Social network usage is growing exponentially every day. Different information are typically exchanged via social media platforms such as Facebook. User knowledge and what they have conveyed through changes in status are useful for learning about the behavior and human personality assessment. This work aims at setting up a framework that can predict the individual's personality based on Facebook user details. In order to analyze the individual's personality, big five model is used. The aim of this research is to predict the personality of user by using the status information present in their social media profile. Based on the analysis result, the user's personality is further classified into one of the categories present in the OCEAN model. The accuracy of personality prediction achieved by using Random Forest Classifier is 64.25%. The mean squared error is achieved using random forest regressor is 5.25.

Machine intelligence based personality prediction using social profile data

Determining the image sentiment is a tedious task for classification algorithms, owing to complexities in the raw images as well as the intangible nature of human sentiments. Classifying image sentiments is an evergreen research area, especially in social data analytics. In current times, it is a common practice for majority people to precise their feelings on the web by substituting text with the upload of images via a multiplicity of social media sites like Facebook, Instagram, Twitter as well as any other platform. To identify the emotions from visual cues, some visual features as well as image processing techniques are used. Several existing systems have already introduced emotion detection using machine learning techniques, but the traditional feature extraction strategies do not achieve the required accuracy on random objects. In the entire process, normalization of image, feature extraction, and feature selection are important tasks in the train module. This work articulates the newest developments in the field of image sentiment employing deep learning techniques. Also, the use of conventional machine learning techniques is compared along with deep learning algorithms. It is indicative that a combination of fast recurrent neural networks and CNN may produce high accuracy with minimum time complexity. It is noted from the survey that existing researchers reflect CNN provides around 96.50% average accuracy for sentiment classification on the flicker image dataset.

Image Sentiment Analysis Using Deep Learning

Social Network Mental Disorder Detection” or “SNMD” is an approach to analyse data and retrieve sentiment\nthat it embodies. Twitter SNMD analysis is an application of sentiment analysis on data from Twitter\n(tweets), in order to extract sentiments conveyed by the user. In this paper, we aim to review some papers\nregarding research in sentiment analysis on Twitter, describing the methodologies adopted and models\napplied, along with describing a generalized Python based approach. A prototype system is developed and\ntested.

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A Comprehensive Study on Social Network Mental Disorders Detection Via Online Social Media Mining

How people think, feel, and behave primarily is a representation of their personality characteristics. By being conscious of the personality characteristics of individuals whom we are dealing with or deciding to deal with, one can competently ameliorate the relationship, regardless of its type. With the rise of Internet-based communication infrastructures (social networks, forums, etc.), a considerable amount of human communications takes place there. The most prominent tool in such communications is the language in written and spoken form that adroitly encodes all those essential personality characteristics of individuals. Text-based Automatic Personality Prediction (APP) is the automated forecasting of the personality of individuals based on the generated/exchanged text contents. This paper presents a novel knowledge graph-enabled approach to text-based APP that relies on the Big Five personality traits. To this end, given a text, a knowledge graph, which is a set of interlinked descriptions of concepts, was built by matching the input text's concepts with DBpedia knowledge base entries. Then, due to achieving a more powerful representation, the graph was enriched with the DBpedia ontology, NRC Emotion Intensity Lexicon, and MRC psycholinguistic database information. Afterwards, the knowledge graph, which is now a knowledgeable alternative for the input text, was embedded to yield an embedding matrix. Finally, to perform personality predictions, the resulting embedding matrix was fed to four suggested deep learning models independently, which are based on convolutional neural network (CNN), simple recurrent neural network (RNN), long short-term memory (LSTM), and bidirectional long short-term memory (BiLSTM). The results indicated considerable improvements in prediction accuracies in all of the suggested classifiers.

/pdf/knowledge-graph-enabled-text-based-automatic-personality-3avhfg24.pdf

Knowledge Graph-Enabled Text-Based Automatic Personality Prediction

Emotions influence all aspects of human behavior. All of these aspects shape people's lives, directly impacting their ways of life. Some diseases are directly linked to emotions. Among them, depression is one of the diseases with the greatest impact on society. Hence, faced with this problem, the objective of this study is to present a context-aware solution based on text mining for gestational depression prevention. This system uses text mining to analyze documents filled from pregnant women in order to identify their feelings through natural language processing techniques and probabilistic algorithms. As a case study, the analyzed texts were obtained from forms answered by pregnant women. The model performance is evaluated using metrics associated with the confusion matrix. The results show that the proposed model has achieved a reliable performance in all metrics, mainly when classifying new cases. Thus, the results obtained by the model can be used as support to health professionals in monitoring high-risk pregnancies.

Recommender System for Postpartum Depression Monitoring based on Sentiment Analysis

Personality is an important parameter as it differentiates various individuals from one another. Personality prediction is an evergreen area of research. Predicting personality with the help of data through social media is a promising approach as this method does not require any questionnaires to be filled by users thus reducing time and increasing credibility. Thus having knowledge of personality is an interesting domain for researchers to work on. Predicting personality has many applications in real world. Use of social media is increasing day by day. Huge amount of textual data as well as images continue to explode to the web daily. Current work focuses on Linear Discriminate Analysis, Multinomial Naive Bayes and AdaBoost over Twitter standard dataset.

Using textual data for Personality Prediction:A Machine Learning Approach

Machine learning has gained tremendous attention from researchers recently. It has wide applications in tasks such as prediction and classification. Current work focuses on the effective detection of the personality of social network users. Personality is a combination of one’s thinking and behavior. Having knowledge about personality of a person has many applications in real world such as varied recommendation systems or HR departments. Personality of a person can be better understood by interacting with him/her. Predicting personality using social media is a new approach where direct interaction with people can be eliminated and accurate predictions can be built. Although different machine learning methods have been used by researchers recently for the task of prediction, the use of Ensembles has not been explored. Current work focuses on advanced classifiers such as XGBoost and Ensemble for prediction. Experimentation on the real-time Twitter dataset indicates high accuracy of 82.59% with an Ensemble. These results are encouraging for future research.

Personality Prediction of Social Network Users Using Ensemble and XGBoost

Use of social media is increasing day by day. A huge amount of textual data, as well as images, continue to explode to the web daily. Personality prediction is an evergreen area of research. Predicting personality with the help of data through social media is a promising approach as this method does not require any questionnaires to be filled by users thus reducing time and increasing credibility. In the current work we have fetched real-time Twitter data through a standard API and thereby created a sample experimental dataset. We have employed several popular machine learning algorithms such as kNN, Naive Bayes and SVM. It was observed that Naive Bayes yielded the highest accuracy of 71.67% as compared to the other classifiers.

Aditi V. Kunte

Papers

Using textual data for Personality Prediction:A Machine Learning Approach

Personality Prediction of Social Network Users Using Ensemble and XGBoost

Analysis of Machine Learning Algorithms for Predicting Personality: Brief Survey and Experimentation