7 Practical ways to implement sentiment analysis in digital marketing and SEO

7 Practical ways to implement sentiment analysis in digital marketing and SEO

Sentiment analysis is a Machine Learning method that is not yet widely used in digital marketing and SEO, despite its many benefits for organizations and practical application possibilities. In the following guide, I will explore some of the following:

  • Why use sentiment analysis in digital marketing and SEO projects
  • How to get started doing sentiment analysis in Google Sheets (without any coding skills needed)
  • Practical projects to implement the analysis with and how it helps with strategy formulation

In case you’ve missed my previous guide on the topic, make sure to check out the Sentiment analysis deep dive into theory, methods, and applications, linked below, where I discussed recent literature in the field of sentiment analysis and the foundation of how this machine learning approach works.

Why use sentiment analysis in digital marketing and SEO projects

Sentiment analysis is a valuable tool for digital marketers and SEO professionals seeking to gain insights into customer sentiment, identify emerging trends, and optimize their campaigns for better results, going beyond basic metrics like clicks and views.

By analyzing sentiments expressed in social media posts, reviews, and online interactions, marketers can gain a nuanced understanding of public perception toward brands, products, or campaigns. This enables them to tailor their strategies to better resonate with their audience, identify areas for improvement, and predict market trends.

For SEO, sentiment analysis helps in understanding the emotional context of search queries, ranked results, and user-generated content, allowing for more targeted and effective content strategies. This alignment not only enhances user engagement but also aids in building a more positive and impactful online presence, driving both brand loyalty and conversion rates.

Before diving into the practical projects, let’s recap how to get started with doing sentiment analysis.

How to get started doing sentiment analysis in Google Sheets (without any coding skills needed)

Sentiment analysis, utilizing Google’s Natural Language API, is a very beginner-friendly way to get started doing sentiment analysis without any coding skills required. The procedure requires obtaining an API key and using a Google Sheets template. It’s worth noting that this template, while not my creation, have been available publicly for a considerable time, developed by various individuals in the field.

To get started, follow these simple steps:

Screenshot 2023 12 30 at 17.26.05
  • Click on the Extensions menu from the top then AppScript, then enter your API key in line 4 of the code. Then click SAVE project
  • Return to the sheet. Paste the text data in column A
Screenshot 2023 12 30 at 17.27.26
  • Click on the cell under the sentiment score title, and paste the formula from the image below – this will return the sentiment score, magnitude and associated title of the sentiment label category. Drag it down to all results you want to analyze from the first column.
Screenshot 2023 12 30 at 17.28.07

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This process is streamlined and user-friendly; with a simple click, the Apps Script integrated into the templates activates the ‘analyze feedback’ formula. This analysis yields three critical insights: the sentiment score of the title, its magnitude, and a sentiment tag – this can be custom-adjusted via the script.

Importantly, this system is flexible. Users can tailor it to their understanding of sentiment magnitude and can even create custom sentiment tags for more personalized analysis. This adaptability is a key feature, enhancing the tool’s applicability to various scenarios.

7 Practical ways to implement sentiment analysis and insights into your digital marketing or SEO strategy

Now that you see how easy it is to do this type of analysis in Google Sheets, let’s see how to practically implement this method in digital marketing or organic search (SEO) tasks.

Use sentiment analysis to monitor online reputation management in web results

If you were to analyze the sentiment of the titles of results, ranking for your branded queries, you can regularly do online reputation sentiment analysis. While this can be done at a title or meta description level (analyzing the titles or meta descriptions of the entires, that rank for the queries in results 1-10), it can also be done at a page text level, where you scrape the text on those pages and analyze the entire text’s overall sentiment.

Another approach is to analyze the sentiment of social media mentions in Facebook comments, Facebook Group posts that mention your brand, or Tweets (or X-es). Analyzing the sentiment of such entries can help you quickly get a helicopter view of whether your customers or target customers are mentioning your brand with positive or negative sentiment, and how strong their emotions are, based on the way they’ve expressed themselves.

