In the previous section, we utilized simple binary features for the words in the reviews in order to learn positive and negative sentiments. A better approach would be to use latent features, such as the frequency of the words used in the text. Compared to a binary representation of the presence or absence of words, the count of the words may better capture the characteristics of the text or document. Bag-of-words is a vector representation of text. Each of the vector dimensions captures either the frequency, presence or absence, or weighted values of words in the text. A bag-of-words representation does not capture the order of the words.
The binary feature extraction that was discussed in the previous section is, therefore, a simple bag-of-words representation of text. We will now look at an example of classifying sentiments in tweets using...