![]() ![]() Stopword - a word that is automatically omitted from a computer-generated concordance or index. What are stopwords? If we just ask a search engine, we should receive an answer for a dictionary: Last but not least, we’re going to remove the stopwords. If your dataset is very large, you should check Stack Overflow for a better solution. The above approach is an easy-to-understand function, but it isn’t the most efficient method. We’re going to use this method more frequently because it’s faster and easier. Notice how we’ve imported a list of punctuation signs from "string" package instead of creating a list and filling it manually with all those signs. # create function for punctuation removal:ĭf = df.apply(remove_punctuations) Now let’s move on to removing the punctuation we’ll create a simple function and apply it to every "title" cell: import string Lowercase, Punctuation, and Stopwordsīefore we move on to some cleaning duties, let’s establish a simple fact: 'ebay' = 'Ebay'Īs demonstrated above, Python is a case-sensitive language - "e" is not the same as "E." That is why one of the first steps in cleaning string data is to convert all the words to lowercase: df = df.str.lower() So let’s try to group those posts by their content. We know that the number of different projects is closer to 20 or 30. We get 1,102 results, because there are 1,102 different titles. What’s the easiest way to get at least some amount of attention? Think of an interesting and original title! So now when someone wants to group all the posts by their titles. We’re all guilty: we want to publish our project and gain attention. ![]() Part 1: The Title Problem - Everybody Wants a Different Title If you have any questions, feel free to reach out and ask me anything: Dataquest, LinkedIn. All the files are already within that folder, so if you want to play around with the data without scraping it, you can just download the dataset. You can find this project’s folder on my Github. Instead of sentiment analysis, we’re more interested in what technical remarks are most common. We’re specifically interested in the technical advice regarding our projects. Our main goal is to understand what feedback is being provided. We’ll use all of the techniques mentioned above. We’ll use various NLP techniques to analyze the content of the feedback: Next, we’ll process and analyze the feedback posts. We’re mostly going to show the potential and quickly move on. We’ll start small: cleaning and organizing the title data, then we’ll perform some data analysis for each title’s numeric information (views, replies). In this post, we’ll clean and analyze the text data. We also scraped the post’s website - specifically, we targeted the first reply to the post.We extracted the title, link to the post, number of replies, and number of views of each post.We gathered the data from Dataquest’s forum pages and organized it in a pandas DataFrame: In the first post, we learned how to perform web scraping using Beautiful Soup. We’re interested in the content of those opinions. After publishing a project, other learners or staff members can share their opinions of the project. ![]() Dataquest encourages learners to publish their guided projects on their forum. The main purpose of this post is to analyze the responses that learners are receiving on the Dataquest Community. It’s worth familiarizing yourself with those concepts before you continue. We’re also going to write a few functions and import a lot of packages and tools. To really benefit from this NLP article, you should read the first post, understand how to use pandas to work with text data, and be aware of list comprehensions and lambda functions. This is the second in a series of posts describing my natural language processing (NLP) project. JanuNLP Project Part 2: How to Clean and Prepare Data for Analysis ![]()
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