Let’s quickly go through a sample process of analyzing the sentiment of branded queries, that was presented in my video on YouTube on SERP analysis. Let’s imagine that we have collected a group of questions, related to the brand Amazon, and the founder Jeff Bezos. We then collected the top 10 results (their titles, meta description and URLs) via a SERP scraper, like dataforSEO, and analyzed the sentiment using Google Natural Language API.

For instance, the keyword “Why is Jeff Bezos inspiring?” yields titles like “Jeff Bezos, an Inspiration to All” from Mirror Review, indicating a positive sentiment score of 0.4. Conversely, a title like “How to Check Amazon Seller Feedback and Not Get Scammed” for the query “How do you know if the Amazon seller is legit?” suggests a negative sentiment, with a score of -0.6.

By visualizing this data, simple yet effective visualizations can be created, though more complex ones are also possible. This visualization plots titles based on their sentiment score and rank, with the bubble size representing the sentiment magnitude, which allows us to see at which positions and for which keywords some entries are negative for our brand, using the provided filters.

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Furthermore, the report allows for filtering based on sentiment, aiding in identifying both opponents and advocates of your brand in search engine results pages (SERPs). This is vital for online reputation management, as it reveals the content that ranks with negative sentiment and identifies websites frequently publishing negative or positive content related to your brand.

This kind of analysis is not only insightful for SEO managers but can also shape content and link strategy. For instance, looking at data where the results in the top 10 are mostly negative, an SEO or PR Consultant might initiate link building efforts to promote more brand-positive results to key positions. This analysis can also highlight the need for creating content that addresses user concerns, whether it be about product usability, pricing, or other aspects negatively impacting brand perception.

Use sentiment analysis to understand the sentiment of text on pages or videos ranking in SERP

A similar project to the one, described in the previous section, might be used with a different purpose. Let’s imagine you are working with a keyword list of terms, relevant to your brand, website, or product – terms that are topically-relevant for your presence online. You have collected the SERP data for these terms, and have scraped the content on the pages that are ranking in key positions. By analyzing the sentiment of the text in these pages, together with the positions they rank for, you will be able to gain a holistic overview of the search landscape ranking of pages and its relation to sentiment.

To clarify, here are some questions that you might address via sentiment analysis of text in ranking pages or videos:

  1. Are there discrepancies between the sentiment expressed in the top 10 results? – e.g. there is always a balanced view showing for a group of topically-relevant keywords – all positive or all negative, or there are some of each sentiment expressed.
  2. Are there patterns of where negative sentiment pages or videos appear in the SERP? – e.g. there is always a page or video with a negative sentiment about the topic, ranking in the top 10 results

Here’s how this analysis can shape content strategy:

  • If you see that Google likes to surface opposing views for products or service searches, ensure that there is a distinct opinion and sentiment towards the product expressed in your content
  • If you see that Google likes to surface negative results, and you see that for your target keywords, most of the results are positive in sentiment, write a content piece from a ‘limitations’ angle to offer a more competitive coverage of the topic

Use sentiment analysis to analyze feedback from your customers or users

Customer feedback is a goldmine of insights, directly influencing your brand’s roadmap and reputation. Leveraging sentiment analysis to dissect customer feedback can turn this raw data into actionable strategies, refining your user experience and product offerings.

Imagine sifting through thousands of customer reviews, social media posts, or survey responses manually—it’s not only time-consuming but prone to biases. Here, sentiment analysis can be a useful and powerful tool. By employing algorithms that can detect nuances in language emotion, you can systematically categorize feedback into positive, negative, or neutral sentiments.

Let’s consider a practical scenario: your company has recently launched a new product, and the initial feedback on social media is voluminous. Utilizing sentiment analysis, you can quickly gauge the general sentiment—Are customers thrilled about its features? Are there complaints about its usability? Is the sentiment around price overall negative? This immediate insight is invaluable, enabling you to respond swiftly to customer concerns or capitalize on positive buzz.

Moreover, this analysis can be segmented further. For instance, by breaking down feedback based on demographics or specific features of your product, you can uncover patterns and preferences unique to different user groups. Such segmentation can guide targeted improvements or marketing strategies.

