factors of air passenger travel. By incorporating the MI principle into the support vector regression (SVR) framework, this paper presents an innovative MI-SVR machine learning model used to predict the air passenger index. Finally, the proposed model is validated by air passenger throughput data of the Shanghai Pudong International Airport (PVG), China. The experimental results prove MI-SVR model feasibility and effectiveness by comparing them with conventional methods, such as ARIMA, LSTM, and other machine learning models. Besides, it is shown that the prediction effect of each model could be improved by introducing influencing factors based on MI. The main findings are considered instrumental to the airport operation and air traffic optimization.

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A Neural Network Approach for Predicting Personality From Facebook Data

Cited by 23 publications

References 41 publications

How Personality Traits can be Used to Shape Itinerary Factors in Recommender Systems for Young Travellers

How Personality Traits can be Used to Shape Itinerary Factors in Recommender Systems for Young Travellers

Personality Identification from Social Media Using Ensemble Bert and Roberta

A Novel Approach to Air Passenger Index Prediction: Based on Mutual Information Principle and Support Vector Regression Blended Model

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