- Web Scraping Using Python
- Web Scraping Text Python Code
- Web Scraping Text Python Example
- Web Scraping Text Python
- Python Web Scraping Get Text
Table of Contents
Python & Data Processing Projects for $10 - $30. Web scraping and text mining using R or python. What follows is a guide to my first scraping project in Python. It is very low on assumed knowledge in Python and HTML. This is intended to illustrate how to access web page content with Python library requests and parse the content using BeatifulSoup4, as well as JSON and pandas. I don't see any problem with your code. If the method is returning None is because the find function of BeautifulSoup is not finding the tag and/or the attribute. Here is some suggestions: Try to use the function findall instead just find (it will return a list); Be sure that the class class is in the tag div; Try to use different libraries with the BeautifulSoup, like 'lxml', 'html5lib' etc.
This is a use case of web scraping twitter for sentiment analysis. Let's start from..Donald Trump.
I am not a big fan of Donald Trump. Technically, I don’t like him at all. However, he has this charismatic sensation effect. His name occupies most newspapers and social media all the time. People’s attitude towards him is dramatic and bilateral. His descriptive words are either highly positive or negative, which are some perfect material for web scraping and sentiment analysis.
The goal of this workshop is to use a web scraping tool to read and scrape tweets about Donald Trump with a web crawler. Then we conduct a sentiment analysis using python and find out public voice about the President. And finally, we visualized the data using Tableau public.
You Should Continue to Read:
- IF you don’t know how to scrape contents/comments on social media.
- OR/AND IF You know Python but don’t know how to use it for sentiment analysis.
Let’s start with scraping using Octoparse. Downloaded the newest version from official websites and finished registration by following the instructions. After you log in, open the built-in Twitter template.
Tweet Data Extracted in the Scraper
- Name
- Publish time
- Content
- Image URL
- Tweet URL
- Numbers of comments, retweets, and likes
Enter “Donald Trump” at the Parameter field to tell the crawler the keyword. Just as simple as it seemed, I got about 10k tweets. You can scrape as many tweets as possible. After getting the tweets, export the data as a text file, name the file as “data.txt”.
Sentiment Analysis using Python
Before getting started, make sure you have Python and a text editor installed on your computer. I use Python 2.7 and Notepad++.
Then we use two opinion word lists to analyze the scraped tweets. You can download them from here. These two lists contain positive and negative words (sentiment words) that were summarized by Minqing Hu and Bing Liu from research study about presented opinions words in social media.
The idea here is to take each opinion word from the lists, return to the tweets, and count the frequency of each opinion words in the tweets. As a result, we collect corresponding opinion words in the tweets and the count.
First, create a positive and negative list with two downloaded word lists. They store all the words that are parsed from the text files.
Then, preprocess texts and massage the data by taking out all the punctuations, signs and numbers with the following code
As a result, the data only consisted of tokenized words, which makes it easier to analyze. Afterword, create three dictionaries: word_count_dict, word_count_positive, and word_count_negative.
Next, define each dictionary. If an opinion word exists in the data, count it by increasing word_count_dict value by “1”.
Afterwords counting, decide whether a word sounds positive or negative. If it is a positive word, word_count_positive increases its value by “1”, otherwise positive dictionary remains the same value. Respectively, word_count_negative increases its value or remains the same value. If the word is not present in either positive or negative list, it is a pass.
Polarity: Positive vs. Negative
![Web Scraping Text Python Web Scraping Text Python](/uploads/1/3/8/3/138383215/989507032.png)
As a result, I got 5352 negative words and 3894 positive words, saved the list with your choice of name, and opened it with Tableau public, and build up a bubble chart. If you don't know how to use Tablau public to create bubble chart, click here.
The use of positive words is unilateral. There are only 404 kinds of positive word used. The most frequent words are, for example, “like”, “great” and “right”. Most word choices are basic and colloquial, like “wow” and “cool,” whereas the use of negative words is much more multilateral. There are 809 kinds of negative word that most of them are formal and advanced. The most frequently used are “illegal,” “lies,” and “racist.” Other advanced words such as “delinquent”, “inflammatory” and “hypocrites” are also present.
The choice of words clearly indicates the level of education of whom is supportive is lower than that disapproval. Apparently, Donald Trump is not so welcomed among Twitter users.
Summary
In this article, we talked about how to scrape tweets on Twitter using Octoparse. We also discussed how to preprocess data text and analyze positive/negative opinion words expressed on Twitter using Python. For a complete version of the code, you can download here (https://gist.github.com/octoparse/fd9e0006794754edfbdaea86de5b1a51)
Ashley is a data enthusiast and passionate blogger with hands-on experience in web scraping. She focuses on capturing web data and analyzing in a way that empowers companies and businesses with actionable insights. Read her blog here to discover practical tips and applications on web data extraction Artículo en español: Scraping de Twitter y Análisis de Sentimientos Utilizando Python También puede leer artículos de web scraping en El Website Oficial |
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Introduction
In this post, we'll cover how to scrape Newegg using python, lxml and requests. Python is a great language that anyone can pick up quickly and I believe it's also one of the more readable languages, where you can quickly scan the code to determine what it is doing.
Just look at this loop with auto incrementing index:
We'll scrape Newegg with the use case of monitoring prices and inventory, especially the RTX 3080 and RTX 3090.
Setting up
Web Scraping Using Python
We're going to work in a virtual python environment which helps us address dependencies and versions separately for each application / project. Let's create a virtual environment in our home directory and install the dependencies we need.