An example of this in action could be analyzing user feedback on a new software update. Suppose the sentiment analysis reveals that users from a non-technical background are expressing frustration over the complexity of new features. This insight could prompt the creation of more user-friendly guides or even a reconsideration of the feature design to better suit your audience’s needs.

To get started analyzing first-party data for sentiment, think about the data you already have at your disposal at your organization and how this data can be used for digital marketing or organic marketing functions. Here are some examples:

  • If you are collecting data via feedback forms on your website: Analyse whether there are patterns in the text people are writing in those forms – common pain-points; content, product or service requests?
  • If you are collecting replies to your company emails, sent part of your email marketing strategy: Anonymise and analyze the collected data to extract content ideas or capture the general sentiment of the effectiveness of your email marketing campaign.
  • If you are regularly sending out customer surveys for service or product appraisal that have free-form text fields: Anonymise and analyze the collected text data for the sentiment expressed to identify common concerns with a product or service, or to identify product or service features that your users love. The loved features can become great assets for content marketing of the offering, as well as used as part of testimonials and other marketing materials.

In essence, sentiment analysis of customer feedback is more than just a reactive tool; it’s a strategic asset. It enables businesses to stay ahead of the curve, preemptively addressing issues and aligning product development with user expectations. You can also take this analysis a step further by granularly analyzing feedback for entities mentioned, and their associated sentiment.

Use sentiment analysis for content opportunity identification

Sentiment analysis can be effectively utilized to identify content opportunities that resonate with your target audience and align with their interests and needs. By analyzing the sentiment of comments, forum posts, or other text online, you can uncover hidden opportunities to capture an audience that does not currently have their needs addressed by the content that is out there.

Here’s how to harness sentiment analysis for content opportunity identification:

  1. Monitor online conversations:
  • Regularly track online conversations about your industry, competitors, and target audience through social media, forums, review websites, and news articles.
  • Analyze the sentiment associated with these conversations to identify topics, trends, and pain points that are generating positive or negative reactions.
  1. Identify gaps in existing content:
  • Analyze your existing content library to identify topics that are not adequately covered or areas where customer sentiment suggests a need for more detailed or specific content.
  • Prioritize content gaps that align with emerging trends or topics generating positive sentiment among your audience.
  1. Track competitor content and reactions:
  • Monitor competitor content, including blog posts, articles, and social media updates, to identify topics they are covering that are generating positive reactions from their audience.
  • Analyze sentiment associated with competitor content to identify opportunities to differentiate your content and address customer concerns or preferences that are not being addressed by competitors.
  1. Identify high-potential content formats:
  • Analyze sentiment associated with different content formats, such as blog posts, videos, infographics, and podcasts, to identify formats that resonate best with your audience and generate positive reactions.
  • Focus on creating content in formats that align with customer preferences and are likely to attract attention and engagement.

Use sentiment analysis to analyze YouTube comments and other social video interactions

In social video platforms like YouTube or TikTok, comments and interactions are not just feedback—they are rich, untapped sources of audience sentiment. Utilizing sentiment analysis in this context can unlock powerful insights, shaping the way you create and adapt your content strategy.

Imagine the time and effort it takes to leave a comment under your YouTube videos! Every reaction to your Facebook or Instagram video posts, is not just a string of words, but a meaningful indicator of your audience’s feelings and preferences. Sentiment analysis enables you to transform these interactions into quantifiable data, revealing the emotions behind them. Whether viewers are expressing joy, frustration, surprise, or disappointment, this analysis helps you understand the emotional pulse of your audience in real time.

For instance, consider a YouTuber analyzing comments on a series of tutorial videos. By employing sentiment analysis, they can quickly gauge the overall reaction—Are viewers finding the videos helpful and enlightening? Or are there aspects that are causing confusion or dissatisfaction? This can be crucial for refining the content strategy of videos.

Alternatively, are users commenting under videos that they would like the content in written format, too? This can be insightful for expanding the content strategy in different formats.

