Using textual data for Personality Prediction:A Machine Learning Approach
01 Nov 2019-
TL;DR: 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.
Abstract: 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.
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01 Jan 2021
TL;DR: It is indicative that a combination of fast recurrent neural networks and CNN may produce high accuracy with minimum time complexity, as existing researchers reflect CNN provides around 96.50% average accuracy for sentiment classification on the flicker image dataset.
Abstract: 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.
10 citations
Journal Article•
TL;DR: This paper aims to review some papers regarding research in sentiment analysis on Twitter, describing the methodologies adopted and models applied, along with describing a generalized Python based approach.
Abstract: Social Network Mental Disorder Detection” or “SNMD” is an approach to analyse data and retrieve sentiment
that it embodies. Twitter SNMD analysis is an application of sentiment analysis on data from Twitter
(tweets), in order to extract sentiments conveyed by the user. In this paper, we aim to review some papers
regarding research in sentiment analysis on Twitter, describing the methodologies adopted and models
applied, along with describing a generalized Python based approach. A prototype system is developed and
tested.
10 citations
01 Mar 2021
TL;DR: In this article, a context-aware solution based on text mining for gestational depression prevention is presented, which can be used as support to health professionals in monitoring high-risk pregnancies.
Abstract: 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.
5 citations
4 citations
TL;DR: A new method to predict personality implicitly based on demographic data is proposed, based on findings by previous researchers stating that there is a correlation between demographic data and personality trait.
Abstract: Currently, the common method to predict personality implicitly (Implicit Personality Elicitation) is Personality Elicitation from Text (PET). PET predicts personality implicitly based on statuses written on social media. The weakness of this method when applied to a recommender system is the requirement to have minimal one social media account. A user without such qualification cannot use such system. To overcome this shortcoming, a new method to predict personality implicitly based on demographic data is proposed. This proposal is based on findings by previous researchers stating that there is a correlation between demographic data and personality trait. To predict personality based on demographic data, a personality model (rule) is needed. This model correlates demographic data and personality. To apply this model to a recommender system, another model is needed, that is preference model which connects personality and preference. These two models are then applied to a personality-based recommender system for fashion. From performance evaluation, the precision of and user satisfaction to the recommendation is 60.19% and 87.50%, respectively. When compared to precision and user satisfaction of PET-based recommender system (which are 82% and 79%, respectively), the precision of demographic data-based recommender system is lower whereas the satisfaction is higher. Keywords—Implicit personality elicitation; demographic data; personality-based recommender system; personality trait
3 citations
References
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TL;DR: In this paper, a 10-item measure of the Big-Five personality dimensions is proposed for situations where very short measures are needed, personality is not the primary topic of interest, or researchers can tolerate the somewhat diminished psychometric properties associated with very brief measures.
6,574 citations
TL;DR: Results show that the predictive power of digital footprints over personality traits is in line with the standard “correlational upper-limit” for behavior to predict personality, with correlations ranging from 0.29 (Agreeableness) to 0.40 (Extraversion).
270 citations
"Using textual data for Personality ..." refers methods in this paper
...User’s digital footprints have been extracted and analyzed in [15]....
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TL;DR: This study attempts to build a system that can predict a person’s personality based on Facebook user information by implementing some deep learning architectures and succeeds to outperform the accuracy of previous similar research.
113 citations
TL;DR: A new approach that uses the social media platform Twitter to quantify suicide warning signs for individuals and to detect posts containing suicide-related content and the application of the martingale framework highlights changes in online behavior and shows promise for detecting behavioral changes in at-risk individuals.
Abstract: Suicidal ideation detection in online social networks is an emerging research area with major challenges. Recent research has shown that the publicly available information, spread across social media platforms, holds valuable indicators for effectively detecting individuals with suicidal intentions. The key challenge of suicide prevention is understanding and detecting the complex risk factors and warning signs that may precipitate the event. In this paper, we present a new approach that uses the social media platform Twitter to quantify suicide warning signs for individuals and to detect posts containing suicide-related content. The main originality of this approach is the automatic identification of sudden changes in a user's online behavior. To detect such changes, we combine natural language processing techniques to aggregate behavioral and textual features and pass these features through a martingale framework, which is widely used for change detection in data streams. Experiments show that our text-scoring approach effectively captures warning signs in text compared to traditional machine learning classifiers. Additionally, the application of the martingale framework highlights changes in online behavior and shows promise for detecting behavioral changes in at-risk individuals.
107 citations
"Using textual data for Personality ..." refers methods in this paper
...In [11] suicide related posts are determined from Twitter posts using random forest, simple logistics and J48....
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02 Feb 2018
TL;DR: This paper proposes a deep learning approach that extracts and fuses information across different modalities to integrate three sources of data at the feature level, and combines the decision of separate networks that operate on each combination of data sources at the decision level.
Abstract: User profiling in social media has gained a lot of attention due to its varied set of applications in advertising, marketing, recruiting, and law enforcement. Among the various techniques for user modeling, there is fairly limited work on how to merge multiple sources or modalities of user data - such as text, images, and relations - to arrive at more accurate user profiles. In this paper, we propose a deep learning approach that extracts and fuses information across different modalities. Our hybrid user profiling framework utilizes a shared representation between modalities to integrate three sources of data at the feature level, and combines the decision of separate networks that operate on each combination of data sources at the decision level. Our experimental results on more than 5K Facebook users demonstrate that our approach outperforms competing approaches for inferring age, gender and personality traits of social media users. We get highly accurate results with AUC values of more than 0.9 for the task of age prediction and 0.95 for the task of gender prediction.
92 citations