‘ED Safe Space’–Deploy NLP Classifier on Android

ED Safe Space There are five Stages of Change that occur in the recovery process of eating disorders: Pre-Contemplation, Contemplation, Preparation, Action, and Maintenance (NEDA). Pre-Contemplation is the stage where individuals have no intention to change behavior in the foreseeable future. Many of them are unaware or under-aware of their problems. This stage is alsoContinue reading “‘ED Safe Space’–Deploy NLP Classifier on Android”

Deep NLP Classifiers — CNN vs. RNN

In this blog, we will train multiple deep learning NLP classifiers to predict which kind of forum a post most likely comes from — dieting, eating disorders, general health or irrelevant forums. These models are baseline prompts waiting for better labeled training data. Please click here to help: https://www.zooniverse.org/projects/joyqiu/edetectives. Transfer GloVe embeddings GloVe word embeddingContinue reading “Deep NLP Classifiers — CNN vs. RNN”

Time Series 3 — Granger Causality Test

Let’s revisit the two time series (Figure 1) — weekly post submissions to eating disorders forums(‘ed’) and weekly post submissions to dieting forums(‘diet’). Our task is to reveal relationships between these time series and conduct granger causality test. Stationarity Before using cross-correlation function to explore relationships between two time series or conducting any temporal causalityContinue reading “Time Series 3 — Granger Causality Test”

Sentiment Analysis on ED / Dieting posts

Does a post submitted to eating disorders forums have higher likelihood of containing negative feelings or attitudes from the writer? How about dieting forums? How are these attitudes different from other irrelevant topic forums? We can find answers by sentiment analysis on posts from all four forums. Sentiment analysis is the process using machine learning modelsContinue reading “Sentiment Analysis on ED / Dieting posts”

NLP (LSTM) — First Attempt

Deep neural network for text classification, particularly NLP(Nature Language Processing), lends itself as a useful tool in capturing ‘encoded’ features from a collection of text (corpus), and learning hidden relationships between words in a certain context. These features are encoded in numerical values that human brain is almost incapable to understand and interpret. LSTM, aContinue reading “NLP (LSTM) — First Attempt”

Time Series 2 — Detrending by Linear Regression

In clinical researches, eating disorders can take months or years to be acknowledged by individuals, and the recovery can take even longer for some patients to break the ‘vicious cycle’ between dieting and eating disorders. From the time series point of view, we will uncover how this ‘vicious cycle’ presents itself in terms of theContinue reading “Time Series 2 — Detrending by Linear Regression”

Text Mining – Comparison Word Cloud

‘Word Cloud’ is a popular and convenient tool for text mining visualizations, which unmasks the most important words in topic context or corpus. Comparison word cloud offers a neat format of visualization when we want to compare several groups of text, the similarity and variance in terms of frequently appeared words. For this project, aContinue reading “Text Mining – Comparison Word Cloud”

Time Series 1 — Basic Visualizations

We are curious about how the quantity of posts submitted to different subreddit forum groups changes over time — the trends, variances and comparisons. After plotting basic time series, we are surprised to notice a continuously significant increase in post submissions to eating disorders forums and dieting forums since 2016, even during the time periodContinue reading “Time Series 1 — Basic Visualizations”

CrowdSourced Labeling 2 — Zooniverse

Want to volunteer? Click here! https://www.zooniverse.org/projects/joyqiu/edetectives Considering the unnecessary burden raised to professionals (physicians, therapists, dietitians or psychiatrists) in the eating disorders fields, if we ask them to read hundreds of posts and manually screen positive ones, we find crowdsourced labeling can be a better way to collect more precisely labeled data. We use https://www.zooniverse.org/Continue reading “CrowdSourced Labeling 2 — Zooniverse”