# 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  
There are many methods for handling semantic orientation recognition in natural language processing, including manual marking and the lexicon-based approach. Manual tagging has the highest accuracy but costs a great amount of time and resources. The lexicon-based method is widely used in sentiment analysis and many researchers have adopted approaches like word embedding to bootstrap lexicons for one particular domain or language [33; 34]. When constructing a lexicon, a seed word set is created using tools like WordNet, HowNet, and a synonymy thesaurus [35]. The list of words is expanded based on seed words according to synonymous or antonymous relations. In this paper, a mood state lexicon was constructed on the basis of abbreviated POMS. The 40 adjectives in abbreviated POMS are regarded as seed words and expanded via Tongyici Cilin (a Chinese synonym thesaurus) and Word2 Vec. The Tongyici Cilin contains both synonymous and related words. The Harbin Institute of Technology Information Retrieval Laboratory produced the extended version of Tongyici Cilin, composed of 77,343 words [36]. In this thesaurus, words are organized into a five-layer tree structure. In the fifth layer, every category contains several primitive words, which refer to words that have  
Figure 1: Constructing the model for identifying mood states. POMS: Profile of Mood State. only one meaning. Every group of primitive words has a code of five layers, which is represented by 11 letters and numbers. [PERSON] et al. has provided a method to calculate the similarity between primitive words in Tongyici Cilin according to their codes [37]. The 40 adjectives in abbreviated POMS are expanded in Tongyici Cilin to obtain seed words. Considering that this study was executed based on a Chinese thesaurus, a similar study was repeated using an English thesaurus to demonstrate the process. The result is shown in Table 2.  
Owing to the lack of updates for Tongyici Cilin and the diversity and variability of the network language, the vocabulary used by Weibo users is usually different from that used in written language. Thus, relying only on the general thesaurus cannot cover enough words under the specific network environment. The word embedding method can map words into high-dimensional numeric vectors and has been applied in computing similarity, lexical analogy, and machine learning. Word2 Vec is an open source natural language processing tool developed by Google in 2013, which translates texts into word vectors. Applying the deep neural network algorithm, Word2 Vec simplifies the processing of textual content into numeric operations in the high dimensional vector spaces. The similarity of word vectors represents the semantic similarity of textual words. Based on the semantic relevance of words, this paper utilized Word2 Vec to expand the word set from 5 million corpuses on Weibo [38]. Before the training of Word2 Vec model, a Chinese segmenting tool, Jieba, was used to cut words and then the stop words were removed. For the train of Word2 Vec, here are some parameter settings. The skip-gram model was adopted and hierarchical softmax method was used to train the skip-gram. The window size was set to be 6 and the dimensionality of the feature vectors was 100.