If you own an online business, the following scenarios must be somewhat familiar—dipping sales, shifting consumer preferences, competitors taking you over, etc. Did you ever think, “why?”
The answer may be buried in volumes of raw, unstructured social media data. However, not all companies are using it to their benefit. If they are, they likely stop at basic demographics and sales data, missing out on the gap between what’s being said and what can be done for (and with) it. To help you make this leap, let’s explore social media data mining as the key to analyzing user sentiment behind social media data like comments, hashtags, product reviews, opinions, etc. Keep reading to witness the power of knowing “why.”
The Value of Social Media Data
Social media platforms and apps (Facebook, Twitter, Instagram, TikTok, etc) are hubs for all sorts of activities, including interactions, product reviews, emerging trends, political opinions, etc. Every post, tweet, reel, comment, or story speaks of what people think, feel, and care about. With billions of people (5.22 billion) using these platforms, you can find conversations on their dislikes, trends they follow, and even their frustrations.
If used smartly and rightfully, this social media data can provide practical insights that traditional consumer information fails to. However, the true value of scraping social media data comes with analyzing it in near real-time.
Here are the many types of social media data that you can scrape:
- Post, comments, and trending hashtags that reveal opinions.
- Likes, reshares, and retweets that point to emerging trends.
- Social media profile data—usernames, emails, demographics, etc.
- Networking data, such as followers, groups, etc.
- Clickstream data that tells you what links users are clicking on.
How to Access this Data: Tools and Techniques
Social media data holds much value, but you must access and extract it effectively. This requires a conscious approach, such as using self-serve software or tools, employing data scraping techniques, or outsourcing social media data scraping to professional service providers.
Using Tools and Data Scraping Software
Several options fall under this category. Software solutions and tools like Scrapy, Import.io, and Octoparse (among many others) are widely used to extract social media data. Their widespread utility has resulted in a market worth US$ 568 million (as of 2024).
Employing Data Extraction Techniques
Instead of using self-serve tools, many businesses extract unstructured data from social media for sentiment tracking through scraping techniques like:
- APIs – Such as Twitter API, Instagram Graph API, and the LinkedIn API.
- Web Crawlers – Custom data crawling bots that automate the process of extracting social media data.
- Python-based Programming – Using Python libraries and other scripts to fetch social media data from specific platforms.
- Proxy Servers – Using proxies to bypass IP blocking and access location-specific social media data. It’s a well-known approach that can reduce your overhead costs (by as much as 40%).
Hiring Data Scraping Experts or Seeking Professional Data Scraping Services
Often, due to high scope or rising data complexity and volume, many businesses outsource social media data extraction. They either hire freelance data scraping professionals or seek professional data scraping/extracting services. By doing so, they externalize the process to free up internal resources, save high costs associated with data extraction tools and infrastructure, and reduce the time invested in manual data collection without compromising on the quality of scraped social media data.
Using Social Media Data for Sentiment Analysis
Gauging user likes and dislikes or spotting emerging trends is only a surface-level application of social media data extraction. Today, this data is used for more intricate use cases. This primarily revolves around getting insights into public sentiment. Based on how people engage with content, businesses can track their emotions to shape their strategies. It could be any kind of unstructured data—some product review expressing frustration or regular Instagram comments.
Once this data is collected, it undergoes processing. It is cleaned, organized, and formatted for easier analysis. The process could also include removing irrelevant text, spam, or duplicates while retaining relevant data points.
Ultimately, AI/ML and NLP algorithms are used to skim through this processed social media data. These algorithms identify patterns that are then categorized into broader sections, such as positive, negative, and neutral. Then, the resulting dataset is analyzed for sentiment. More sophisticated solutions can also spot subtler sentiments like anger, sarcasm, and even happiness.
The Flip Side: Limitations and Ethical Considerations
The social media data you extract will only yield insights if you have a predetermined use case for extracting social media data, it is sourced rightfully, it is processed properly, and you’ve adhered to data extraction guidelines.
The above conditions stem from the limitations and ethical considerations mentioned below:
Bias in Collecting Social Media Data
Unknowingly, you may scrape social media data only from one demographic section. As a result, you will have a biased dataset. And analyzing it will provide inaccurate findings. This could be an underrepresentation of certain sentiments and vice versa.
Lack of Ground Truth
At the core, using unstructured social media data to analyze sentiment is based on a fluke. There is no actual “ground” to validate or confirm the findings. This makes it challenging to comment on the accuracy. That said, it still offers some direction to plan.
Legal Risks
While social media data extraction is legal, there are still some restrictions. Each platform has its own APIs, safeguards, and data extraction limits. Some, like Twitter, are also known to restrict unauthorized crawlers. Companies that don’t adhere to these guidelines risk getting banned from platforms.
Context and Slang-Related Issues
Social media data contains regional, grammatically incorrect, and slang-based data. Additionally, the use of emojis, trending abbreviations, and regional nuances make it a confusing mix. This can easily confuse even advanced algorithms.
Data Quality Challenges
Raw social media data is highly messy and unfiltered. It often comes with spam, irrelevant posts, and broken information, making it harder to draw clear insights. This requires rigorous and conscious filtering, cleaning, and preprocessing.
However, if you have a well-defined purpose, extract data consciously, and adhere to platform-specific rules and ethical guidelines, you can steer clear of these limitations. Keep in mind; a thoughtful approach will not only mitigate risks but also enhance the value of the insights derived.
Aligning Social Media Insights with Broader Business Strategies
Gauging user sentiment and social media mining goes well beyond simple monitoring and social listening. This information can have tangible implications in your business’s favor. Let’s see how you can integrate these insights into a broader business strategy.
Competitor Review & Benchmarking
Insights from social media data mining can reveal strategies that might have worked for your competitors. It could be some product features that customers love or an advertisement that performs well. By benchmarking this data against your own efforts, you can tailor your strategies for better sales performance.
Managing Crisis in Real Time
If user sentiment reveals a negative perception about a certain product of yours, social media data can help you assess its urgency. You will know whether it is a one-time complaint or a prevailing issue that points to a larger problem. This knowledge will position you to act quickly, make amendments, and clarify actions before issues escalate.
Maintaining a Consistent Brand Reputation
Social media mining is a great peek into people’s perception of your brand. Are they sharing it with their peers or engaging with your content? If yes, you’re on the right path. And if not, you can adjust your marketing efforts and change how you approach customers.
Finding Partnership Opportunities
Tracking positive mentions, comments, and reshares can open doors for potential collaborations. If these mentions come with some other brand or product, you can explore partnership opportunities. This will not only broaden your user base but also help your brand connect with others that resonate with a similar audience.
Also Read: What is Social Media Integration
Final Thoughts
Social media platforms and apps have grown beyond simple networking and entertainment. With billions of users, they hold valuable, actionable data to help online businesses hone their presence and reach a wider audience. This data allows them to spot trends, track emotions & opinions, and interact with their customers in unprecedented new ways. That said, the true value lies in how you get access to this data and your ability to integrate the insights with your business strategy. Suppose this is done responsibly by adhering to ethical standards and respecting user privacy. In that case, you stand a better chance at using social media data mining to shape and create lasting value for your company and your audience.
