Revealing the differences of urban parks’ services to human wellbeing based upon social media data

The “tragedy paradox” of music, avoiding experiencing negative emotions but enjoying the sadness portrayed in music, has attracted a great deal of academic attention in recent decades. Combining experimental psychology research methods and machine learning techniques, this study (a) investigated the effects of gender and Big Five personality factors on the preference for sad music in the Chinese social environment and (b) constructed sad music preference prediction models using audio features and individual features as inputs. Statistical analysis found that males have a greater preference for sad music than females do, and that gender and the extraversion factor are involved in significant two-way interactions. The best-performing random forest regression shows a low predictive effect on the preference for sad music ( $R^{2} =0.138$ ), providing references for music recommendation systems. Finally, the importance-based model interpretation feature reveals that, in addition to the same music inputs (audio features), the perceived relaxation and happiness of music play an important role in the prediction of sad music preferences.

/pdf/predicting-the-preference-for-sad-music-the-role-of-gender-3d8qs35ymm.pdf

Predicting the Preference for Sad Music: The Role of Gender, Personality, and Audio Features

Using Social Media Text Data to Analyze the Characteristics and Influencing Factors of Daily Urban Green Space Usage—A Case Study of Xiamen, China

The outbreak of COVID-19 has led to a global health crisis and caused huge emotional swings. However, the positive emotional expressions, like self-confidence, optimism, and praise, that appear in Chinese social networks are rarely explored by researchers. This study aims to analyze the characteristics of netizens' positive energy expressions and the impact of node events on public emotional expression during the COVID-19 pandemic. First, a total of 6,525,249 Chinese texts posted by Sina Weibo users were randomly selected through textual data cleaning and word segmentation for corpus construction. A fine-grained sentiment lexicon that contained POSITIVE ENERGY was built using Word2Vec technology; this lexicon was later used to conduct sentiment category analysis on original posts. Next, through manual labeling and multi-classification machine learning model construction, four mainstream machine learning algorithms were selected to train the emotional intensity model. Finally, the lexicon and optimized emotional intensity model were used to analyze the emotional expressions of Chinese netizens. The results show that POSITIVE ENERGY expression accounted for 40.97% during the COVID-19 pandemic. Over the course of time, POSITIVE ENERGY emotions were displayed at the highest levels and SURPRISES the lowest. The analysis results of the node events showed after the outbreak was confirmed officially, the expressions of POSITIVE ENERGY and FEAR increased simultaneously. After the initial victory in pandemic prevention and control, the expression of POSITIVE ENERGY and SAD reached a peak, while the increase of SAD was the most prominent. The fine-grained sentiment lexicon, which includes a POSITIVE ENERGY category, demonstrated reliable algorithm performance and can be used for sentiment classification of Chinese Internet context. We also found many POSITIVE ENERGY expressions in Chinese online social platforms which are proven to be significantly affected by nod events of different nature. 

/pdf/the-positive-energy-of-netizens-development-and-application-170sc9rd.pdf

The positive energy of netizens: development and application of fine-grained sentiment lexicon and emotional intensity model

Personality Differences between Children and Adults over the Past Two Centuries: Evidence from Corpus Linguistics

As one of the most popular social media platforms in China, Weibo has aggregated huge numbers of texts containing people’s thoughts, feelings, and experiences. Analyzing emotions expressed on Weibo has attracted a great deal of academic attention. Emotion lexicon is a vital foundation of sentiment analysis, but the existing lexicons still have defects such as a limited variety of emotions, poor cross-scenario adaptability, and confusing written and online expressions and words. By combining grounded theory and semi-automatic methods, we built a Weibo-based emotion lexicon for sentiment analysis. We first took a bottom-up approach to derive a theoretical model for emotions expressed on Weibo, and the substantive coding led to eight core emotion categories: joy, expectation, love, anger, anxiety, disgust, sadness, and surprise. Second, we built a new emotion lexicon containing 2,964 words by manually selecting seed words, constructing a word vector model to expand words, and making rules to filter words. Finally, we tested the effectiveness of our lexicon by using a lexicon-based approach to recognize the emotions expressed in Weibo text. The results showed that our lexicon performed better in Weibo emotion recognition than five other Chinese emotion lexicons. This study proposed a method to construct an emotion lexicon that considered both theory and application by combining qualitative research and artificial intelligence methods. Our work also provided a reference for future research in the field of social media sentiment analysis.

/pdf/a-novel-emotion-lexicon-for-chinese-emotional-expression-41d3zl3nx5.pdf

A Novel Emotion Lexicon for Chinese Emotional Expression Analysis on Weibo: Using Grounded Theory and Semi-Automatic Methods

The internet’s convenience and anonymity have facilitated different types of covert fraud, resulting in economic, mental, and social harm to victims. Understanding why people are deceived and implementing appropriate interventions is critical for fraud reduction. Based on the Bayesian brain theory, individuals’ mental states may be a key point in scam compliance and warning compliance. Fraud victims with different mental states may construct various hypotheses and explanations about the fraud they are exposed to, causing different cognition and behavior patterns. Therefore, we first conducted a semi-structured in-depth interview with online fraud victims to investigate the individual and social factors that affect victims’ mental states. Grounded theory analysis showed five core factors influencing scam compliance: psychological traits, empirical factors, motivation, cognitive biases, and emotional imbalance. Based on our findings of psychological processes and deception’s influential factors, we then designed warnings to inform victims of fraud, particularly for those involving novel types of scams. Tested on a real-life setting, our designed warnings effectively enhanced warning compliance, allowing more fraud victims to avoid financial losses.

/pdf/mental-states-a-key-point-in-scam-compliance-and-warning-g5577d7h.pdf

Mental States: A Key Point in Scam Compliance and Warning Compliance in Real Life

The effect of signal icon and persuasion strategy on warning design in online fraud

Music emotion information is widely used in music information retrieval, music recommendation, music therapy, and so forth. In the field of music emotion recognition (MER), computer scientists extr...

Effects of individual factors on perceived emotion and felt emotion of music: Based on machine learning methods:

Psychological predictors of online fraud victimhood in China: a machine learning approach

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Jiaming Shi

Author Tools

Chat about Author