## 3 Results
### Sentiment Analysis  
The results of the sentiment analysis showed that two emotions prevailed in tweets during enrollment season: \"trust\" and \"fear\" (Table 1). The emotion \"trust\" was the primary driver of positive sentiment expressed in tweets, while the emotion \"fear\" was the primary driver of negative sentiments and accounted for the slightly negative overall sentiment. Trust was expressed in the context of doctors, nurses, and other medical providers. Here is an example of a tweet discussing this trustworthy role: \"Patients value their family doctor at the bedside when gravely ill. Healing presence is so powerful.\"  
In a tweet like this, the NRC Emolex classified the word \"value\" as positive and associated with \"trust,\" while the word \"healing\" is classified as positive and associated with the emotions of anticipation, joy, and trust. Another tweet referred to the importance of continuity of care: \"Seeing anxiety in culture from lack of relationship with continuity of fam Doctor.\" The word \"anxiety\" is classified by the NRC Emolex as negative and associated with the emotions of fear, anticipation, and sadness. In this way, the words used in tweets are given a subjectivity score and common ones are reported in Table 1.  
Fear was conveyed in Tweets about medical events such as \"lose,\" \"disease,\" \"emergency,\" \"surgery,\" \"cancer,\" and unanticipated costs. Consumers expressed both negative and positive \begin{table}
\begin{tabular}{l l} \hline \hline
**Positive** & **Negative** \\ \hline \hline
**Doctor (trust)** & Pressure (negative) \\ \hline
**Physician (trust)** & Die (fear, sadness) \\ \hline
**Hospital (trust)** & Emergency (fear, anger, disgust; sadness) \\ \hline
**Nurse (trust)** & Disease (fear, anger, disgust, sadness) \\ \hline
**Plan (anticipation)** & Pain, surgery (fear, sadness) \\ \hline
**Save money (joy)** & Miscarriage (fear, sadness) \\ \hline
**Choice (positive)** & Cancer (fear, anger, disgust, sadness) \\ \hline \hline \end{tabular} Note: In brackets are the emotions that the NRC Emolex associated with the word used in the tweets.  
\end{table} 
Table 1: Positive and negative emotions expressed on Twitter, by the words used in the tweets. Figure 1: Word associations for plan attributes and switching among health insurance tweets. sentiments about choice, but the NRC Emolex could not specify the exact emotion. For example, one tweet stated: \"I hate choice so much that I've essentially disregarded my doctor because I've had such a low care of my own wellbeing.\" It also identified \"pressure\" as being negative but could not specify what kind of emotion consumers expressed. Overall, the sentiment of consumers toward health insurance was slightly negative, although most sentiments were not at either extreme (Figure 2). The figure shows how the words used in the tweets were classified: either positive or negative. It follows from the histogram that most words were classified as \"slightly negative\" (-1) and few words were classified as either extremely negative or extremely positive. To understand what attitudes consumers expressed regarding specific attributes of health plans, we examined \"premium,\" \"access,\" and \"network.\" Table 2 illustrates that consumers used the insurance attribute \"premium\" most often in combination with words like \"increase\" or \"relief.\"  
Access was associated most of the time with \"nocopay,\" suggesting that consumers who care about access also care about copays. The attribute \"network\" was associated with \"narrow\" 23% of the time, and with \"providers\" 16% of the time, suggesting that many consumers talk about narrow network plans when discussing health insurance.