- Data Categories
Agriculture Data 
B2B Data 
Biometric Data 
Company Data 
Conservation Data 
Demographic Data 
Economic Data 
Environmental Data 
Geospatial Data 
Healthcare Data 
Individual Data 
Industry-Specific Data 
Internet of Things (IoT) Data 
Legal and IP Data 
People and Employment Data 
Political Data 
Psychology Data 
Real Estate Data 
Retail & Commerce Data 
Social Data 
Space Exploration Data 
Sports & Entertainment Data 
Web Data
- Data Categories
- Agriculture Data
- B2B Data
- Biometric Data
- Company Data
- Conservation Data
- Demographic Data
- Economic Data
- Environmental Data
- Geospatial Data
- Healthcare Data
- Individual Data
- Industry-Specific Data
- Internet of Things (IoT) Data
- Legal and IP Data
- People and Employment Data
- Political Data
- Psychology Data
- Real Estate Data
- Retail & Commerce Data
- Social Data
- Space Exploration Data
- Sports & Entertainment Data
- Web Data
Social Media Analytics
How Is External Data Used in Social Media Analytics?
Social media analytics platforms utilizing machine learning programs measure social media interest in a topic through many external data sources. The most common sources come directly from social media sites and apps like Google, Twitter, Facebook, and so on. Additional sources include sites that aggregate social media trends, like SocialBlade, and news sites.
Machine learning tools either scrape the web for this data or pay these social media companies for access to their raw data. They then store, clean, and analyze the information for the amount of interest in a subject and people’s feelings about it, according to whatever demographic the company wants to target.
How are Machine Learning Models Used in Social Media Analytics?
Machine learning models today do not stop analysis at web scraping, opinion mining, or text analysis. Rather, they continue with NLP (natural language processing) and sentiment analysis to understand exactly how people feel about a topic. These social media users are then divided into customer segments. Good machine learning programs can also apply geographic and demographic data to these segments to analyze sentiment as completely as possible.
These programs can also identify emerging topics and trends. This enables data scientists to analyze the entire social media landscape in real time.
Finally, machine learning can identify sources of social media influence. Companies can use this information to partner with social media users with large audiences to market their products. Recently, however, companies have begun shifting focus to social media users with smaller audiences, as their followers consider their endorsements more genuine.
Which Companies Lead the Way in Building Advanced Analytics and Machine Learning Products in This Field?
Kantar Media
Kantar Media specializes in providing social media and consumer sentiment data for retail and marketing companies. In addition, they provide demographic, consumer transaction, lifestyle, and price optimization data.
Please feel free to review them on our site here.
Hootsuite
Hootsuite not only provides social media data but a user-friendly analytics platform that your entire team can utilize. They also offer reports and online marketing training certification courses.
Socialbakers
This site offers social media data as well as customer segmentation, marketing help, and customer services. They partner with major social media names like Linkedin, Instagram, Facebook, Youtube, Pinterest, and more.
Like Hootsuite above, they also offer educational courses on online marketing.
Connected Datasets
PipeCandy eCommerce Leads & Insights for Fulfillment
PipeCandy eCommerce Leads & Insights for Fulfillment tracks company data and company shipping details: shipping volume, which companies they use to ship products, whether they ship internationally or not, and so on. You can find company leads easily with this dataset with filter capabilities.
Wikiroutes Transit Data
Wikiroutes Transit Data provides public transport information—routes, stop points, and more—via crowd-sourcing. The data is constantly updated and can be easily converted and integrated into your own software system.
Wikiroute’s Transit Data is used by individuals, private companies, and government agencies of all types and sizes.
TrackStar Predictive Credit Technology
TrackStar’s Predictive Credit Technology uses fifteen years of financial dispute data to create predictive models of future borrowing potential. With this data and AI technology, your bank or other lending company can mitigate the risk of fraud, improve existing customer relations, and reduce your operating costs.