# Learning Word Embeddings with Chi-Square Weights for Healthcare Tweet Classification
## 2 Related Work  
Microblogging sites and online healthcare forums distribute many posts that share aspects of an individual's life and experience each day. The potential for working with great amounts of real healthcare-related clinical records, disease and symptom descriptions and even clinical transcripts attracted many researchers with interesting projects. To further extract and track healthcare information especially from users' social media profiles, the most basic and crucial task is to discriminate the healthcare-related tweets or target users from the massive pool of tweets and users that are irrelevant to the topic. [PERSON] et al. note that prior research on eating disorders only focused on datasets collected from particular forums and communities [4]. Their goal was to identify behavioral patterns and psychometric properties of real users that suffered from eating disorders and not the patterns of people who simply discussed it on Twitter. They proposed a snowball sampling method to collect data based on the labeled eating disorder users' social media connections. [PERSON] et al. also used tweets to track influenza by distinguishing tweets about truly flu-affected people from the ones that express only concerns and awareness [3]. Because of the subtlety in distinguishing the two types of tweets, a keyword-based approach is insufficient since both sets contain typical keywords. [PERSON] et al. proposed handcrafted feature types such as a word lexicon, stylometric features and part-of-speech template features. However, handcrafted features have the problem of scalability. [PERSON] and [PERSON] [20] build an unsupervised topic model based on latent Dirichlet allocation (LDA) [21] to extract healthcare topics discussed in tweets. [PERSON] et al. designed a platform to detect the trend and breakout of disease at an early stage [8]. They identify healthcare-related tweets from a pre-defined keywords list. [PERSON] et al. tracked H1N1 activity levels and public concerns on Twitter in real time [5]. They used SVM and handcrafted features such as age, recent clinic visits, etc., to track public sentiment with respect to H1N1, the swine flu. Interestingly, they found hygiene keywords such as \"wash hands\" positively correlated with the outbreak of the disease. The popularity of the vector space model lies in its ability to quantify semantic similarities by the distributional structure of the language [22; 23]. The assumption here is that words with similar distributional statistics tend to have similar semantic meaning. The distributional structure of the language can be captured by multi-dimensional vectors learned from the words' co-occurrence statistics. The research based on this assumption to quantify words' meaning and similarity is called distributional semantics [24]. There are multiple vector space models implementing distributional semantics, including Latent Semantic Analysis (LSA) [25] and Latent Dirichlet Allocation (LDA) [21]. [PERSON] and [PERSON] endowed LSA with a psychological interpretation and used LSA as a computational theory to solve the fundamental problem of knowledge acquisition and knowledge representation [26]. Enlightened by LSA's capability to capture similarity between words and its usage of Singular Value Decomposition (SVD) [27] to smooth the vector and handle the sparseness, [PERSON] proposed capturing the relations between pairs of words and developed a new algorithm called Latent Relational Analysis (LRA) which also used SVD to smooth the data [28]. The context of a target word is defined as a small unordered number of words surrounding the target word in semantic space models. [PERSON] and [PERSON] incorporated the syntactic information (dependency relations) to represent the context of the target word and formed a general framework for the construction of semantic space models [29]. The development of distributional vector representation of words greatly solves the scalability issue by releasing engineers from tedious handcrafted feature creation work.