Automatic Personality Prediction; an Enhanced Method Using Ensemble Modeling

Human personality is significantly represented by those words which he/she uses in his/her speech or writing. As a consequence of spreading the information infrastructures (specifically the Internet and social media), human communications have reformed notably from face to face communication. Generally, Automatic Personality Prediction (or Perception) (APP) is the automated forecasting of the personality on different types of human generated/exchanged contents (like text, speech, image, video, etc.). The major objective of this study is to enhance the accuracy of APP from the text. To this end, we suggest five new APP methods including term frequency vector-based, ontology-based, enriched ontology-based, latent semantic analysis (LSA)-based, and deep learning-based (BiLSTM) methods. These methods as the base ones, contribute to each other to enhance the APP accuracy through ensemble modeling (stacking) based on a hierarchical attention network (HAN) as the meta-model. The results show that ensemble modeling enhances the accuracy of APP

The contribution of unstructured data from social media for prediction in marketing management

The capacity to obtain market insights is a strategic need for companies to remain competitive. Despite this and the massive volume of data generated by consumers every second, companies rarely have the culture of making marketing decisions based on data and, when they do, rarely use consumer data widely available online, specially on social networks. One reason is that these data (e.g. texts) tend to be “dirty”, disorganized and bulky, a so-called unstructured data. Despite the complexity involved in extracting informational value from this data, companies can gain insights that can improve decision making and result in greater competitive performance. The purpose of this article is to discuss the benefits of new types of data that have become more abundant and accessible in Web 3.0, as well as new methods of analysis, particularly learning methods. For this, an extensive literature review was carried out and a topic modeling was conducted to get an overview of the data and methods. At the end, the article suggests six main marketing challenges that unstructured data analytics can contribute, improving companies’ competitiveness. The capacity to obtain market insights is a strategic need for companies to remain competitive. Despite this and the massive volume of data generated by consumers every second, companies rarely have the culture of making marketing decisions based on data and, when they do, rarely use consumer data widely available online, especially on social networks. One reason is that these data (e.g. texts) tend to be “dirty”, disorganized and bulky, a so-called unstructured data. The purpose of this article is to discuss the benefits of new types of data that have become more abundant and accessible in Web 3.0 through popular social networks, as well as new methods of analysis, particularly learning methods for prediction. For this, an extensive literature review was carried out and a topic modeling was conducted to get an overview of the data and methods. At the end, the article suggests six main marketing challenges that unstructured data analytics can contribute to overcome, improving companies’ competitiveness