Text Detergent: The Systematic Combination of Text Pre-processing Techniques for Social Media Sentiment Analysis

Zaki, Ummu Hani' Hair; Ibrahim, Roliana; Halim, Shahliza Abd; Kamsani, Izyan Izzati

doi:10.1007/978-3-030-98741-1_5

Cited by 4 publications

(1 citation statement)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Before the measurement of variables, we first cleaned the texts for subsequent analysis. The messages on social platforms are usually presented informally, which might influence the machine learning model performance [ 71 ]; thus, text preprocessing is an essential prerequisite before the model training [ 72 - 74 ]. To clean the data, the posts and comments were processed by removing the URLs as well as redundant punctuation and normalizing the whitespaces.…”

Section: Methodsmentioning

confidence: 99%

News Coverage of the COVID-19 Pandemic on Social Media and the Public’s Negative Emotions: Computational Study

Wang,

Li,

Ning

2024

J Med Internet Res

View full text Add to dashboard Cite

Background Social media has become an increasingly popular and critical tool for users to digest diverse information and express their perceptions and attitudes. While most studies endeavor to delineate the emotional responses of social media users, there is limited research exploring the factors associated with the emergence of emotions, particularly negative ones, during news consumption. Objective We aim to first depict the web coverage by news organizations on social media and then explore the crucial elements of news coverage that trigger the public’s negative emotions. Our findings can act as a reference for responsible parties and news organizations in times of crisis. Methods We collected 23,705 Facebook posts with 1,019,317 comments from the public pages of representative news organizations in Hong Kong. We used text mining techniques, such as topic models and Bidirectional Encoder Representations from Transformers, to analyze news components and public reactions. Beyond descriptive analysis, we used regression models to shed light on how news coverage on social media is associated with the public’s negative emotional responses. Results Our results suggest that occurrences of issues regarding pandemic situations, antipandemic measures, and supportive actions are likely to reduce the public’s negative emotions, while comments on the posts mentioning the central government and the Government of Hong Kong reveal more negativeness. Negative and neutral media tones can alleviate the rage and interact with the subjects and issues in the news to affect users’ negative emotions. Post length is found to have a curvilinear relationship with users’ negative emotions. Conclusions This study sheds light on the impacts of various components of news coverage (issues, subjects, media tone, and length) on social media on the public’s negative emotions (anger, fear, and sadness). Our comprehensive analysis provides a reference framework for efficient crisis communication for similar pandemics at present or in the future. This research, although first extending the analysis between the components of news coverage and negative user emotions to the scenario of social media, echoes previous studies drawn from traditional media and its derivatives, such as web newspapers. Although the era of COVID-19 pandemic gradually brings down the curtain, the commonality of this research and previous studies also contributes to establishing a clearer territory in the field of health crises.

show abstract

Section: Methodsmentioning

confidence: 99%