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Social Media Listening And Analytics With AI

Social listening analytics helps identify trends by analyzing social media data to generate actionable insights. In this blog, we explore the technology and how insights derived from social media analytics help businesses.

What Is Social Listening Analytics?

Social listening analytics is the analysis of brand-related conversations, content, and mentions on social media channels for customer insights. A business can use these insights for developing intelligent strategies to capture new business opportunities and enhance its current presence. AI-based machine learning platforms scan thousands of comments, posts, hashtags, user-generated videos, news items, memes, and all other social chatter, and analyze all of it for sentiment. This way, you can know how the public feels about your brand, and what the reasons behind your social media metrics are.

What Are The Benefits Of Social Listening Analytics?

Social media analytics can give an organization important market intelligence to create effective strategies for business growth and increased revenue. Some of these advantages include:

  • Discovering important industry trends
  • Tracking brand reputation
  • Developing result-oriented, targeted marketing campaigns
  • Measuring brand performance and awareness
  • Developing personalized shopping experiences for customers
  • Conceiving new ideas for bettering customer service
  • Social influencer marketing
  • Getting accurate competitor intelligence
  • Exploring product innovations and new product ideas
  • Ideas for engaging social media content that different audiences can relate to

Discover more about the insights a benefits of social listening tools.

Which Insights Are Derived From Social Media Analytics?

Social listening analytics give several insights that businesses can use, such as brand mentions, social sentiment scores, emotion-aspect co-occurrence, hashtag analysis, and much more. Below we explain these insights in detail.

1. Brand mentions

Social listening analytics can tell you all the information you need about your brand mentions. You will know whether they are positive or negative, and depending on the sentiment expressed you get the opportunity to decide how you want to address them. Social media analytics tools enabled by machine learning also have a sentiment analysis dashboard that can alert you when your brand mentions appear through notifications on your email or phone. You can set these alerts based on keywords as well. A vital aspect in keeping a tab on brand mentions is also that you are constantly aware of who is associated with your brand, and who is using your name and social posts in a context, inappropriate and in contrast, to your branding, like the BBC Panorama investigation revealed.

2. Social sentiment

Social media analytics provides you with all the low down regarding public sentiment about your brand expressed across social media platforms. Video content analysis allows you to analyze not only comments, posts, and hashtags but also videos themselves on TikTok, YouTube, Douyin, IGTV, and other social media channels. These videos could be for product reviews or even advertising campaigns run on GenX channels that could give you, for example, TikTok consumer insights for a social media influencer campaign that you are running. Social sentiment gathered thus can be used to either maneuver the audience in the direction you want or develop more engaging social content.

3. Logo identification

Logo identification is a very clever way of finding out where your brand is being used without your consent, as well as getting competitor intelligence even in social data formats that are not text. Many times voice of the customer data can go beyond comments, previews, etc, and just involve videos and gifs, where there is no spoken word nor written text. Repustate’s clever sentiment analysis API overcomes this hurdle in social media listening through sophisticated video AI that includes tasks such as search inside video, semantic search, etc, to capture logo and imagery in the background of the video data. This way, if your logo or your competitor’s logo appears even on a t-shirt, let alone on the video footer, the API will extract that information and analyze it contextually, to tell you why your or your competitor’s logo was in the video at all.

4. Trend analysis

Every brand and organization wants to be on social media to ensure that it has the widest reach possible and meets the most audiences. This has lead to a massive amount of big data on social media. The good news is that we can harness this information to discover trends and see how we can use them to our advantage. Social media is the great equalizer, meaning that regardless of size, a company can take advantage of the many perks that social media marketing can bring. Whether you are a famous clothing brand like Abercrombie & Fitch or a musician who wants to get noticed for sponsorship deals, social listening analytics can give you all the information you can use to.

5. Sentiment over time

Social media analytics can tell you whether public opinion is with you, or against you, as time progresses. This is extremely useful when you are running multiple social media campaigns and want to measure the effectiveness of each campaign based on certain timelines, along with an audience breakup. Based on what the findings are, you can regroup and change your strategy by adding more engaging and interesting content or run different challenges as you can do on TikTok, and take part in hashtag campaigns.

6. Sentiment over various channels

Relating to multiple age groups and customer demographics means also measuring how well you are doing with all of them individually. Social listening analytics can give you insights into the effectiveness of your customer experience programs by analyzing data from every social channel separately. You can get consumer insights about GenZ audiences through TikTok analysis, mature audiences through Facebook data analysis, or gaming fans through analysis of channels like Twitch that can help you with ad placement, consumer outreach, and better content creation.

7. Sentiment in various regions/languages

Having an online presence through your own supply chain or by using platforms like Amazon and AliExpress can enable you to reach a wide audience spread across geographies. Social media analytics helps you engage all these people regardless of the language they engage in, as well as, conduct emotion mining by gathering and analyzing all the product/service review sentiment data in different languages. Natural language processing (NLP) allows a machine learning-based social listening platform to understand and analyze social media lingo in multiple languages. Repustate’s social media sentiment analysis platform, in particular, analyzes 23 languages natively. Since using translations dilutes the essence of the data due to the fact that nuances in various languages differ to a great extent, analyzing a language in the native tongue through specific speech taggers gives vastly accurate results.

8. Hashtag analysis

Social media analytics can tell you details about sentiments revolving around specific hashtags, and the frequency of their appearance. You can analyze trending hashtags and process the content by context of time, brand, product type, audience type, user profiles, and other aspects. By analyzing TikTok or Facebook hashtags, or segmenting Twitter hashtags, you can find out all you need to know about what’s working for you and what’s not and make an informed decision about using it for better efficiency and results.

9. Emotion-aspect co-occurrence

Last, but not least, social listening analytics tells you about emotion aspect co-occurrence by breaking the social data into finer categories from which aspects are extracted. The platform’s sentiment mining capability then analyzes each piece of aspect for occurring emotions expressed by people. The machine model then shows the trend of aspects and emotion co-occurrence so you can know which product feature coincides with which sentiment the most.

Learn more about emotion-aspect co-occurrence.

Ending Notes

AI-based machine learning platforms like Repustate’s sentiment analyser are able to semantically understand social media data by context and not just through the literal meaning of words. Every social post is analyzed based on the channel it is in, the social lingo used, and by extracting emojis and special characters as well, so that there are no false positives or negatives while calculating social media sentiment scores.