Digital social interactions result from the increased use of social media and internet-based communications. As time goes, this is becoming more and more complex in a faster speed than people can create standard concepts for handling it. In fact, big social data has generated mixed reactions, as data scientists try to work against time to create concepts that can work for them. Most companies have faced the challenge of how to handle big social data in the past few years. However, clearer concepts are coming up, as evidenced on Active Wizards team website. So, let’s see the various concepts that drive big social data.
Social media and Internet-based communications are more on text messages than audio. Thus, experts work hard to develop tools for text data mining from the pool of data in servers. This will definitely go far beyond the extracting documents, blog pages, and website links to statistical analysis and linguistic computation. In fact, all experts handling this kind of data need to have a machine learning background.
The information extraction techniques will be used to dig pre-set information from databases like the names or social media pages for various clients; it can also use dates to extract similar information.
Social media data can be extracted from the database through certain query-answering techniques. This common concept has worked well, although it will give a list of results for one to select. For greater filtering of details, the QA concept has various stages of use as indicated below:
- Questions processing – This involves determining the type of question to use, the focus, and the answers to be used to retrieve data.
- Document processing – This is used when one wants to retrieve social media documents or contexts from larger write-ups.
- Answer processing – This is used to process the answers one will use, so as to achieve the most accurate results.
Social media data is full of people’s opinions on products and services from firms. As such, experts can decide to categorize the opinions and use this whenever one wants to extract the data. So, if the attention is on the document level, it will first assume that this is based on a single entity and center its attention on whether the sentiments are positive or negative.
Sentence analysis, which is more detailed, deals with one entitle described in a single sentence; most data scientists use this technique whenever possible. As a matter of fact, it’s more applicable in social media, as people comment about a product in simple sentences.
Audio and video Analytics
Video content analysis (VCA) and audio analysis are not so popular in big social data handling since not many people use this in comparison to text data. However, experts have come up with these concepts to deal with any that happen. The analytics mostly deals with automated retrieving, using titles and segmentation or larger video or audio files. As a result, people can get more specific media from their social media data pools for reference or decision-making strategies.