From eda0c268818f1f4f2f6e8a2c83c58c34551f0e0d Mon Sep 17 00:00:00 2001 From: Jen Looper Date: Tue, 22 Jun 2021 17:43:56 -0400 Subject: [PATCH 1/2] NLP 4 edits --- 6-NLP/4-Hotel-Reviews-1/README.md | 20 +++++++++++++++----- 1 file changed, 15 insertions(+), 5 deletions(-) diff --git a/6-NLP/4-Hotel-Reviews-1/README.md b/6-NLP/4-Hotel-Reviews-1/README.md index b0e47ada7..dba067d06 100644 --- a/6-NLP/4-Hotel-Reviews-1/README.md +++ b/6-NLP/4-Hotel-Reviews-1/README.md @@ -1,4 +1,4 @@ -# Sentiment Analysis +# Sentiment Analysis: Hotel Reviews In this section you will use the techniques in the previous lessons to do some exploratory data analysis of a large dataset. Once you have a good understanding of the usefulness of the various columns, you will learn how to remove the unneeded columns, calculate some new data based on the existing columns, and save the resulting dataset for use in the final challenge. @@ -92,8 +92,8 @@ Here they are grouped in a way that might be easier to examine: ##### Examples -| Average Score | Total Number Reviews | Reviewer Score | Negative
Review | Positive Review | Tags | -| -------------- | ---------------------- | ---------------- | :----------------------------------------------------------- | --------------------------------- | ------------------------------------------------------------ | +| Average Score | Total Number Reviews | Reviewer Score | Negative
Review | Positive Review | Tags | +| -------------- | ---------------------- | ---------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------- | ----------------------------------------------------------------------------------------- | | 7.8 | 1945 | 2.5 | This is currently not a hotel but a construction site I was terroized from early morning and all day with unacceptable building noise while resting after a long trip and working in the room People were working all day i e with jackhammers in the adjacent rooms I asked for a room change but no silent room was available To make thinks worse I was overcharged I checked out in the evening since I had to leave very early flight and received an appropiate bill A day later the hotel made another charge without my concent in excess of booked price It s a terrible place Don t punish yourself by booking here | Nothing Terrible place Stay away | Business trip Couple Standard Double Room Stayed 2 nights | As you can see from this guest, they did not have a happy stay at this hotel. The hotel has a good average score of 7.8 and 1945 reviews, but this reviewer gave it 2.5 and wrote 115 words about how negative their stay was. If they wrote nothing at all in the Positive_Review column, you might surmise there was nothing positive, but alas they wrote 7 words of warning. If we just counted words instead of the meaning, or sentiment of the words, we might have a skewed view of the reviewers intent. Strangely, their score of 2.5 is confusing, because if that hotel stay was so bad, why give it any points at all? Investigating the dataset closely, you'll see that the lowest possible score is 2.5, not 0. The highest possible score is 10. @@ -496,6 +496,16 @@ print("Saving results to Hotel_Reviews_Filtered.csv") df.to_csv(r'Hotel_Reviews_Filtered.csv', index = False) ``` -### NLP & Sentiment Analysis Operations +--- +## 🚀Challenge -*I'm currently editing this final section* + + +## [Post-lecture quiz](tbd) + +## Review & Self Study + + +## Assignment + +[Poetic license](assignment.md) From 615b500eb701013ee31419ea5de35156f18b1620 Mon Sep 17 00:00:00 2001 From: Jen Looper Date: Tue, 22 Jun 2021 17:48:13 -0400 Subject: [PATCH 2/2] fix for dangling modifier --- 4-Classification/1-Introduction/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/4-Classification/1-Introduction/README.md b/4-Classification/1-Introduction/README.md index 869543d1e..111c41b51 100644 --- a/4-Classification/1-Introduction/README.md +++ b/4-Classification/1-Introduction/README.md @@ -25,7 +25,7 @@ To state the process in a more scientific way, your classification method create Before starting the process of cleaning our data, visualizing it, and prepping it for our ML tasks, let's learn a bit about the various ways machine learning can be leveraged to classify data. -Derived from [statistics](https://wikipedia.org/wiki/Statistical_classification), classification using classic machine learning uses features, such as 'smoker','weight', and 'age' to determine 'likelihood of developing X disease'. As a supervised learning technique similar to the regression exercises you performed earlier, your data is labeled and the ML algorithms use those labels to classify and predict classes (or 'features') of a dataset and assign them to a group or outcome. +Derived from [statistics](https://wikipedia.org/wiki/Statistical_classification), classification using classic machine learning uses features, such as 'smoker','weight', and 'age' to determine 'likelihood of developing X disease'. Because classification uses a supervised learning technique similar to the regression exercises you performed earlier, your data is labeled and the ML algorithms use those labels to classify and predict classes (or 'features') of a dataset and assign them to a group or outcome. ✅ Take a moment to imagine a dataset about cuisines. What would a multiclass model be able to answer? What would a binary model be able to answer? What if you wanted to determine whether a given cuisine was likely to use fenugreek? What if you wanted to see if, given a present of a grocery bag full of star anise, artichokes, cauliflower, and horseradish, you could create a typical Indian dish?