Posts

Showing posts with the label Reddit

Sentiment Analysis with Deep Learning and Traditional Approaches: An Ensemble Modeling Example

Image
image downloaded from: https://beyondphilosophy.com/a-sentiment-analysis-of-sentiment-analysis-some-gobbledygook-some-bright-spots-and-a-nice-looking-dashboard/ In this article, we w ill use a simple text classification dataset to demonstrate how sentiment analysis can be done with both traditional text mining approaches and deep learning approaches. We will also compare the performance of the two modeling strategies and develop an ensemble model that maximizes prediction accuracy. The data is cited from de Freitas, Nando, and Misha Denil. "From Group to Individual Labels using Deep Features." (2015).  We will cover: Develop a LSTM deep learning model Sentiment analysis with polarity scores  Comparison and ensemble modeling Before we start, let's take a look at the data. The data contains 3,000 reviews labeled with positive and negative sentiments extracted from Amazon, IMDb, and Yelp.  The head of the data looks like this: So there is no way for m...
Post a Comment
Keep reading

Crawl Reddit.com with Python scrapy

Image
Nowadays, there's a lot of information on the internet. Yet when they separate among web pages, there's no way analysis can be done. In this article, we will visit Reddit.com, download posts, parse information and store in a database with Python scrapy. The steps are: 1. Visiting URL This includes finding the specific URL that your desired information is in, and interact with the website's server by GET or POST. In practice, it can be sending usernames and password, setting cookies and so on. At the end of this step, you'll get a html file with all the contents. 2. Parsing information In the previous step, we have a html file, we can simply store it as a text file. But this is not too helpful for further analysis as there're irrelevant contents. We want to extract information. To do this, we need some background knowledge on HTML and CSS. Identify in which HTML element the information is contained and parse it out! 3. Store information in da...
Post a Comment
Keep reading