Emotion Analysis

For health, we have focused on emotion changes that people have experienced during this pandemic. Emotion changes have stemmed from various reasons such as unemployment, implementation of stay-at-home policies, fear of the virus, etc. We quantify emotion changes by using social media data, including Twitter and Instagram. Since the breakout of COVID-19, we have seen an increase in discussions tagged with hashtags such as #COVID-19 and #depression on Twitter and Instagram. We believe it is vital to analyze differences in. We also hope to analyze overall responses to the pandemic as well as changes in behavior due to the virus and generate reports on global situations regarding emotion changes. We plan to categorize the tweets and Instagram posts that mention COVID-19 by sentimental categories: sadness, depression, isolation, positive, etc. Further detailed analysis will also look into specific keywords and corresponding trends.

Research Topics

1. Emotion Analysis with Twitter More details

We applied a deep learning model (BERT) as well as a new model for sentiment analysis, categorizing each tweet into different sentiments; trust, fear, joy, sadness, anticipation, anger, disgust, and surprise.

Visit the pre-print version of initial results and analysis at https://arxiv.org/pdf/2004.10899.pdf.

2. Emotion with Instagram More details

We performed statistical analysis on posts related to mental health topics. More than 1.7 million posts with #mentalhealth have been posted on Instagram since January 2020. The number of posts increased in mid-March, when countries in Europe and North America issued stay-at-home orders, although the number of posts with #depression did not change.

Topic 1. Emotion Change Analysis with Twitter

Contributors: Irene Li, Tianxiao Li, Yixin Li

We applied deep learning methods for sentiment analysis, categorizing each tweet into different sentiments; trust, fear, joy, sadness, anticipation, anger, disgust, and surprise, etc.

Section 1:  Sentimental distribution on 8 categories

We applied a deep learning model (BERT) trained on 750 manually labeled cases to 1 million English tweets. Fear, Anger and Sadness ranked first. (A multilingual version will be available soon!)

Section 2: Sentiment trend among topics

We now look at the emotion trends on different topics. Using the same model from Section1, we analyzed two topics: “mask” and “lockdown”.  

Fig1: Emotion trend over the time about "mask"

Fig2: Emotion trend over the time about "lockdown"

Section 3: The reasons why people feel sad or fear.

To understand why people feel fear and sadness, we calculated correlation on the tweets categorized by fear and sadness, and then kept nouns and noun phrases with the help of Stanford Stanza tool. The first graph shows some founded keywords from English tweets. The second and third graph show multilingual keywords which contain English, Spanish, German, Portuguese, Japanese and Chinese based on different models (2nd is a lexicon-based method; 3rd is an attentional method). 

Image tool credit: https://worditout.com/word-cloud/create

Section 4: The topics on tweets.

We utilized the LDA(Latent Dirichlet allocation) topic modeling to analyze the topics on people’s tweets. Each “topic” learned by the model is a bunch of key words, then we manually labeled these topics as meaningful concepts. The following are five topics we learned.

Topic 0: Covid19 testing, deaths cases, positive caes

Topic 1: President Trump, government, federal affairs

Topic 2: lockdown, stay at home, physical distancing

Topic 3: (Spanish) pandemic, health conditions

Topic 4: the peak, serious treatment, Boris Johnson

We choose the data on April 7th, and first we do inference on all the data, and show the ratio for each topic learned above (All). Then we do inference on the tweets that are only labeled as sadness or fear (Sad and Fear). And the following is the ratio of each topic learned. In general, the public may be worried about Topic 3 and 2, mainly, the pandemic and lockdown, which are making people stressed. 

Topic Distribution on the data from April 7th (only English Tweets)

Topic 2. Emotion Change Analysis with Instagram

Contributors: Keita Suzuki, Hiroki Kanezashi, and Naomi Nakagawa 

We collected 71,737 posts with #depression from March 31 to April 5, 2020. During this period, the number of the posts are steadily increasing as below. Among the posts with location information, #depression was mostly posted in the U.S., the U.K. and India. In those countries, users posted the hashtag during the local daytime. During the long quarantine, people might struggle to keep their mental conditions healthy and the trend of #depression on Instagram might reflects their attitudes.