Building consumer confidence index based on social media big data

Abstract

This study takes microblog texts as the original data, builds Consumer Confidence Index (CCI) user dictionary, and calculates the CCI through different experimental conditions of the target words' frequency analysis. We explored the better way between “exact matching” and “fuzzy matching,” and tested the necessity of degree words and negative words. The main findings are as follows: (a) The numbers of principal components extracted in the six different matching methods and weight settings were similar, which were nine or 10 principal components, and the explanatory variance of composition was above 96%; (b) Comprehensively considered, the principle of “fuzzy matching target word + degree word” was the best calculation method; (c) When the validation set data to predict the CCI in next month which based on the optimal method was used, the difference between the index estimate and the official value was within 10%. To a certain degree, CCI based on microblog can be on behalf of consumers' subjective feelings as well as attitudes towards China's current and future economic situations.

The Method of How to Predict Weibo Users’ Recovery Experience on the Weekend Based on Weibo Big Data

“…Step1: Segmentation of Weibo Data According to Wang [43], it is the basis of further target word frequency counts to split Weibo text by punctuation. This study adopted the method proposed by Wang [42], and use ",", ".…”

“…F1, F2, ……, and Fn of Weibo fragments were matched to the corresponding files of each target word in the work recovery dictionary through fuzzy matching [43]. For example, the sentence "今天工作忒累了, 下了班一定要好 好休息一下。" (I am so tired from work today; I must have a good rest after work.)…”

“…Since the appearance of the degree adverb may increase or decrease the emotional tendency expressed by the target word, we adopted the Chinese Degree Words used by wang et al [43,44] in this study. In the example sentence of "今天 工作忒累了, 下了班一定要好好休息一下"(I am so tired from work today, so I must have a good rest after work), the adverb "忒" (too) appeared in F1 once.…”