Going Beyond Sentiment Analysis: A Guide for Social Media Marketers

To truly tap into social media sentiment analysis and leverage it for building effective marketing strategies and brand campaigns, social media marketers need to go beyond settling for positive and negative sentiment scores. In this article, we explore 10 powerful ways in which marketers can use sentiment analysis for more than just vanity scorings and actually put them to use strategically for business growth.

Why Companies Are Spending Heavily On Social Media Marketing

Companies are investing heavily in social media marketing. Social marketing-spend in the US alone is projected to be USD56 Billion this year, with more than 80% of it being on Facebook alone. Seven years ago, in 2016, this figure was only USD15 Billion. A contributing factor to this phenomenon is that almost every business - big and small - accounting for more than 91% of companies in the US, is on social media today.

This has caused a competition like none other to capture the attention of consumers - be it through TikTok challenges, Facebook surveys, YouTube Ads - you name it. The exercise, of ofcourse, is for social media listening.

There are several reasons why companies are in such a frenzy to up their social media presence. Key factors include:

1. The spectacular rise of a variety of video-driven platforms such as Douyin, TikTok, YouTube, Instagram, and others.
2. To dig deep and analyze social media comments to build effective engagement strategies with customers for greater conversions.
3. To understand customer needs for product/service innovation for competitive advantage through social media sentiment analysis.
4. To better cater to a diverse target audience spread across numerous languages and cultures.
5. To create better targeted and relevant below-the-line (BTL) and above-the-line (ATL) marketing strategies.
6. Explore new-age marketing techniques like influencer marketing.

Why Go Beyond Sentiment Analysis?

Given the huge amount of cost and planning involved in social media marketing, it’s a no-brainer that businesses need to get the most out of their investment. This means that marketers should be able to use social media sentiment analysis insights beyond just tracking customer sentiment for their brand. They should be able to use that data to boost sales conversions, recharge marketing strategies, plan parallel social campaign strategies, track emerging industry trends, and explore new markets.

It is also important that marketers ensure that the sentiment analysis tool they are using to this end can overcome all the challenges in sentiment analysis, and give accurate, in-depth insights at a speed and scale they can use for greater business strategies.

How Can You Go Beyond Social Media Sentiment Analysis?

Social marketers should leverage sentiment analysis by using this data to develop important strategies for brand building, targeted advertising, better audience engagement, influencer marketing, tracking emerging market trends, and more. This is what you can do to go beyond just using sentiment analysis for vanity metrics.

Here are 10 impactful ways you can use sentiment analysis to your advantage.

1. Brand Insights

You can use a sentiment analysis tool for all kinds of brand experience analysis. You can find out customer experience details or you can use the insights to build your employer-brand, so that you can make holistic changes in the right direction. For example, when Repustate’s sentiment analysis analyzed Wendy’s for brand insights, it found contrasting results for two Wendy’s locations, even though the overall brand sentiment was positive.

This means that even though you may think that customers’ opinions towards you are generally positive, there could be many factors that, if not, checked and corrected, could lead to a decline in your brand ratings. Read the case study.

2. Competitor analysis.

Just as you use the ML platform to discover important social media sentiment analysis insights about your brand, you can do the same to get insights about your competitors. You can find out why customers love your brand but still buy certain products from a competitor.

This also helps you keep a track of other things like which social media platforms your competitors are most active on, if they are mimicking your marketing tactics, what you have in common with them, and such. Marketers can find this information and more in the form of easy-to-understand charts and graphs in the platform’s customer experience dashboard.

3. Track emerging market trends

Being ahead of the competition also means knowing what new trends are emerging in the market and what it is that audiences want. Analyzing social media comments is vital for this process because it can give you many insights that you wouldn’t be able to find on your own. Social media comments are spontaneous and therefore sometimes there are nuggets of information that can be gold to your growth strategy. Check out the intriguing insights we extracted from a viral McDonald’s TikTok video.

4. Inspect sentiment trends

You can analyze how your brand sentiment is going up and down based on a particular time period. This will help you discover a trend, which you can dive into deeper to see what actually caused them. You can use this data to prepare for future situations that are similar in nature and to ensure that your customers are always happy.

For example, see how data in a popular MAC lipstick YouTube video analysis showed negative sentiment but when inspected closely, revealed something else. It showed that the negative sentiment was not because people were unhappy with the product but because the company misjudged the popularity of the new color and therefore did not have enough stock. This had lead to disappointment and frustration amongst customers. In short, customers loved the product more than the company had anticipated.

5. Check aspect trends

The social listening process, with the use of an aspect-based ML model, allows you to keep a close eye on sentiment trends of specific aspects of your brand or product. For example, if you are concerned about the customer sentiment regarding a new design for your luxury watch series, you can do an aspect sentiment analysis trend to see how customers are reacting to your new design compared to the same data for another design or time period.

6. Explore brand partnerships

You can use social media comments analysis to explore customer preferences for brands and products that are complementary to you and strike brand partnerships. For example, if you are a gaming company, you can strike a brand partnership with hardware companies that produce monitors, chairs, clothing, accessories, etc. Or if you are a hotel, you can explore brand partnerships with a car rental company.

