Why Should We Use Sentiment Analysis In Social Media Mining?

Social media mining is a strategic way to monitor trends and patterns in the feedback people give about your brand on different social platforms. More than 3.6 billion people actively use social media to drive conversations, and so gathering data from these social sites is a great place to start. This article gives a brief overview of how you can use social media mining and social media sentiment analysis to define a growth strategy and sense customers' sentiment about your brand.

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Once data is gathered, what matters most is how you translate it into actionable insights to improve customer experience at multiple touchpoints in the sales cycle. For this, you need to conduct sentiment analysis on all our data for insights.

What Is Sentiment Analysis?

Sentiment analysis is a natural language processing (NLP) technique used for understanding the emotions behind user-generated content from social media mining. It gives a clear sense of how people feel about your brand.

Since social media has become a vital brand touchpoint, businesses use sentiment analysis to interpret feedback, comments, social media conversations, and clients’ reviews. Social media sentiment analysis tools combine machine learning and NLP tasks to give you a glimpse of what’s on your customers’ minds. Are they happy with your product or services, or are they discontent.

Common Channels for Social Media Mining

Common Channels Used for Social Media Mining

  • Twitter

With more than 350 million active users, Twitter is one of the most important channels for customer engagement. About 70% of Twitter users fall between the ages of 18 to 49. For brands targeting this age demography, Twitter is an effective tool for market research, audience listening, and data mining.

  • TikTok

TikTok has more than 689 million monthly active users who share short, creative video content. The platform has helped build a niche community, and is ideal for brands with younger consumer groups since 60% of TikTok users are between the ages of 16 and 24 years.

  • YouTube

YouTube analytics is another effective data source for social media data mining. YouTube allows you to upload videos that are up to 12 hours long. It also supports live video content sharing. Users interact with YouTube content through likes and dislikes, comments, and follows. It has more than two billion monthly active users, and the bulk of these users are between the ages of 15 and 25.

  • Facebook

Facebook is the world’s biggest and most famous social network. With nearly three billion active users and 1.84 billion visitors every day, it is one of the favorite voice of the customer research channels for B2B and B2C businesses.

Facebook allows users to share content in different formats, including text, video, and pictures. You can also build communities using Facebook groups, and share live content with their friends and followers.

  • Instagram

Instagram has over one billion active users spread over different age groups, especially people aged 18 to 24, and 25 to 34. Instagram allows users to share pictures and videos. It also has additional features like reels, stories, Instagram guides, and live chats. Users engage through shares, likes, follows, tags, mentions and also save posts they find interesting.

  • Tumblr

Tumblr hosts more than 475 million blogs. It allows bloggers to share curated multimedia and other content as short-form blogs with their audience. Tumblr is popular among all age groups, but most of its users are 18 to 24 years old.

What Are the Challenges of Social Media Mining?

Social media mining carries many challenges such as the volume and quality of the data itself. Add to this, the difference in multimedia formats can pose a limitation. Data security is also a critical factor. Let us examine these and more issues that can arise in data mining.

Social Media Mining Challenges

  1. Volume

Social media mining involves extracting large volumes of data from multiple social media platforms. It can be challenging to put these big data sets into meaningful categories to understand your customers. The large scale also means that the solution for sentiment analysis needs to have a high speed of processing capacity.

  1. Security

When you’re handling large data sets, you need to be mindful of data security threats. Data breaches and leaks can compromise valuable customer information and ruin brand reputation as consumer information can be highly delicate.

  1. Multimedia

Raw social media data is in multiple file formats. For instance, Facebook data is in text, video, and image files, while Instagram data is primarily videos and images. Sorting data and extracting insights in these multimedia formats can be tedious and time-consuming.

  1. Text Overlay On Video

When analyzing visual channels like TikTok for valuable data, you need to extract meaningful insights from caption overlay and dialogue. Repustate’s social media listening tool helps you categorize these insights from audio data via speech to text, and analysis, text overlay for videos and background images from branded content.

  1. Entity Identification

Entity identification automatically classifies key elements in data sets into predetermined categories or entities like location, product, person, and organization. With large-scale multimedia data, it becomes difficult to identify and extract relevant entities from a pool of diverse data formats.

  1. Quality

To accurately understand consumers’ sentiments and predict their behaviors, you need to extract high-quality data during social network analysis. When accumulating data at scale, it is easy to compromise the semantic quality. This results in multiple data errors and omissions.

