Tweets_df.dropna(axis='columns', inplace=True) Tweets_df = pd.read_csv(‘data/health_tweets.csv’) We will drop these and see what columns remain with actual values for all tweets. The original scraped data provided from Twint has a lot of columns, a handful of which contain null or NaN values. To begin, we will load in our scraped tweets into a data frame. Removing unnecessary columns and duplicate tweets Plus, if we’d like to retain hashtags because they may serve us another analytical purpose, we will see shortly that there is already a hashtags column in our raw data that holds them all in a list. #HarvardHealth 'įor instance, the hashtags, links, and handle references above may not be necessary for our topic modeling approach since those terms don’t really provide meaningful context for discovering inherent topics from the tweet. ‘How to not practice emotional distancing during social distancing.
There’s a lot of noise in a tweet that we might not need or want depending on our objective(s): This step is important because raw tweets without preprocessing are highly unstructured and contain redundant and often problematic information. This article will focus on preprocessing the raw tweets. The csv is provided here for your reference to follow along if you are just joining us in part 2. Nothing has been removed or changed from the format of the data that was given to us by the scraper. In our previous article, we had scraped tweets from Twitter using Twint and merged all the raw data into a single csv file. A reader interested in having a more thorough and statistical understanding of LDA is encouraged to check out these great articles and resources here and here.Īs a pre-requisite, be sure that Jupyter Notebook, Python, & Git are installed on your computer.Īlright, let’s continue! PART 2: Cleaning and Preprocessing Tweets These articles will not dive into the details of LDA or STTM but rather explain their intuition and the key concepts to know. Part 3: Applying Short Text Topic Modeling Part 2: Cleaning and Preprocessing Tweets I will cover all the topics in the following 4 articles in order: It will be a combination of data scraping/cleaning, programming, data visualization, and machine learning.
#Clean text with gensim how to#
This series of posts are designed to show and explain how to use Python to perform and apply a specific STTM approach ( Gibbs Sampling Dirichlet Mixture Model or GSDMM) to health tweets from Twitter. This is where more recent short text topic modeling (STTM) approaches, some that build upon LDA, come in handy and perform better! Major News Sources with Health - Specific Twitter Accounts ( Image by author)
However, one shortcoming of LDA is that it doesn’t work well with shorter texts such as tweets. A typical use of LDA (and topic modeling in general) is applying it to a collection of news articles to identify common themes or topics such as science, politics, finance, etc. One of the most popular topic modeling approaches is Latent Dirichlet Allocation (LDA) which is a generative probabilistic model algorithm that uncovers latent variables that govern the semantics of a document, these variables representing abstract topics. In other words, the model does not know what the topics are when it sees the data but rather produces them using statistical relationships between the words across all documents. Its real strength is that you don’t need labeled or annotated data but instead it accepts the raw text data as input only, and hence why it is unsupervised. Topic modeling is an unsupervised machine learning approach with the goal to find the “hidden” topics (or clusters) inside a collection of textual documents (a corpus). We do not encourage anyone to scrape websites, especially those web properties that may have terms and conditions against such actions. Web Scraping, Programming, Natural Language Processing Multi-part series showing how to scrape, preprocess and apply & visualize short text topic modeling for any collection of tweetsĭisclaimer: This article is only for educational purposes. Last Updated on Januby Editorial Team Author(s): John Bica