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

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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...

Reddit text mining and visualization with R Shiny

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Text mining, unlike other fields of data mining, it requires expertise on many disciplines. To make meaningful conclusions, we'll need psychology, sociology, marketing, just to name a few. It can't be and shouldn't be finished by data scientists only. However, text mining is not accessible for most people. Take web data text mining for exam, there're three difficulties: 1. Collecting data is hard Though data are open and free online, there're often too big and messy to be downloaded by hand. It requires some programming and data management skills. 2. Data is dirty and messy Text data is made by people. People can make terrible data with strange spelling, grammar, format, encoding... Data cleaning on text data is a big challenge. 3. Fitting model & visualization is complicated Text data is unstructured. That makes modeling and visualizing complicated. Knowledge on both math and programming is required.  About this project In this project, ...