
China Retail Investor Sentiment Analytics | Alternative Data | Social Media | China, Hong Kong, US stocks | Intra-day Update
# | stock_id |
figi |
pub_date |
relevant_type |
like_neg_sum |
fav_neg_sum |
retweet_neg_sum |
reply_neg_sum |
post_neg_sum |
uid_neg |
user_avg_barage_neg |
like_neu_sum |
fav_neu_sum |
retweet_neu_sum |
reply_neu_sum |
post_neu_sum |
uid_neu |
user_avg_barage_neu |
like_pos_sum |
fav_pos_sum |
retweet_pos_sum |
reply_pos_sum |
post_pos_sum |
uid_pos |
user_avg_barage_pos |
like_all_sum |
fav_all_sum |
retweet_all_sum |
reply_all_sum |
post_all_sum |
uid_all |
user_avg_barage_all |
senti_score_div |
senti_score_log |
senti_conform |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | xxxxxxxxxx | Xxxxxxxxx | xxxxxx | xxxxxxxxxx | Xxxxx | Xxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxxx | Xxxxx | xxxxxxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxx | Xxxxx | Xxxxxx | xxxxx | xxxxxxxx | xxxxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxxx | xxxxxx | Xxxxxxxxx | xxxxxx | Xxxxxxxxx | Xxxxxxxxx | xxxxxxxxxx | Xxxxxx |
2 | Xxxxx | xxxxxx | xxxxxxx | xxxxxxx | Xxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxxx | xxxxxx | Xxxxx | Xxxxxxx | xxxxxx | Xxxxxxxx | Xxxxxxx | Xxxxx | xxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxxxxx | xxxxxxxx | Xxxxxxxxxx | Xxxxxxxx | Xxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | xxxxx | xxxxxxx | xxxxxxxxx | Xxxxxx | Xxxxxxx |
3 | Xxxxxxxxx | xxxxxxxxx | xxxxxxxxx | Xxxxx | xxxxxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxx | Xxxxxxx | xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxx | xxxxxx | Xxxxxxxxx | xxxxx | Xxxxxxxxxx | xxxxxx | xxxxx | xxxxxxxx | Xxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxx | xxxxx |
4 | xxxxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxx | Xxxxxxxx | Xxxxxxx | xxxxx | xxxxxxxx | xxxxxxxxxx | Xxxxxx | xxxxxxxxx | Xxxxx | xxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxxxx | xxxxxxx | Xxxxxx | xxxxxxxxx | xxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxxx | xxxxxxxx | Xxxxxxxx | Xxxxxxxx | xxxxxxxx | xxxxxxxxx | Xxxxxxx |
5 | Xxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxxxxxx | xxxxxxxxxx | xxxxxx | Xxxxx | Xxxxxxx | Xxxxx | Xxxxxx | Xxxxx | Xxxxxxxxx | xxxxxx | xxxxxxxx | Xxxxxxxxx | Xxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxx | Xxxxxxx | xxxxxxxxx | Xxxxx | xxxxx | Xxxxxx | xxxxxxxxx | xxxxxxx | xxxxxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxx | Xxxxx | Xxxxxxxxx | xxxxxxxxxx | xxxxxx | xxxxxxxxx |
6 | xxxxxxx | Xxxxxxx | Xxxxxxxxxx | Xxxxxxxxxx | Xxxxxxxx | Xxxxxxxxx | xxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxxxxxx | Xxxxxxxx | xxxxxxxxxx | xxxxxxx | Xxxxxxxx | xxxxx | Xxxxxx | xxxxxx | xxxxxxxx | xxxxxxx | Xxxxx | Xxxxxxxxx | Xxxxx | Xxxxxxx | Xxxxxxxx | xxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxx | xxxxxxxxx | xxxxxxx | xxxxxxxxxx | xxxxxx | xxxxx | Xxxxxxxxxx |
7 | Xxxxxxxxx | xxxxxxx | Xxxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxx | xxxxxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxx | Xxxxx | xxxxxxx | xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | xxxxxxx | Xxxxxxxx | xxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxx | Xxxxxxxx | Xxxxxxx | xxxxx | xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxxxxxx | xxxxxxx | Xxxxx | xxxxxxxxx | xxxxxxxx | Xxxxxxxx | xxxxxxxx |
8 | Xxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxx | Xxxxxxxxxx | Xxxxxxx | Xxxxxx | Xxxxxxxxxx | xxxxxxxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxx | Xxxxx | Xxxxx | Xxxxxxx | xxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxx | xxxxxx | xxxxxxxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxxxxx | Xxxxx | xxxxxxx | Xxxxxx | Xxxxx | xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | Xxxxxxxxx | Xxxxxxxx | xxxxxxxxx | Xxxxxxx |