7. Audience profile

You can use an ML-based social media sentiment analysis tool to get a better understanding of your customers by analyzing publicly available user profile data. This will tell you what the common interests amongst users are, where they are mostly located, what they are most likely to purchase, what they talk about you in brand mentions, and more. This will help you tailor your product or service and its availability in a better manner.

8. Vet Influencers and brand ambassadors

Influencer marketing is still one of the best ways to create a buzz around your brand. It is also certainly the most economical way to do so. It can give you the style and glamor of a Vogue feature but without the costs involved in a photoshoot with art directors, makeup artists, clothing stylists, hair & makeup, lighting, models, photographers, and multiple other people.

The Covid19 pandemic lead to a temporary influencer slowdown but now as things gear up, a social media sentiment analysis tool can analyze comments and hashtags across platforms to tell you which influencers would be a good fit for your brand and help you keep track of their content.

9. Create Targeted content

Brand insights, audience profiles, and competitor analysis can give you all the details you need to know to develop a really engaging piece of content. Social media sentiment analysis of your social media campaigns, hashtags, brand mentions, and comments in user-generated and brand videos can lead you to see which platforms are best for audience engagement and which give you the most return on your time and resources.

10. Build brand loyalty

Investing effort in building brand loyalty goes a long way. If you have a close relationship with your customers, if they trust you, and think of you as a ubiquitous part of their life, they will forgive missteps and bounce back. One can’t think of a better example than Tim Hortons, a brand that has become so ingrained in Canadian culture that there are memes abounding forever and everyone nods in agreement.

How Does A Sentiment Analysis API Discover Key Insights?

Social media marketers can go beyond sentiment analysis by using an AI-based sentiment analysis API or platform that is available in custom models matching their exact requirements. The API discovers and extracts powerful insights from social data with the help of semantic clustering, natural language processing, social media video analysis capabilities such as TikTok analysis etc, the ability to extract nuances from social lingo (emojis, memes, hashtags), much more. Let’s look at them in detail.

1. Aspect sentiment through semantic clustering

Aspect-based social media sentiment analysis is the most granular of all emotion mining techniques. It gives you not only the overall sentiment in your data but also that related to aspects. For example, if you’re a hotel, aspects would be price, food, accommodation, etc. ML tasks segregate all related data through semantic clustering so that there is no redundancy, and this gives you the most accurate interpretation of the data. This is especially critical in brand experience analysis.

2. Custom aspect models

In order to get accurate sentiment results from aspect-based sentiment, it is important that you use an ML model that matches your business and industry. This is because the aspects of a hotel (pool, restaurant, rooms) vary from a bank’s (account type, salary, branch).

3. Video analysis

Social video analysis can extract insights from videos and gifs to give an accurate picture of what’s being said. It can do so from any video source such as YouTube, Instagram, Facebook, or TikTok. The capability uses many ML tasks to get audience insights from these videos. Learn more.

4. Powerful NER

Named Entity Recognition or NER enables the sentiment analysis platform to find brand mentions, locations, celebrity names, or any other name-based information that may be key to your insights. It does so with the help of knowledge graphs and neural networks. When you need to analyze brand sentiment during events like the Oscars or the Superbowl, in different geographies, amidst certain demographics, a powerful NER capability can be a game-changer. This is especially relevant for fan experience analysis.

5. Review analysis

The platform can analyze review websites (Google, Amazon, etc) to extract valuable information from comments. It is important to note that apart from analyzing text data, the platform also takes into account emojis or memes that people may share in the comments. This is important because many text analytics APIs treat emojis as non-text data and so ignore them. But this can lead to incorrect evaluation as a sentence could be sarcastic instead of positive. This can be easily identified through the emojis that are present in the comment.

6. Audio analysis

Podcasts are fun and a great source that can be mined for social media sentiment analysis. The audio is first transcribed and then the data is analyzed for sentiment, mentions, etc.

7. Social media surveys

This is an important one. Many companies use social media for surveys, which are quick and easy. You find many such surveys on YouTube or Linkedin, etc. A sentiment analysis API will extract information even from non-quantified data such as free-flow answers to an open-ended question. See how.

8. Multilingual

The platform uses native natural language processing (NLP) to understand regional colloquialisms, grammatical rules, sentence construction, and nuances in different languages. This is so that there is no inaccuracy in the data, which is almost always the case in machine translations.

Repustate IQ - Actionable Social Media Sentiment Insights

Repustate has been helping clients use insights from social media sentiment analysis for more than a decade across the globe. Repusate’s sentiment analysis platform, Repustate IQ, gives quick, reliable, and accurate insights from all social media data including videos, comments, emojis, audio, captions, and more. It analyzes 23 languages natively using speech taggers for each language and extracts relevant insights from all hot sources such as TikTok, Douyin, YouTube, and more.

See how Repustate helped New York-based agency IF7 extract actionable marketing insights from TikTok for clients such as Tinder and Abercrombie & Fitch.