  1. Logo Identification

Visual analytics is an essential data mining technique. For example, a Facebook user can add your brand’s logo to a post without explicitly showing positive or negative sentiments towards your business. Data analytics helps you to interpret this behavior accurately to know what it means for your brand.

  1. Data Unification

Data unification is a complex process of cleaning different social media datasets and merging the result into a single, accurate data source. Since you’re dealing with large data volumes in multiple formats from different social media platforms, you need the right sentiment analysis dashboard, or this process can easily become frustrating.

  1. Multilingual Customers

If your existing customers and target audience are spread across multiple geographic locations and speak different languages, this can affect your data mining. With Repustate, you can detect different languages and understand your data using a custom sentiment analysis model.

How Sentiment Analysis Improves Social Media Mining Results

After gathering and categorizing large data sets, you need to interpret them to arrive at meaningful insights. Every interaction a social media user has with a brand in terms of likes, comments, shares, follows, and mentions communicate some feeling or attitude.

However, there’s no way to figure out whether these feelings are positive or negative without sentiment analysis. Let’s show you how social media sentiment analysis helps businesses to understand the meaning of comments, audio, videos, and brand mentions.

  • Brand Mentions

Having a high number of brand mentions on social media can initially feel like a good sign. However, when you take a deep dive with sentiment analysis, you may discover that a number of these mentions are more negative than positive. Sentiment analysis helps you to understand the true emotions behind brand mentions on different social platforms.

  • Comments

You can use sentiment analysis tools to analyze comments about your brand for opinion-polarity to know if these comments are positive, negative or neutral.

  • Audio and Video

More than understanding the emotions behind textual references like comments and mentions, sentiment analysis also helps you to extract insights from audio-visual content on social media platforms like TikTok and Instagram. Using the right tools, you can extract vital brand insights from short-form videos. You can also identify branded content in the background of a video, for example if a social media user wears a shirt with your company logo.

  • Product Intelligence

With sentiment analysis, you can understand which features of a product or service are liked by your customers. Check out this example of how YouTube video sentiment analysis is done to extract sentiments for different features of the new PS5 in comparison to the XBOX Series X.

Unlike social media monitoring, sentiment analysis is all about interpreting different opinion mining use cases to discover the emotions behind them. Depending on the business needs, the social media sentiment analysis spectrum can focus on polarity precision, emotion detection, or aspect-based sentiment analysis.

In a general sense, here are four ways sentiment analysis can improve social media mining results for businesses:

  1. It helps to sort and organize large volumes of data.

  2. It highlights pressing issues that need to be attended to right away.

  3. It outlines standard and consistent criteria for data interpretation.

  4. It allows us to monitor sentiment associated with trends.

Social Media Sentiment Analysis - The Repustate Advantage

Repustate helps you carry out effective social media listening and gather helpful information to drive decisions. With our text analytics tools and multilingual sentiment analysis, you can get easy, accessible insights at scale.

  1. Sentiment Analysis Dashboard for Data Visualization

You can track analytics and measure your performance on different social media platforms from one place. Whether it’s TikTok, Twitter, Facebook, or Instagram, you can monitor all conversations simultaneously. With a single button, you can view data on keywords, trending hashtags, brand mentions, and more.

  1. Real-Time Social Media Listening

Never miss out on relevant conversations on your preferred social media platforms. Our real-time data capabilities track brand mentions as they happen. It empowers you to resolve issues quickly and boosts customer experience for your clients.

  1. Competitor Analysis

With Repustate, it is easy to know how you measure up against the competition. More than viewing relevant insights on your brand, you can also track conversations about your competitors for comparative analysis.

  1. Social Listening From Audio or Video Stream

You have access to not only text data but also all mentions on streaming channels and social media platforms. We provide deep-level insights on brand mentions in other media formats with astute audio and video content analysis capabilities.

  1. Stock Market Sentiment Analysis

The stock market can be a competitive and cut-throat industry. High-frequency trading algorithms search for any disruptions in the market and use them in sub second transactions. Having accurate information at your fingertips means that you can make the right call on any given trade.

  1. Customer Trend Analysis

Repustate interprets existing trends to reveal the behavior patterns of your customers. You can track customer experience information over defined periods, and figure what likes and dislikes are common amongst your customers.

With the right sentiment analysis model, you can effectively interpret data from social media mining. Thus making it easy to define your brand’s messaging and growth strategy around things that your customers care about. Our social media listening solution will help you understand not only your customers but also your competitors.

Learn more about Repustate’s social media listening solution

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