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

Xu, Liang; Li, Linjian; Jiang, Zehua; Sun, Zaoyi; Wen, Xin; Shi, Juanjuan; Sun, Rui; Qian, Xiuying

doi:10.1109/access.2020.3009292

Cited by 16 publications

(12 citation statements)

References 68 publications

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“…We selected the HowNet sentiment dictionary as the general dictionary. The HowNet sentiment dictionary covers positive and negative evaluation words and emotional words [ 67 ] widely used in mainland China [ 67 , 71 ].…”

Section: Methodsmentioning

confidence: 99%

Public Attention and Sentiment toward Intimate Partner Violence Based on Weibo in China: A Text Mining Approach

Zeng

Tai

et al. 2022

Healthcare

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The mobile internet has resulted in intimate partner violence (IPV) events not being viewed as interpersonal and private issues. Such events become public events in the social network environment. IPV has become a public health issue of widespread concern. It is a challenge to obtain systematic and detailed data using questionnaires and interviews in traditional Chinese culture, because of face-saving and the victim’s shame factors. However, online comments about specific IPV events on social media provide rich data in understanding the public’s attitudes and emotions towards IPV. By applying text mining and sentiment analysis to the field of IPV, this study involved construction of a Chinese IPV sentiment dictionary and a complete research framework. We analyzed the trends of the Chinese public’s emotional evolution concerning IPV events from the perspectives of a time series as well as geographic space and social media. The results show that the anonymity of social networks and the guiding role of opinion leaders result in traditional cultural factors such as face-saving and family shame for IPV events being no longer applicable, leading to the spiral of an anti-silence effect. Meanwhile, in the process of public emotional communication, anger often overwhelms reason, and the spiral of silence remains in effect in social media. In addition, there are offensive words used in the IPV event texts that indicate misogyny in emotional, sexual, economic and psychological abuse. Fortunately, mainstream media, as crucial opinion leaders in the social network, can have a positive role in guiding public opinion, improving people’s ability to judge the validity of network information, and formulating people’s rational behaviour.

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Section: Methodsmentioning

confidence: 99%

Public Attention and Sentiment toward Intimate Partner Violence Based on Weibo in China: A Text Mining Approach

Zeng

Tai

et al. 2022

Healthcare

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“…At the same time, we combined UID to collect the original texts posted by these randomly selected Weibo users as the corpus. After excluding users with less than 50 original posts on Weibo (Xu et al, 2021 ), 7,415,252 original Weibo texts for corpus construction were finally obtained.…”

Section: Measurement Instrumentsmentioning

confidence: 99%

“…Referencing previous studies (Luo et al, 2015 ; Xu et al, 2021 ), this study recruited three volunteers (including a PhD student and two master students in psychology) to manually select 100 words that best describe the state of each category of emotional vocabulary in the ALO lexicon. For the unanimously selected words, these words were directly included into the seed word set of this category of emotion.…”

Section: Measurement Instrumentsmentioning

confidence: 99%

“…Some Chinese sentiment lexicons have accumulated an impressive amount of words in recent years, such as HowNet (Dong & Dong, 2003 ), Simplified Chinese-Linguistic Inquiry and Word Count (SC-LIWC; Cheng et al, 2017 ), Encoding Emotion in Chinese (Lin & Yao, 2016 ) and ALO. Among the rest of these lexicons, the ALO lexicon had high coverage and taxonomic classification that were widely used in Chinese sentiment analysis (An et al, 2021 ; Xu et al, 2008 , 2021 ). However, these sentiment analysis studies have operationalized emotions as the simple factor contrasting emotional texts vs. neutral texts, failing to consider quantifying emotions by combining emotional categories and emotional intensity (Buratto et al, 2014 ).…”

Section: Introductionmentioning

confidence: 99%

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

Pan

Han

et al. 2022

Curr Psychol

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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.

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“…As a hot research topic, Weibo search is also widely used by academic backgrounds and industries. e main content of the Weibo search is divided into two parts: extraction of Weibo features and research on Weibo topics [24]. e extraction of Weibo features refers to the analysis of the characteristics of Weibo social relations and the temporal and spatial characteristics of Weibo on the micro-meaning of Weibo text information, unifying the content of Weibo blogs, eliminating the semantic gap between media, and performing images and videos and unified extraction of multimedia information.…”

Section: Weibo Search Ranking and Topic Feature Mining Andmentioning

confidence: 99%

Security Analysis of Social Network Topic Mining Using Big Data and Optimized Deep Convolutional Neural Network

Tang

Zeng

et al. 2022

Computational Intelligence and Neuroscience

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This research aims to conduct topic mining and data analysis of social network security using social network big data. At present, the main problem is that users’ behavior on social networks may reveal their private data. The main contribution lies in the establishment of a network security topic detection model combining Convolutional Neural Network (CNN) and social network big data technology. Deep Convolution Neural Network (DCNN) is utilized to complete the analysis and search of social network security issues. The Long Short-Term Memory (LSTM) algorithm is used for the extraction of Weibo topic information in the memory wisdom. Experimental results show that the recognition accuracy of the constructed model can reach 96.17% after 120 iterations, which is at least 5.4% higher than other models. Additionally, the accuracy, recall, and F1 value of the intrusion detection model are 88.57%, 75.22%, and 72.05%, respectively. Compared with other algorithms, the model’s accuracy, recall, and F1 value are at least 3.1% higher than other models. In addition, the training time and testing time of the improved DCNN network security detection model are stabilized at 65.86 s and 27.90 s, respectively. The prediction time of the improved DCNN network security detection model is significantly shortened compared with that of the models proposed by other scholars. The experimental conclusion is that the improved DCNN has the characteristics of lower delay under deep learning. The model shows good performance for network data security transmission.

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

Cited by 16 publications

References 68 publications

Public Attention and Sentiment toward Intimate Partner Violence Based on Weibo in China: A Text Mining Approach

Public Attention and Sentiment toward Intimate Partner Violence Based on Weibo in China: A Text Mining Approach

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

Security Analysis of Social Network Topic Mining Using Big Data and Optimized Deep Convolutional Neural Network

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