9 | Xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxxxxx | Xxxxxxx | Xxxxxxx | xxxxxxxx | xxxxx | Xxxxx | Xxxxxxxx | xxxxxxxx | Xxxxxxxxx | xxxxxxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxxxxxx | Xxxxxxx | Xxxxxxx | Xxxxxxx | Xxxxxxx | xxxxxxx | Xxxxxxxxxx | xxxxxxxx | Xxxxx | xxxxxxxxxx | xxxxxxxxxx | xxxxxx | xxxxxxxx | Xxxxxxxx | xxxxxx | xxxxxxxx | xxxxxx | Xxxxxxxx | xxxxxxxxx | xxxxx |
10 | Xxxxxxxxxx | Xxxxxxxxx | Xxxxxxx | xxxxxxxx | Xxxxxxx | Xxxxxxxxxx | Xxxxxxxxx | xxxxxxxxxx | xxxxxxx | Xxxxxxxxx | xxxxxxxxx | xxxxxxxx | xxxxxxxxx | xxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxxx | xxxxxxx | Xxxxxxxxxx | Xxxxxxxxx | Xxxxxx | xxxxxxxx | xxxxxxxxxx | xxxxxxxx | Xxxxx | xxxxxxxx | xxxxxxxxxx | xxxxxxxx | Xxxxx | xxxxxxxx | xxxxxx | Xxxxxxxx |
... | xxxxxxxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxx | Xxxxx | Xxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxx | xxxxx | Xxxxx | Xxxxxxxxxx | Xxxxxxx | Xxxxxxxxxx | Xxxxxxxx | xxxxxxx | xxxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxxxxxxx | xxxxx | xxxxxxx | xxxxxxxxx | xxxxxxxxx | xxxxxx | xxxxxx | Xxxxxxxxxx |
# | stock_id |
figi |
pub_date |
read_neg_sum |
reply_neg_sum |
post_neg_sum |
user_neg |
user_avg_bar_age_neg |
read_neu_sum |
reply_neu_sum |
post_neu_sum |
user_neu |
user_avg_bar_age_neu |
read_pos_sum |
reply_pos_sum |
post_pos_sum |
user_pos |
user_avg_bar_age_pos |
read_all_sum |
reply_all_sum |
post_all_sum |
user_all |
user_avg_bar_age_all |
senti_score_div |
senti_score_log |
senti_conform |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | xxxxxxxxxx | Xxxxxxxxx | xxxxxx | xxxxxxxxxx | Xxxxx | Xxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxxx | Xxxxx | xxxxxxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxx | Xxxxx | Xxxxxx | xxxxx | xxxxxxxx | xxxxxxx | Xxxxx |
2 | Xxxxxxxx | xxxxxxxxxx | xxxxxx | Xxxxxxxxx | xxxxxx | Xxxxxxxxx | Xxxxxxxxx | xxxxxxxxxx | Xxxxxx | Xxxxx | xxxxxx | xxxxxxx | xxxxxxx | Xxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxxx | xxxxxx | Xxxxx | Xxxxxxx | xxxxxx | Xxxxxxxx | Xxxxxxx | Xxxxx | xxxxxx | xxxxxxxxxx |
3 | Xxxxx | xxxxxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxxxxx | xxxxxxxx | Xxxxxxxxxx | Xxxxxxxx | Xxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | xxxxx | xxxxxxx | xxxxxxxxx | Xxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxxxxx | xxxxxxxxx | Xxxxx | xxxxxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxx |
4 | xxxxx | Xxxxxxx | xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxx | xxxxxx | Xxxxxxxxx | xxxxx | Xxxxxxxxxx | xxxxxx | xxxxx | xxxxxxxx | Xxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxx |
5 | xxxxx | xxxxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxx | Xxxxxxxx | Xxxxxxx | xxxxx | xxxxxxxx | xxxxxxxxxx | Xxxxxx | xxxxxxxxx | Xxxxx | xxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxxxx | xxxxxxx | Xxxxxx | xxxxxxxxx |
6 | xxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxxx | xxxxxxxx | Xxxxxxxx | Xxxxxxxx | xxxxxxxx | xxxxxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxxxxxx | xxxxxxxxxx | xxxxxx | Xxxxx | Xxxxxxx | Xxxxx | Xxxxxx | Xxxxx | Xxxxxxxxx | xxxxxx | xxxxxxxx | Xxxxxxxxx | Xxxxxx |
7 | Xxxxxxxxxx | Xxxxxx | Xxxxx | Xxxxxxx | xxxxxxxxx | Xxxxx | xxxxx | Xxxxxx | xxxxxxxxx | xxxxxxx | xxxxxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxx | Xxxxx | Xxxxxxxxx | xxxxxxxxxx | xxxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxx | Xxxxxxxxxx | Xxxxxxxxxx | Xxxxxxxx | Xxxxxxxxx | xxxxx |
