Because of the patient's complaints to Dr. I regarding his eyes, Dr. I referred him to an ophthalmologist. The ophthalmologist diagnosed recurrent pterygium in the right eye and performed pterygium surgery 2 months later.
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The ophthalmologist prescribed difluprednate eye drops post-surgery and issued a refill on a return visit 7 weeks after surgery. That prescription included instructions for tapering after 1 week and discontinuing the anti-inflammatory steroid 2 weeks thereafter. On a visit to Dr. I 2 months post-surgery, the patient complained of chronic ear pain and requested a referral to an ENT. Four months after his eye surgery, the patient visited Dr. I complaining of pressure in his eyes and pain from the surgery. The patient told Dr. I that he could not get in to see his ophthalmologist because of insurance issues.
He told Dr.
I that the difluprednate worked very well for the pain while nonsteroidal anti-inflammatory medications did not. He asked Dr. I for a refill of difluprednate to use until he could see his ophthalmologist again. I did an eye examination and assessed bilateral ocular pain and conjunctivitis. I charted that he explained the risks and benefits of ophthalmic corticosteroids.
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I issued a 1-week prescription for difluprednate and requested that the patient obtain his ophthalmologist's records for him. Seven weeks later, the patient returned to Dr.
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I complaining of a right earache and seeking a difluprednate refill. The patient reported that he was unhappy with his ophthalmologist and was seeking a new one whom he would visit once his insurance changed. On examination, Dr. I noted a slight increase of vascularity of conjunctiva in both eyes but no growths, lesions, ptosis, or discharge.
He prescribed cefdinir for the earache, a methylprednisolone pack, and difluprednate. I noted: "Pt. I noted that the patient was still aware and accepted the risks of long-term use of the difluprednate. Early the next year, the patient saw Dr. I for throat and right ear pain and reported that he had been seen by an ophthalmologist and that he was still working on getting records sent.
I charted a normal eye exam, noted a likely viral upper respiratory infection, and advised the man to follow up with an ENT for his ear pain and to continue the workup of his eye discomfort with the ophthalmologist. The patient requested another methylprednisolone pack, which Dr. University of Chicago Press: E. About Contact News Giving to the Press. Piracy Adrian Johns.
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Artistic License Darren Hudson Hick. The Cloaking of Power Paul O. Judicial Reputation Nuno Garoupa. Read the first chapter. Conley, William M.
In the previous chapter, we explored in depth what we mean by the tidy text format and showed how this format can be used to approach questions about word frequency. When human readers approach a text, we use our understanding of the emotional intent of words to infer whether a section of text is positive or negative, or perhaps characterized by some other more nuanced emotion like surprise or disgust. We can use the tools of text mining to approach the emotional content of text programmatically, as shown in Figure 2.
Figure 2. This chapter shows how to implement sentiment analysis using tidy data principles. One way to analyze the sentiment of a text is to consider the text as a combination of its individual words and the sentiment content of the whole text as the sum of the sentiment content of the individual words.
As discussed above, there are a variety of methods and dictionaries that exist for evaluating the opinion or emotion in text. The tidytext package contains several sentiment lexicons. Three general-purpose lexicons are. All three of these lexicons are based on unigrams, i. The bing lexicon categorizes words in a binary fashion into positive and negative categories. The AFINN lexicon assigns words with a score that runs between -5 and 5, with negative scores indicating negative sentiment and positive scores indicating positive sentiment.
How were these sentiment lexicons put together and validated? They were constructed via either crowdsourcing using, for example, Amazon Mechanical Turk or by the labor of one of the authors, and were validated using some combination of crowdsourcing again, restaurant or movie reviews, or Twitter data. Given this information, we may hesitate to apply these sentiment lexicons to styles of text dramatically different from what they were validated on, such as narrative fiction from years ago.
There are also some domain-specific sentiment lexicons available, constructed to be used with text from a specific content area.
Section 5. Dictionary-based methods like the ones we are discussing find the total sentiment of a piece of text by adding up the individual sentiment scores for each word in the text. Not every English word is in the lexicons because many English words are pretty neutral. For many kinds of text like the narrative examples below , there are not sustained sections of sarcasm or negated text, so this is not an important effect.
Also, we can use a tidy text approach to begin to understand what kinds of negation words are important in a given text; see Chapter 9 for an extended example of such an analysis.
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One last caveat is that the size of the chunk of text that we use to add up unigram sentiment scores can have an effect on an analysis. A text the size of many paragraphs can often have positive and negative sentiment averaged out to about zero, while sentence-sized or paragraph-sized text often works better.
With data in a tidy format, sentiment analysis can be done as an inner join. This is another of the great successes of viewing text mining as a tidy data analysis task; much as removing stop words is an antijoin operation, performing sentiment analysis is an inner join operation. What are the most common joy words in Emma?
This is a convenient choice because the sentiment lexicons and stop word datasets have columns named word ; performing inner joins and anti-joins is thus easier.
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