Make sure you are running at least python 3.6, 3.5 is end of support.
Let's create the following folders and files.
Web Scraping Text Python Code
We created a
__main__.py
file, this lets us run the Newegg scraper with the following command (nothing should happen right now):Crawling the content
We need to write code that can crawl the content, by crawl I mean fetch or download the HTML from the target website. Our first target is Newegg, this website doesn't seem to require javascript for the data we need. We'll get into rendering javascript in a future post that covers headless scraping using requests-html on Google Places.
Open
core/crawler.py
which we created earlier. Now, we'll begin by requesting the HTML content from Newegg's domain.In
newegg/__main__.py
we can import crawler and the code above will execute.Remember you can execute and test your code with the previous python command in your terminal (must be run in the root folder
~/intro-web-scraping
).It looks like the request succeeded, the status code should of been printed to your terminal with a success of
200
.Let's clean up the code to make it reusable and define a function for returning the response text.In
core/crawler.py
we'll define a crawl_html
function (we want to reuse it and this lets us redefine where the HTML comes from in the future).In
newegg/__main__.py
we'll use the function, you can run it and see the HTML being printed. We use an uppercased variable NEWEGG_URL
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Scraping the data we need
Now that we have access to the HTML content from Newegg, we want a way to pull out stock information and price for the RTX 3080 and RTX 3090.Let's find the page from Newegg that has that information first.
Navigate to https://www.newegg.com/p/pl?N=100007709%20601357282 in your browser and you'll see we have filters applied for RTX 30 series.
We'll take that path and append it to our
NEWEGG_URL
. We do this using f-strings in python, which is a way to interpolate variables in strings.From this URL we can start scraping the data we need. Let's start by creating a few useful functions in the file
core/scraping.py
. These functions wrap lxml and handle some of the type conversions to make it easier for us to work with.Finding the data
We'll first try to get the prices with XPath. I highly recommend you use XPath instead of CSS selectors which is much more declarative and more expressive, you can use this simple cheat sheet for quickly finding out how to specify selectors. A more in-depth guide can be found from librarycarpentry.
Open your chrome browser and visit the crawl url we defined earlier: https://www.newegg.com/p/pl?N=100007709%20601357282.
Press F12 on your keyboard or open the developer console by right-clicking one of the prices on the page and selecting
inspect
.Using XPath
We'll use the inspector and practice our XPath to figure out how to get all prices on the page (there are 29 items listed). This selector:
//li[contains(@class, 'price-current')]
grabs all relevant prices.With the selector in hand, let's modify our
newegg/__main__.py
entry file by adding a new function to grab the prices.We should see output like the following.
Let's clean this extra HTML entity appearing at the end of our prices with a utility function. We'll make use of
re
for regex and unescape
from html module to cleanup our data. We need to check if the input contains numbers in order to account for the COMING SOON
labels. We'll keep this logic encapsulated in our get_rtx_prices
by mapping over each item and then converting it back to a list (map
returns an object iterator).Let's grab the item names.
Web Scraping Text Python Example
We also want the link to the item.
More complex XPath
Next we want the stock information (out of stock or in stock). To do this we need to add another function called
get_children_text
to core/scraper.py
. This will allow us to specify a parent selector and a child selector, which will return the first child that matches. If our parent selector has many matches it will try to find a matching child and if it does not find one it will return None
. In our case we have many parent matches but some of them may not contain the OUT OF STOCK
element.In
core/scraper.py
add the new function.Back in
newegg/__main__.py
we can add the stock selector.We also want the product id, having this can help us track changes to the product in the future. Here's how we can find the item id from the page.
If you notice on the highlighted lines below, you can see we added another function to our scraper. Because we are using the
text()
function of XPath, we are asking for the text node which ignores the other strong
label node in the tree seen in the screenshot above.Let's add
get_nodes
to our core/scraper.py
module.Our final output structure
Let's put it all together now to generate the final structure for our output which will contain basic stock information, price, product name, product id and product link.
This is what our
newegg/__main__.py
should look like now.Ommitted some of the results for readability, but the output should total 29 products as of this post.
Saving our data
With our data in hand, we can quickly save it for analysis later - it's not hard to imagine what else is possible when you have the data you want. We could monitor the price changes of these items, their stock status or when new items are added.
Let's add two csv utility functions to our
core/utils.py
file. We will write one to tansform our scraped output to proper csv lines and another to write the csv output.We can use it in our
newegg/__main__.py
file and just save the output we receive from get_rtx_items
. First import the utils at the top of the file.Now let's use our utility function at the bottom of our Newegg scraper to save the output and complete the full web scraping cycle - crawling, scraping and saving the output.
Checking the output
We can open the csv file to view the output which is saved in the folder we created at the beginning
~/intro-web-scraping
.Wrapping up
Web Scraping Text Python
From this guide we should have learned most of what I believe is the web scraping basics:
- Crawling content (using requests)
- Scraping relevant data (lxml and XPath)
- Saving the output (writing to a csv file)
Python Web Scraping Get Text
What we didn't cover:
- Headers
- Proxies (residential, data center, tor)
- Headless browsers
- Bot detection (fingerprinting)
- Throttling
- Captcha (recaptcha, image based input)
In a future post, we will scrape a website which requires javascript rendering and we'll make use of the requests-html python library to render the page and execute javascript.
Hopefully you'll find this post enlightening as web scraping has some really creative use cases that are not so obvious. Till next time!