five

Instagram Profiles & Posts & Comments Datasets|社交媒体分析数据集|市场研究数据集

收藏
Snowflake2024-04-08 更新2024-05-01 收录
社交媒体分析
市场研究
下载链接:
https://app.snowflake.com/marketplace/listing/GZT0Z4C8RF3H3
下载链接
链接失效反馈
资源简介:
We provide 3 types of Instagram datasets: 1) Instagram - Profiles Dataset: Extracts business and non-business information from complete public profiles. Allows filtering by hashtags, followers, account type, or engagement score. Useful for sentiment analysis, brand monitoring, and influencer marketing. Key Features: -Username or handle of the Instagram account. -Unique identifier for the Instagram account. -Number of followers for the account. -Total count of posts made by the account. -Indicates whether the account is a business or professional account. -Indicates whether the account is verified. -Average engagement rate. -External URLs linked in the profile. And more! Use Case: -Influencer Marketing: Identify influencers based on follower count, engagement rate, and account verification status. -Market Research: Analyze competitor profiles to understand their audience demographics and engagement strategies. -Brand Monitoring: Track mentions of brand hashtags or external URLs to gauge brand visibility and sentiment. 2) Instagram - Posts Dataset: Contains information on posts including URL, user, description, hashtags, comments, date posted, likes, attached media, and location. Useful for analyzing user engagement, content trends, and audience interactions. Key Features: -Post URL. -Username of the post creator. -Post text description. -Hashtags used in the post. -Number of comments. -Post publication date. -Number of likes. -URLs of attached photos/videos. -Geographical location. Use Case: -Content Strategy Optimization: Analyze popular hashtags and content types to inform content creation strategies. -Audience Engagement: Identify high-engagement posts to understand audience preferences and optimize engagement tactics. -Trend Analysis: Track trends over time by analyzing post frequency and engagement metrics. 3) Instagram - Comments Dataset: Comprises commenter username, comment date, content, likes received, number of replies, reply content, hashtags, tagged users, post URL, post user, comment ID, and post ID. Valuable for analyzing user interactions and engagement on posts. Key Features: -Commenter username. -Comment date. -Comment content. -Number of likes on the comment. -Number of replies on the comment. -URLs of attached photos/videos. -Geographical location. -Hashtags used in the comment. -URLs of post and commenter profile. Use Case: -Community Management: Monitor comments to address customer inquiries, feedback, or complaints. -Sentiment Analysis: Analyze comments to gauge audience sentiment towards specific topics or brands. -Influencer Identification: Identify users who frequently engage with influencer posts, potentially indicating brand advocates or collaborators. Why Use Our Datasets? 1.Comprehensive Insights: Combine Instagram Profiles, Posts, and Comments datasets for a full view of activity. 2.Advanced Analytics Tools: Each dataset offers powerful analytics for deep market analysis. 3.Content Strategy Optimization: Analyze performance, hashtags, and engagement to refine content strategies. 4.Audience Understanding: Gain insights into audience preferences and demographics for targeted marketing. 5.Enhanced Brand Visibility: Leverage insights to boost brand visibility and connect with followers. Note: This is a sample - for all datasets, please contact us.
提供机构:
Bright Data
创建时间:
2024-04-08
用户留言
有没有相关的论文或文献参考?
这个数据集是基于什么背景创建的?
数据集的作者是谁?
能帮我联系到这个数据集的作者吗?
这个数据集如何下载?
点击留言
数据主题
具身智能
数据集  4098个
机构  8个
大模型
数据集  439个
机构  10个
无人机
数据集  37个
机构  6个
指令微调
数据集  36个
机构  6个
蛋白质结构
数据集  50个
机构  8个
空间智能
数据集  21个
机构  5个
5,000+
优质数据集
54 个
任务类型
进入经典数据集
热门数据集

中国1km分辨率逐月降水量数据集(1901-2023)

该数据集为中国逐月降水量数据,空间分辨率为0.0083333°(约1km),时间为1901.1-2023.12。数据格式为NETCDF,即.nc格式。该数据集是根据CRU发布的全球0.5°气候数据集以及WorldClim发布的全球高分辨率气候数据集,通过Delta空间降尺度方案在中国降尺度生成的。并且,使用496个独立气象观测点数据进行验证,验证结果可信。本数据集包含的地理空间范围是全国主要陆地(包含港澳台地区),不含南海岛礁等区域。为了便于存储,数据均为int16型存于nc文件中,降水单位为0.1mm。 nc数据可使用ArcMAP软件打开制图; 并可用Matlab软件进行提取处理,Matlab发布了读入与存储nc文件的函数,读取函数为ncread,切换到nc文件存储文件夹,语句表达为:ncread (‘XXX.nc’,‘var’, [i j t],[leni lenj lent]),其中XXX.nc为文件名,为字符串需要’’;var是从XXX.nc中读取的变量名,为字符串需要’’;i、j、t分别为读取数据的起始行、列、时间,leni、lenj、lent i分别为在行、列、时间维度上读取的长度。这样,研究区内任何地区、任何时间段均可用此函数读取。Matlab的help里面有很多关于nc数据的命令,可查看。数据坐标系统建议使用WGS84。

国家青藏高原科学数据中心 收录

LFW

人脸数据集;LFW数据集共有13233张人脸图像,每张图像均给出对应的人名,共有5749人,且绝大部分人仅有一张图片。每张图片的尺寸为250X250,绝大部分为彩色图像,但也存在少许黑白人脸图片。 URL: http://vis-www.cs.umass.edu/lfw/index.html#download

AI_Studio 收录

中国气象数据

本数据集包含了中国2023年1月至11月的气象数据,包括日照时间、降雨量、温度、风速等关键数据。通过这些数据,可以深入了解气象现象对不同地区的影响,并通过可视化工具揭示中国的气温分布、降水情况、风速趋势等。

github 收录

CE-CSL

CE-CSL数据集是由哈尔滨工程大学智能科学与工程学院创建的中文连续手语数据集,旨在解决现有数据集在复杂环境下的局限性。该数据集包含5,988个从日常生活场景中收集的连续手语视频片段,涵盖超过70种不同的复杂背景,确保了数据集的代表性和泛化能力。数据集的创建过程严格遵循实际应用导向,通过收集大量真实场景下的手语视频材料,覆盖了广泛的情境变化和环境复杂性。CE-CSL数据集主要应用于连续手语识别领域,旨在提高手语识别技术在复杂环境中的准确性和效率,促进聋人与听人社区之间的无障碍沟通。

arXiv 收录

红外谱图数据库

收集整理红外谱图实验手册等数据,建成了红外谱图数据库。本数据库收录了常见化合物的红外谱图。主要包括化合物数据和对应的红外谱图数据。其中,原始红外谱图都进行了数字化处理,从而使谱峰检索成为可能。用户可以在数据库中检索指定化合物的谱图,也可以提交谱图/谱峰数据,以检索与之相似的谱图数据,以协助进行谱图鉴定。

国家基础学科公共科学数据中心 收录