8 | Xxxxxxx | xxxxxxxxxx | Xxxxxxxxx | Xxxxxxxx | xxxxxxxxxx | xxxxxxx | Xxxxxxxx | xxxxx | Xxxxxx | xxxxxx | xxxxxxxx | xxxxxxx | Xxxxx | Xxxxxxxxx | Xxxxx | Xxxxxxx | Xxxxxxxx | xxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxx | xxxxxxxxx | xxxxxxx | xxxxxxxxxx | xxxxxx |
9 | xxxxx | Xxxxxxxxxx | Xxxxxxxxx | xxxxxxx | Xxxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxx | xxxxxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxx | Xxxxx | xxxxxxx | xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | xxxxxxx | Xxxxxxxx | xxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxx | Xxxxxxxx | Xxxxxxx |
10 | xxxxx | xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxxxxxx | xxxxxxx | Xxxxx | xxxxxxxxx | xxxxxxxx | Xxxxxxxx | xxxxxxxx | Xxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxx | Xxxxxxxxxx | Xxxxxxx | Xxxxxx | Xxxxxxxxxx | xxxxxxxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxx | Xxxxx | Xxxxx | Xxxxxxx |
... | xxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxx | xxxxxx | xxxxxxxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxxxxx | Xxxxx | xxxxxxx | Xxxxxx | Xxxxx | xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | Xxxxxxxxx | Xxxxxxxx | xxxxxxxxx | Xxxxxxx | Xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxxxxx | Xxxxxxx | Xxxxxxx |
Data Dictionary
Attribute | Type | Example | Mapping |
---|---|---|---|
stock_id
|
String | 000001.SZ | |
figi
|
String | BBG000BZDPV5 | |
pub_date
|
DateTime | 2024-04-01T00:00:00+00:00 | |
relevant_type
|
Integer | 1 | |
like_neg_sum
|
Integer | 0 | |
fav_neg_sum
|
Integer | 0 | |
retweet_neg_sum
|
Integer | 0 | |
reply_neg_sum
|
Integer | 1 | |
post_neg_sum
|
Integer | 6 | |
uid_neg
|
Integer | 6 | |
user_avg_barage_neg
|
Integer | 2587 | |
like_neu_sum
|
Integer | 33 | |
fav_neu_sum
|
Integer | 5 | |
retweet_neu_sum
|
Integer | 0 | |
reply_neu_sum
|
Integer | 16 | |
post_neu_sum
|
Integer | 14 | |
uid_neu
|
Integer | 13 | |
user_avg_barage_neu
|
Integer | 1370 | |
like_pos_sum
|
Integer | 307 | |
fav_pos_sum
|
Integer | 201 | |
retweet_pos_sum
|
Integer | 27 | |
reply_pos_sum
|
Integer | 167 | |
post_pos_sum
|
Integer | 20 | |
uid_pos
|
Integer | 16 | |
user_avg_barage_pos
|
Integer | 1894 | |
like_all_sum
|
Integer | 340 | |
fav_all_sum
|
Integer | 206 | |
retweet_all_sum
|
Integer | 27 | |
reply_all_sum
|
Integer | 184 | |
post_all_sum
|
Integer | 40 | |
uid_all
|
Integer | 35 | |
user_avg_barage_all
|
Integer | 1818 | |
senti_score_div
|
Float | 0.35 | |
senti_score_log
|
Float | 1.09861 | |
senti_conform
|
Float | 0.0632503 |
Attribute | Type | Example | Mapping |
---|---|---|---|
stock_id
|
String | 000001.SZ | |
figi
|
String | BBG000BZDPV5 | |
pub_date
|
DateTime | 2024-04-01T00:00:00+00:00 | |
read_neg_sum
|
Integer | 6491 | |
reply_neg_sum
|
Integer | 81 | |
post_neg_sum
|
Integer | 51 | |
user_neg
|
Integer | 40 | |
user_avg_bar_age_neg
|
Integer | 1580 | |
read_neu_sum
|
Integer | 1611 | |
reply_neu_sum
|
Integer | 8 | |
post_neu_sum
|
Integer | 6 | |
user_neu
|
Integer | 4 | |
user_avg_bar_age_neu
|
Integer | 657 | |
read_pos_sum
|
Integer | 5685 | |
reply_pos_sum
|
Integer | 40 | |
post_pos_sum
|
Integer | 25 | |
user_pos
|
Integer | 22 | |
user_avg_bar_age_pos
|
Integer | 1892 | |
read_all_sum
|
Integer | 13787 | |
reply_all_sum
|
Integer | 129 | |
post_all_sum
|
Integer | 82 | |
user_all
|
Integer | 54 | |
user_avg_bar_age_all
|
Integer | 1650 | |
senti_score_div
|
Float | -0.317073 | |
senti_score_log
|
Float | -0.693147 | |
senti_conform
|
Float | 0.0515989 |
Description
Country Coverage
History
Volume
5,000 | A-share |
2,900 | Hong Kong stocks |
300 | U.S. stocks |
Pricing
License | Starts at |
---|---|
One-off purchase | Not available |
