## 4 Discussion  
In this study, we used text data from Twitter to identify attitudes that consumers express towards health insurance, plan attributes, and health care providers. The health insurance choice literature focuses primarily on well-defined features of plans that are easily observable from administrative data such as benefit design, co-insurance rates, and deductibles. Previous studies found that financial considerations, such as premiums, deductibles, and maximum out-of-pocket spending caps, are important to consumers. This study reinforces some results from previous research. The sensitivity of consumers to higher premiums that our study finds is well documented in other literature. The role of provider networks has been debated recently--our study reinforces the importance of the networks to consumers.  
There are limitations associated with the bag of words approach that we used. The main disadvantage is that it severely limits the context of the tweet and loses the order of specific information. Also, it requires supervised machine learning, which entails modeling linguistic knowledge through the use of dictionaries containing words that are tagged with their semantic orientation [39]. This means that we used an existing data dictionary, but we accept the classification of the English words to identify emotions.  
It is a challenge to capture the essential meaning of a tweet in a machine-understandable format [40] as issues like short length, informal words, misspellings, and unusual grammar make it difficult to obtain a good representation to capture these text aspects. More recently, there has been a paradigm shift in machine learning towards using distributed representations for words [41] and sentences [42; 43]. Most studies analyzing tweets have not been able to use a fully unsupervised approach for message-level and phrase-level sentiment analysis of tweets. The advantage to such an approach would have been, for example, that we would have been able to convey emotions in the same manner in the tweets as in newspaper articles or blogs, reviews, or other types of user-generated content. In everyday life, we rely on context to interpret a piece of text or comment, so with bag of words it is harder to capture context as it merely focuses on isolated words or term frequencies.  
We do not know how the demographics of people tweeting about health insurance compares to the Affordable Care Act marketplace population, the Medicaid expansion population, and the uninsured. Tweets contain macro data about the user, but it is limited to whatever information the user decides to give. In practice, a small percentage of users provide personal information such as gender or age. We do have some information about location, but we lack this information for a substantial part of the sample and the level of information (city, state) differs by user.  
Another limitation is that tweets have social network-oriented properties, and therefore we believe that a good representation of our tweets should also capture social aspects. A social network analysis is beyond the scope of this study, as well as conversational science approach looking at how a comment is influenced by the previous one.