540.docx - -title\"Logistic Regression author\"Yang Sun date\"`` output word_document default html_document default-`{r setup include=FALSE

540.docx - -title"Logistic Regression author"Yang Sun...

This preview shows page 1 - 2 out of 6 pages.

--- title: "Logistic Regression" author: "Yang Sun" date: "`02/01/2021`" output: word_document: default html_document: default --- ```{r setup, include=FALSE} knitr::opts_chunk$set(echo = TRUE) ``` ## Load the Libraries + Functions Load all the libraries or functions that you will use to for the rest of the assignment. It is helpful to define your libraries and functions at the top of a report, so that others can know what they need for the report to compile correctly. Understanding whether a written review is positive or negative can be tricky as the context of what is being reviewed and other factors can impact the sentiment of a review. In this assignment, you will investigate if the words used in a reviews can predict their sentiment. The datasets come from a Kaggle project with labelled sentences which you can check out here []. For this assignment, you can pick one of three datasets to analyze: Amazon Reviews, Yelp Reviews, or Movie Reviews. The first column in each dataset is a measure of sentiment of the review (0 = negative, 1 = positive) and the second is the number of tokens (or words) in the review. The rest of the columns are words that were either used (coded as 1) or not used (coded at 0) in the review. The sentiment of the review should be used as your outcome in your binary logistic regression. For your predictors, choose 10-20 words to test if use of those words predicts the sentiment of the review.
Image of page 1
Image of page 2

  • Left Quote Icon

    Student Picture

  • Left Quote Icon

    Student Picture

  • Left Quote Icon

    Student Picture