Incorporate sentiment analysis into competitive analysis

To gain a competitive edge in the dynamic digital landscape, it’s crucial to stay ahead of the curve by analyzing competitor sentiments. Sentiment analysis can be a powerful tool to identify your competitors’ strengths, weaknesses, and areas for improvement. By analyzing the sentiment associated with their brands, products, and services, you can gain valuable insights into their customer perception and market positioning.

Start by gathering data on how customers feel about your competitors’ offerings. This involves scrutinizing online reviews, social media mentions, and forum discussions about their products or services. Sentiment analysis tools can process this vast amount of data, categorizing feedback into positive, negative, or neutral sentiments. The goal is to uncover patterns—what aspects are customers consistently praising or complaining about? For instance, a competitor might be lauded for their customer service but criticized for product durability. These insights reveal areas for improvement in your own strategy and potential gaps in the market that you could fill.

Compare your brand’s sentiment against competitors to assess your overall performance and identify areas where you can differentiate yourself. This comparative analysis will help you understand how your brand is perceived relative to your competitors and pinpoint areas where you can enhance customer satisfaction and gain a competitive advantage. Maybe your products are well-received for their innovation but fall short in user-friendliness compared to competitors? These contrasts not only highlight areas for improvement but also opportunities to differentiate your brand in the market.

By understanding the emotional drivers behind customer preferences and perceptions, you can tailor your marketing strategies, product development, and customer service to appeal more effectively to your target audience, gaining a competitive edge. For example, if sentiment analysis reveals that customers feel strongly about sustainable practices, and this is an area where competitors are weak, focusing your efforts on sustainability can attract a more environmentally conscious customer base.

Moreover, sentiment analysis can help identify emerging trends and shifts in customer sentiment. Staying ahead of these changes allows you to adapt quickly, keeping your brand relevant and appealing in a constantly evolving market.

To implement this strategy, consider using tools like Brandwatch, Mention, or SEMrush, which offer comprehensive sentiment analysis capabilities. These platforms can track and analyze sentiment across various online channels, providing insights that can guide your competitive strategy.

Utilize sentiment analysis for influencer marketing

In the realm of influencer marketing, it’s crucial to partner with personalities who not only have reach but also resonate with your brand’s ethos. Sentiment analysis can play a key role in this assessment. By analyzing the sentiment associated with various influencers in your niche, you can gauge how their audience perceives them and whether this aligns with your brand’s values and target audience. This analysis helps in avoiding mismatches that could lead to ineffective campaigns or even brand damage.

Once you’ve identified influencers whose sentiment aligns with your brand, the next step is collaboration. These partnerships can extend your reach and positively influence your brand’s perception. For instance, if your brand is seen as innovative and an influencer is positively regarded for their cutting-edge content, a collaboration could amplify this perception among a wider audience.

The final, and perhaps most critical, step is measuring the impact of your influencer campaigns. Sentiment analysis of the content generated by influencers, as well as the feedback from their audience, provides valuable insights into the campaign’s effectiveness. This data goes beyond traditional metrics like views or likes; it delves into how the content emotionally resonated with the audience. Using the same tactics, discussed in the section about analyzing YouTube comments, analyze user comments received on the campaign and ask: Did it spark excitement, trust, or even controversy? Understanding this can help in refining future campaigns and strategies, ensuring a better alignment and impact with your target audience.

Key Takeaways

Sentiment analysis is a powerful tool that can be effectively employed to enhance various aspects of digital marketing and SEO strategies. Despite there being many ways to get started, one of the easiest for beginners is via the Google Natural Language API in Google Sheets, which requires no coding skills.

By harnessing sentiment insights, marketers and SEO professionals can gain a deeper understanding of customer sentiment, identify emerging trends, and optimize their campaigns for better results. From monitoring online reputation and tracking brand mentions to analyzing content and social media interactions, sentiment analysis offers a wealth of practical applications that can transform digital marketing efforts. By embracing sentiment analysis, businesses can gain a competitive edge, enhance customer satisfaction, and achieve long-term success in the ever-evolving digital landscape.