Optimization for Automatic Personality Recognition on Twitter in Bahasa Indonesia

Adi, Gabriel Yakub N.N.; Tandio, Michael Harley; Ong, Veronica; Suhartono, Derwin

doi:10.1016/j.procs.2018.08.199

Cited by 32 publications

(20 citation statements)

References 24 publications

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“…It has the advantages of good robustness and strong scalability. In recent years, it has shown excellent performance in many fields, such as information technology and software engineering [42], environmental science [30], and economics and finance [36]. XGBoost performs a second-order Taylor expansion on the loss function and uses a quadratic function to approximate the loss function.…”

Section: Extreme Gradient Boosting (Xgboost)mentioning

confidence: 99%

Prediction of Highway Tunnel Pavement Performance Based on Digital Twin and Multiple Time Series Stacking

Zhang

et al. 2020

Advances in Civil Engineering

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The existing pavement performance prediction methods are limited to single-factor predictions, which often face the challenges of high cost, low efficiency, and poor accuracy. It is difficult to simultaneously solve the temporal, spatial, and exogenous dependencies between pavement performance data and maintenance, the service life of highways, the environment, and other factors. Digital twin technology based on the building information modeling (BIM) model, combined with machine learning, puts forward a new perspective and method for the accurate and timely prediction of pavement performance. In this paper, we propose a highway tunnel pavement performance prediction approach based on a digital twin and multiple time series stacking (MTSS). This paper (1) establishes an MTSS prediction model with heterogeneous stacking of eXtreme gradient boosting (XGBoost), the artificial neural network (ANN), random forest (RF), ridge regression, and support vector regression (SVR) component learners after exploratory data analysis (EDA); (2) proposes a method based on multiple time series feature extraction to accurately predict the pavement performance change trend, using the highway segment as the minimum computing unit and considering multiple factors; (3) uses grid search with the k-fold cross validation method to optimize hyperparameters to ensure the robustness, stability, and generalization ability of the prediction model; and (4) constructs a digital twin for pavement performance prediction to realize the real-time dynamic evolution of prediction. The method proposed in this study is applied in the life cycle management of the Dalian highway-crossing tunnel in Shanghai, China. A dataset covering 2010–2019 is collected for real-time prediction of the pavement performance. The prediction accuracy evaluation shows that the mean absolute error (MAE) is 0.1314, the root mean squared error (RMSE) is 0.0386, the mean absolute percentage error (MAPE) is 5.10%, and the accuracy is 94.90%. Its overall performance is better than a single model. The results verify that the prediction method based on digital twin and MTSS is feasible and effective in the highway tunnel pavement performance prediction.

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Section: Extreme Gradient Boosting (Xgboost)mentioning

confidence: 99%

Prediction of Highway Tunnel Pavement Performance Based on Digital Twin and Multiple Time Series Stacking

Zhang

et al. 2020

Advances in Civil Engineering

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“…Naive Bayes model performs better than the others with Introvert-Extrovert (IE) accuracy is 72.5%. Next, Adi [7] developed the classifier model with 286 data for classifying the Indonesian Big-5 personality traits. There are 12 extraction features, namely the number of tweets, retweets, replies, followers, retweeted, hashtags, following, quotes, URLs, favorites, mentions and tweet content.…”

Section: Previous Workmentioning

confidence: 99%

Word Analysis of Indonesian Keirsey Temperament

Iskandar¹,

Utami²,

Prasetio³

2020

Indonesian J. Comput. Cybern. Syst.

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Personality uniquely relates to our feeling and pattern to the aspect of actions. This behavior will change through the experience, formal education, and the surrounding environment. This works based on the Keirsey Temperament Sorter, a personality questionnaire developed by David Keirsey. This model divides the personality into four categories as Idealists, Rationals, Guardians, and Artisans. This concept is commonly recognized for the interpretation of specialist trends, potentially contributes to the process of recruitment or selection, and potential fields for analysis of social media data. Words selected by using Chi-Square with an error of 5%. Accuracy of the lexicon approach is 34%, while the best machine learning approach is Random Forest algorithm with 69.59%

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“…Author Daniel Ricardo Jaimes Moreno [15] proposed the personality of users on Twitter using textbased features and compared the performance of multiple techniques. Author Derwin S [20], in Bahasa Indonesia, suggested an automatic twitter-based personality identification method. Machine learning algorithm namely Stochastic Gradient Descent(SGD),two ensemble learning algorithms, Gradient Boosting and stacking is used.…”

Section: A Research Reviewmentioning

confidence: 99%

“…Hyper parameter tuning, choice of features and sampling was used to address the data set's imbalance and noise. System with all the algorithms of optimization FS, HPT and sampling produces the greatest performance of all the models implemented [20]. Personality can be evaluated by means of tweeter tweets using evaluation of DISC(Dominance, Influence, Compliance, Steadiness).…”

Section: B General Framework For Personality Predictionmentioning

confidence: 99%

“…To build both the sentence vector from a sequence of word vectors and the document vector from a sequence of senten ce vectors, recurrent Long Short Term Memory (LSTM) net work can be developed. [20].Accuracy for personality predic tion can be enhanced with deep learning techniques and XG Boost algorithm for certain characteristics of personality pre diction. Resampling method can be used to considerably enh ance precision [2].To improve the performance of the frame work, combinations of descriptive features with latent features could be applied.…”

Section: Future Directionsmentioning

confidence: 99%

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Personality Prediction from Social Networks text using Machine Learning

Bhamare*,

Kumar

2019

IJRTE

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Personality, a typical way of thinking, feeling, and behaviour. Personality embraces moods, attitudes and views and is expressed most obviously in relationships with others. It involves both intrinsic and acquired behavioural features that differentiate one individual from another and can be found in the relationships of people with the surroundings and with the social group. With the development of social networks, a broad variety of techn iques have been developed to identify user personalities based on their social activities and language usage practices. In terms of distinct machine learning algorithms, information sources and function sets, particular methods vary. Personality prediction has been an important research topic for describing user profiles and person not only in psychology but also in computer science. This paper presents a systematic survey of current work done of personality prediction from social networks. We also prepared a Comparison chart of existing techniques for personality prediction on the basis of relevant parameters. Based on this survey, we finally presented a few future research directions related to personality prediction.

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Optimization for Automatic Personality Recognition on Twitter in Bahasa Indonesia

Cited by 32 publications

References 24 publications

Prediction of Highway Tunnel Pavement Performance Based on Digital Twin and Multiple Time Series Stacking

Prediction of Highway Tunnel Pavement Performance Based on Digital Twin and Multiple Time Series Stacking

Word Analysis of Indonesian Keirsey Temperament

Personality Prediction from Social Networks text using Machine Learning

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