In this section, we are going to get a list of sentences and classify them based on the user's sentiment. We want to tell whether the sentence carries a positive or a negative sentiment. Dimitrios Kotzias et al created this dataset for their research paper, From Group to Individual Labels using Deep Features. They collected a list of random sentences from three different websites, where each sentence is labeled with either 1 (positive sentiment) or 0 (negative sentiment).
In total, there are 2,745 sentences in the data set. In the following sections, we are going to download the dataset, preprocess it, and classify the sentences in it.
Downloading the data
You can just open the browser, download the CSV files into a local folder, and use pandas to load the files into DataFrames. However, I prefer to use Python to download the files, rather than the browser. I don't do this out of geekiness, but...
