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Scholarly Works (2 results)

Thesis
Peer Reviewed

Part-Of-Speech Tag Embedding for Modeling Sentences and Documents

Yu, Dong Jin
Advisor(s): Wang, Wei

UCLA Electronic Theses and Dissertations (2016)

In sentence modeling, neural network approaches that leverage the tree-structural features of sentences have recently achieved state-of-the-art results. However, such approaches require complex architectures and are not easily extensible to document modeling. In this paper, we propose a very simple convolutional neural network model that incorporates Part-Of-Speech tag information (PCNN). While our model can be easily extensible to document modeling, it shows great performance on both sentence and document modeling tasks. As a result of sentiment analysis and question classification tasks, PCNN achieves the performance comparable to that of other more complex state-of-the-art models on sentence modeling and outperforms them on document modeling. We also make efforts to explore the effect of POS tag embeddings more thoroughly by conducting various experiments.

Cover page: Part-Of-Speech Tag Embedding for Modeling Sentences and Documents

Article
Peer Reviewed

Using Real-Time Social Media Technologies to Monitor Levels of Perceived Stress and Emotional State in College Students: A Web-Based Questionnaire Study

UCLA Previously Published Works (2017)

Background

College can be stressful for many freshmen as they cope with a variety of stressors. Excess stress can negatively affect both psychological and physical health. Thus, there is a need to find innovative and cost-effective strategies to help identify students experiencing high levels of stress to receive appropriate treatment. Social media use has been rapidly growing, and recent studies have reported that data from these technologies can be used for public health surveillance. Currently, no studies have examined whether Twitter data can be used to monitor stress level and emotional state among college students.

Objective

The primary objective of our study was to investigate whether students' perceived levels of stress were associated with the sentiment and emotions of their tweets. The secondary objective was to explore whether students' emotional state was associated with the sentiment and emotions of their tweets.

Methods

We recruited 181 first-year freshman students aged 18-20 years at University of California, Los Angeles. All participants were asked to complete a questionnaire that assessed their demographic characteristics, levels of stress, and emotional state for the last 7 days. All questionnaires were completed within a 48-hour period. All tweets posted by the participants from that week (November 2 to 8, 2015) were mined and manually categorized based on their sentiment (positive, negative, neutral) and emotion (anger, fear, love, happiness) expressed. Ordinal regressions were used to assess whether weekly levels of stress and emotional states were associated with the percentage of positive, neutral, negative, anger, fear, love, or happiness tweets.

Results

A total of 121 participants completed the survey and were included in our analysis. A total of 1879 tweets were analyzed. A higher level of weekly stress was significantly associated with a greater percentage of negative sentiment tweets (beta=1.7, SE 0.7; P=.02) and tweets containing emotions of fear (beta=2.4, SE 0.9; P=.01) and love (beta=3.6, SE 1.4; P=.01). A greater level of anger was negatively associated with the percentage of positive sentiment (beta=-1.6, SE 0.8; P=.05) and tweets related to the emotions of happiness (beta=-2.2, SE 0.9; P=.02). A greater level of fear was positively associated with the percentage of negative sentiment (beta=1.67, SE 0.7; P=.01), particularly a greater proportion of tweets related to the emotion of fear (beta=2.4, SE 0.8; P=.01). Participants who reported a greater level of love showed a smaller percentage of negative sentiment tweets (beta=-1.3, SE 0.7; P=0.05). Emotions of happiness were positively associated with the percentage of tweets related to the emotion of happiness (beta=-1.8, SE 0.8; P=.02) and negatively associated with percentage of negative sentiment tweets (beta=-1.7, SE 0.7; P=.02) and tweets related to the emotion of fear (beta=-2.8, SE 0.8; P=.01).

Conclusions

Sentiment and emotions expressed in the tweets have the potential to provide real-time monitoring of stress level and emotional well-being in college students.