# A Method for Identifying the Mood States of Social Network Users Based on Cyber Psychometrics
## 1 Introduction  
In the psychological field of emotion science, emotions play a crucial role in people's decision-making [1]. Emotions constitute potent, pervasive, predictable, harmful, and beneficial drivers of judgment and decisions through multiple mechanisms [2]. Because mood states comprise of a particularly important set of sentimental factors [3], they may have significant effects on cognition control [4], consumer behavior [3], and investment decisions [5]. Differing from immediate emotions, mood state is a long-term, continuous, emotional state and is reflected in the process of cognitive behavior, including individual perception, learning, and decision-making [3, 6]. With the prevalence of Web 2.0, user-generated content (UGC) has exploded on social networks, especially on microblogging platforms such as Sina Weibo, Facebook, and Twitter, which provide data sources for social studies. As the technology of sentiment analysis and natural language processing (NLP) has gradually developed, many researchers have investigated sentiment analysis, applying it to opinion mining [7, 8] and prediction of stock performance [5]. Research attempts to acquire people's emotion or mood information by analyzing text data on social networks and extracting emotional words [9]. However, due to the anonymity of the internet, the self-disclosure of socialnetwork users is different from that in real life [10], which means the emotion expressed on the internet may not be in agreement with the users' real sentiments. Therefore, this study considered a stable and continuous emotional variable, mood state, trying to build a model to identify the mood state of social network users automatically. The mood state was defined as an indirect evaluation state that allows one to explain things and produce corresponding behaviors for the time being, according to their emotional content [11]. The Profile of Mood State (POMS) is a widely-used scale for the measurement of mood state and the abbreviated POMS used in this study consists of seven mood scales, measuring different aspects of psychological health of individuals [12]. The POMS has been used to study the influence of different mood states on the survival rate of long-term cancer patients [13]. It has been proven that there is a correlation between online behavior and psychological traits through empirical study [14]. In order to automatically identify the mood state of network users, this paper collected users' online behavior, extracted related features and adopted a multitask regression model to build the relationship between mood state and users' online behavior. The online information was used to calculate the psychological variables and this cyber psychometrics method focuses on making linkages from online behavior to psychological traits. Based on cyber psychometrics, this study proposed a multitask model to automatically calculate the long-term, continuous mood state, and a natural language processing technology was used to build the mood state lexicon in order to extract emotional information. Identifying the mood state of social network users may provide some information for advertisers to improve the recommendation performance. For example, a combination of users' mood with a mood-based music recommendation could possibly make a better effect [15]. Merchants may develop different strategies since people in different moods behave differently with respect to risky decision-making [16; 17]. Sentiment analysis techniques have been widely applied to the prediction and observation of political events [18], such as monitoring day-to-day electoral campaigns [19]. Thus, the automated information extraction method proposed by this study could be used to explore or track the preferences of social network users and provide reference information for political decisions as a growing number of citizens choose to express opinions and sentiments online.