# A Method for Identifying the Mood States of Social Network Users Based on Cyber Psychometrics
## 3 Materials and Methods
### Detecting Emotional Features of Weibo Text
\begin{table}
\begin{tabular}{c c c} \hline \hline
**POMS Subscales** & **Items** & **Seed Words** \\ \hline \multirow{3}{*}{Tension} & Restless, Nervous, On-edge, Tense, & Agitated, Anxious, Disturbed, Nervous, \\  & Uneasy, Anxious & Edgy, Antsy, Uneasy, Concerned, Tense, \\  & & Apprehensive, Straianed, Impatient, \\  & & Insecure, Afraid, Distressed \\ \hline \multirow{3}{*}{Anger} & Peeved, Bitter, Resentful, Grouchy, & Annoyed, Enraged, Heated, Resentful, \\  & Angry, Furious, Annoyed & Indignant, Outraged, Sullen, Bitter, Furious, \\  & & Irate, Irritable, Offended, Mad \\ \hline \multirow{3}{*}{Fatigue} & Worn out, Weary, Bushed, & Bored, Bushed, Disgusted, Exhausted, \\  & Fatigued, Exhausted & Fatigued, Overworked, Jaded, Drained, \\  & & Weakened, Disabled, Sleepy, Overtired \\ \hline \multirow{3}{*}{Depression} & Hopeless, Helpless, Sad, & Desperate, Forlorn, Helpless, Sad, Tragic, \\  & Worthless, Miserable, Discouraged & Dejected, Discouraging, Gloomy, Pathetic, \\  & & Wortched, Useless, Despairing, Depressed, \\  & & Dismayed, Pessimistic \\ \hline \multirow{3}{*}{Vigor} & Cheerful, Vigorous, Full of pep, & Animated, Bright, Optimistic, Lively, Rosy, \\  & Active, Energetic, Lively & Chirpy, Cheery, Perky, Merry, Buoyant, \\  & & Active, Zealous, Potent, Dynamic, Brisk, \\  & & Spirited, Vital, Peppy, Frisky \\ \hline \multirow{3}{*}{Confusion} & Bewildered, Forgetful, Confused, & Added, Puzzled, Perturbed, Distracted, \\  & Concentration, Uncertain & Dazed, Bewildered, Refuddled, Confused, \\  & & Disorganized, Forgetful, Intententive \\ \hline \multirow{3}{*}{Esteem} & Embarrassed, Ashamed, Proud, & Ashamed, Abashed, Apologetic, \\  & Competent, Satisfied & Embarrassed, Humbled, Honored, Noble, \\ \cline{1-1}  & & Satisfied, Appreciative, Content, \\ \cline{1-1}  & & Competent, Qualified, Decent \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Extracting seed words from an English thesaurus. The cosine similarity of word vectors is computed here to select words added into the corpus. Given that \(word_{1}=(v_{1}^{word_{1}},v_{2}^{word_{1}},\dots v_{3}^{word_{1}})\), and \(word_{2}=(v_{1}^{word_{2}},v_{2}^{word_{2}},\dots v_{m}^{word_{2}})\), where \(m\) is the dimension of word vector, the cosine similarity could be calculated as:  
\[sim(word_{1},word_{2})=\cos\theta=\sum_{k=1}^{m}\frac{v_{k}^{word_{1}}\times v _{k}^{word_{2}}}{\sqrt{\sum_{k=1}^{m}\left(v_{k}^{word_{1}}\right)^{2}\times \sum_{k=1}^{m}\left(v_{k}^{word_{2}}\right)^{2}}} \tag{1}\]  
The numerator represents the dot product of two-word vectors and the denominator represents the modular product of two-word vectors. This research set 0.8 as the threshold of the similarity value, and traversed the corpus to obtain the top-10 most similar words with seed words. After filtering through the threshold, the extended word was added into candidate word sets. Based on the new candidate word sets, the same procedure was carried out repeatedly and the next iteration operated back and forth until there were no new words extracted. Finally, manual verification was adopted to remove words with large deviation or no meaning. As a result, approximately 3000 candidate words were selected and a mood state lexicon containing seven dimensions of mood state words (tension, anger, fatigue, depression, vigor, confusion, and esteem) was constructed. The semantic distance between words in tweets and the seed words in the lexicon was computed and the score of the word in each mood state dimension could be obtained. For every user, the word set of each tweet was represented as Logwords \(=\{word_{1},word_{2},\dots,word_{n}\}\), and the seed words set of mood states was \(L_{SeedWordsets}=\{L_{s1},L_{s2},\dots,L_{s7}\}\), where \(L_{si}\) contains a set of seed words in one dimension.