Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: “To get started, we need to import the libraries required for our sentiment analysis project. These libraries include pandas for data manipulation, scikit-learn for machine learning functionalities, NLTK for natural language processing tasks, and zipfile for handling compressed files.”

A block of code is set as follows:

# Import necessary libraries
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import LogisticRegression
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
import zipfile

Bold style is used to emphasize a word or phrase. Here is an example: “This dataset contains 1.6 million tweets labeled as 0 (negative), 2 (neutral), or 4 (positive).”

Italics style indicates important words or words that refer to a category, classification, or subject highlighted in the section. They are also used to reference figures or tables – for example, “Table 4.1 highlights the distinctive features of each framework, providing a quick and easy comparison.”

Keyword style is used for important keywords that are included in the glossary – for example, “Natural Language Processing (NLP) tools are crucial for web development projects that involve text analysis and processing.”

Tips or important notes appear like this:

Tip

Please note that this is a simplified example and may not work directly without some modifications depending on your development environment. Please adjust it as necessary.