By analyzing microblog data such as Weibo, the government can better understand the public''s sentiments about COVID-19 epidemic events, discover sensitive information, grasp the hot
To perform sentiment analysis, this study categorized the four emotions, good, joy, surprise, and anticipation, as positive sentiments, and categorized the other four emotions,
Weibo text was coded from four aspects: correlation, state, sentiment and topic. Each Weibo post was marked with a code consisting of four dimensions (r, s, o, t). Correlation(r): Determine
State Grid International Limited · 工作经历: State Grid International Development Limited · 教育经历: Peking University · 地点: 北京市 · 76 位领英好友。在领英 (一个拥有 10 亿会员的职业社
This technique was applied to uncover attitudes and emotions expressed by Chinese and Western publics about the Covid-19 epidemic on Twitter and Weibo (RQ1). Twitter and Weibo data were respectively analyzed
In other words, the Weibo semantic network suggests that the narrative most likely to circulate on Weibo was that relating to the efforts of the Chinese government''s ''prevention and control'' of the epidemic, ''experts''
Public opinion is the abbreviation of public ideas, which is the sum of the beliefs, attitudes, opinions and emotions expressed by the masses about various phenomena in the
Weibo posts not closely correlated with the topic of the COVID-19 vaccines were filtered out using machine learning. to the negative sentimental tendency contained in this
To perform sentiment analysis, this study categorized the four emotions, good, joy, surprise, and anticipation, as positive sentiments, and categorized the other four emotions, fear, disgust, sadness, and anger, as
The findings of this study bring some practical implications. The majority of Weibo users are unverified users, while the information diffused on Weibo is controlled by a small group of Blue V and Orange V accounts. We
As Weibo data is non-structured, the Weibo data collected in this paper was coded, which is the basic task of data analysis. Weibo text was coded from four aspects: correlation, state,
Covid-19 out broke gave an extreme impact to the globe, imposing a challenge to health publicly and causing social interruptions. As a result, the role of mainstream media in
Yue Su et al. used Twitter and Sina Weibo data to evaluate the changes in the mental state of residents as expressed through social media, both before and after city closure
Most of the Weibo posts showing positive sentiment revolved around support, understanding and praise for the Chinese government’s epidemic response measures, as well as praise for some heroic characters, such as Zhong Nanshan, a Chinese pulmonologist who played an important role during the epidemic.
The Weibo engagements, including repost, comment, and like, were summarized for the three account types and seven emotional messages. Unverified users received fewer reposts, comments, and likes than verified users. Although Blue V received more reposts, they had fewer comments than Orange V (see Table 3).
Therefore, we gathered data on daily Weibo texts from residents during the lockdown periods in two critical cities throughout China’s COVID-19 policy cycle, using natural language processing (NLP) methods to assign sentiment scores to each text as a proxy for daily resident sentiment.
Chinese mainstream news organizations, such as People’s Daily, endorsed the response measures adopted by the Chinese government and further foregrounded them on Weibo.
The topic of category 1 is summarized and headed as “Chinese New Year”. The main content of Weibo posts includes key phrases such as “New Year” and “going home”. Of interest is that the most popular word in the word cloud is “hope”.
When extracting the sentiment characteristics of each Weibo, the cleaned text data is used as input, and the words in the emotion dictionary are used for matching, and the number of keywords in the seven emotion categories of the Weibo is counted to form the Weibo text emotional characteristics.
The European energy storage market is booming with Germany leading residential adoption (+58% YoY) thanks to €500/kWh subsidies. Italy's new tax credits drive 5.2GWh commercial deployments, while UK grid-scale projects exceed 8GWh with 2-hour duration systems. Key selection criteria: German-certified safety (VDE-AR-E 2510), 10+ year warranties, and VPP readiness. Top-performing products include Sonnen's hybrid inverters (98% efficiency) and BYD's Blade Battery (12,000 cycles @80% DoD). For snowy regions like Scandinavia, consider Huawei's -30°C compatible systems. France mandates carbon footprint declarations - Sungrow's ISO-14067 certified solutions gain preference.
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