Monthly License | Not available |
Yearly License | Available |
Usage-based | Not available |
Suitable Company Sizes
Delivery
Use Cases
Categories
Related Searches
Related Products
Frequently asked questions
What is China Retail Investor Sentiment Analytics Alternative Data Social Media China, Hong Kong, US stocks Intra-day Update?
Gain insight into popularity indicators and sentiment data of all A-share, Hong Kong stocks and popular US stocks, and understand the investment interests of China’s retail investors from Guba and Xueqiu, the popular stock forums in China.
What is China Retail Investor Sentiment Analytics Alternative Data Social Media China, Hong Kong, US stocks Intra-day Update used for?
This product has 3 key use cases. Datago Technology Limited recommends using the data for Trading, Systematic Trading, and Quantitative Investing. Global businesses and organizations buy Social Media Data from Datago Technology Limited to fuel their analytics and enrichment.
Who can use China Retail Investor Sentiment Analytics Alternative Data Social Media China, Hong Kong, US stocks Intra-day Update?
This product is best suited if you’re a Enterprise, Small Business, or Medium-sized Business looking for Social Media Data. Get in touch with Datago Technology Limited to see what their data can do for your business and find out which integrations they provide.
How far back does the data in China Retail Investor Sentiment Analytics Alternative Data Social Media China, Hong Kong, US stocks Intra-day Update go?
This product has 17 years of historical coverage. It can be delivered on a daily basis.
Which countries does China Retail Investor Sentiment Analytics Alternative Data Social Media China, Hong Kong, US stocks Intra-day Update cover?
This product includes data covering 3 countries like USA, China, and Hong Kong. Datago Technology Limited is headquartered in Hong Kong.
How much does China Retail Investor Sentiment Analytics Alternative Data Social Media China, Hong Kong, US stocks Intra-day Update cost?
Pricing information for China Retail Investor Sentiment Analytics Alternative Data Social Media China, Hong Kong, US stocks Intra-day Update is available by getting in contact with Datago Technology Limited. Connect with Datago Technology Limited to get a quote and arrange custom pricing models based on your data requirements.
How can I get China Retail Investor Sentiment Analytics Alternative Data Social Media China, Hong Kong, US stocks Intra-day Update?
Businesses can buy Social Media Data from Datago Technology Limited and get the data via S3 Bucket and SFTP. Depending on your data requirements and subscription budget, Datago Technology Limited can deliver this product in .json and .csv format.
What is the data quality of China Retail Investor Sentiment Analytics Alternative Data Social Media China, Hong Kong, US stocks Intra-day Update?
You can compare and assess the data quality of Datago Technology Limited using Datarade’s data marketplace.
What are similar products to China Retail Investor Sentiment Analytics Alternative Data Social Media China, Hong Kong, US stocks Intra-day Update?
This product has 3 related products. These alternatives include Retail Investor Sentiment Analytics-Australia (RISA-Australia) Social Media Data Daily Update Alternative Data 2300+ ASX stocks, Webautomation Social Media Data Reddit/ Twitter User Generate Content & Discussions GDPR Compliant, and Dataplex: Reddit Data Global Social Media Data 2.1M+ subreddits: trends, audience insights + more Ideal for Interest-Based Segmentation. You can compare the best Social Media Data providers and products via Datarade’s data marketplace and get the right data for your use case.