We have vast experience in producing data visualizations from scraped data of all major online social networks and aggregators - Twitter, Instagram, Reddit and others.
As part of our projects and platforms, we have scraped over billion data points (tweets, posts, followers, etc.) from online social networks.
Example of Visualization - Social Media Mentions on Twitter
For several of our projects, we provided mentions analytics - the number of mentions of an entity through time. An example of hourly mentions for #GDPR on Twitter (from one of our projects):
We use machine learning models for sentiment analysis and opinion mining of tweets, posts, reviews and other texts. If you are looking for cryptocurrency sentiment rest api data we provide this as well. Hourly sentiment of tweets containing hashtag #digitaleconomy (from one of our projects):
Sentiment and mentions analytics can be combined to assess in real-time the sentiment and interest in entities, decisions, events and other topics. Analysis of fear and greed emotions displayed by sentiment can be utilized by building Fear and Greed Indices for Crypto Markets. Example for @Eu_commision:
We use advanced natural language processing (NLP) methods such as Latent Dirichlet Allocation (LDA) and Latent Semantic Indexing (LSI) to determine topics discussed in social media. Example of topics determined from tweets mentioning #digitalhealth on 18th July:
Named entity recognition (NER) denotes the process of entity extraction from texts, whereby entities can be persons, organizations, locations, countries, etc. NER system in combination with other analytics helps in numerous questions and analytical tasks, such as:
- - which organizations write most/least about a particular entity, e.g. GDPR
- - which organizations write most positively/negative about a particular entity
- - which entities are often found together in documents
- - which entities are more discussed comparatively (e.g. trade agreements with different countries)
- - what is the time evolution of entities mentions and associated sentiment through time
An example of our NER visualization as applied on a document „A new era in EU-China relations: more wide ranging strategic cooperation?“ with automatic extraction of persons, organizations, geopolitical entities, events and laws.
We use the following tools to help us with categorization websites: website classification tool, including rest API and for reverse IP Lookups via API: reverse IP address lookup of domains.