A Method for Identifying the Mood States of Social Network Users Based on Cyber Psychometrics

Abstract

Analyzing people's opinions, attitudes, sentiments, and emotions based on user-generated content (UGC) is feasible for identifying the psychological characteristics of social network users. However, most studies focus on identifying the sentiments carried in the micro-blogging text and there is no ideal calculation method for users' real emotional states. In this study, the Profile of Mood State (POMS) is used to characterize users' real mood states and a regression model is built based on cyber psychometrics and a multitask method. Features of users' online behavior are selected through structured statistics and unstructured text. Results of the correlation analysis of different features demonstrate that users' real mood states are not only characterized by the messages expressed through texts, but also correlate with statistical features of online behavior. The sentiment-related features in different timespans indicate different correlations with the real mood state. The comparison among various regression algorithms suggests that the multitask learning method outperforms other algorithms in root-mean-square error and error ratio. Therefore, this cyber psychometrics method based on multitask learning that integrates structural features and temporal emotional information could effectively obtain users' real mood states and could be applied in further psychological measurements and predictions.

Lifestyles in Amazon: Evidence from online reviews enhanced recommender system

“…Researches in opinion mining and sentiment analysis indicated that a lexicon construction is the basis of users’ emotions and opinions recognition (Huang, Niu, & Shi, 2014). We introduced the lexicon construction method used in our previous study (Wang, Li, Huang, Liu, & Zhang, 2017) applied both semantic knowledge and Amazon corpus to construct an online lifestyles lexicon. First, we obtained seven seed words sets including dimensions like “Comfortable,” “Entertainment,” “Luxury,” “Tradition & Conservation,” “Rational,” “Fashion Sense,” and “Social Activities.” Second, we used the synsets in WordNet to extend the seed words.…”

Personality or Value: A Comparative Study of Psychographic Segmentation Based on an Online Review Enhanced Recommender System

“…Lexicon is the basis for utilizing customer word use behaviors to automatically extract psychological traits. The combination of semantic knowledge and online corpora not only utilizes the semantic relationships as prior knowledge to provide accurate seed words but, also, help to foster the word association information, such as term position and word co-concurrency in the corpus [60]. Based on semantic knowledge and Amazon corpus, our research applies the NLP method to automatically construct SVS and BFF consumer psychographic lexicons for consumer value and personality identification.…”

Three years - three moves - government verdicts to renovate customary 'Bharat' to contemporary 'India': evaluation of opinions from citizens

“…Manual analysis, though attains highest precision ingests costs, time and resources. Lexicon analysis, on the other hand, espouses the automated evaluation of sentiment analysis and thus commonly accepted (Wang et al, 2017). When constructing a lexicon, firstly, seed words are generated, which then identifies the mood state of responses grounded on these seed words (Mantyla et al, 2017).…”