Sentiment – it’s more than a nine-letter word for a thought backed by passion or feeling; it’s also a leading cause of crises in consumer relations. Sentiment as it pertains to your customers describes the perception they have of your business and its practices, as well as its various offerings.
Positive sentiment towards your brand can significantly boost its performance and bottom line. However, should expressed sentiment veer off course and land in negative space, your brand could suffer needlessly and lose revenue abruptly.
Understanding sentiment among your customers and analyzing it properly through interaction analytics and other tools can prove to be integral to establishing a sustainable growth trajectory for your organization. Read on to learn more about sentiment analysis and the tools you can use to simplify it.
Understanding Sentiment Among Consumers
Sentiment manifests among consumers as the perceived mood or emotion their thoughts express regarding your brand.
Negative sentiment is easy to recognize and tends to focus on a company’s faults, highlighting recent failures to accommodate the individual’s needs or hinging on its history of the same. Positive sentiment is far better, of course, and is also relatively simple to pick out. Positive sentiment centers on emotional satisfaction with products, services or the company itself.
What is Sentiment Analysis?
Sentiment analysis is the process by which an organization can filter, as well as measure, the balance of positivity and negativity that customers and others express about its identity.
It is through sentiment analysis that a concise representation of the public’s perception of your business can be crafted and acted upon. The process is automated through a variety of tools that parse data pulled from social media, surveys and more, segmenting such information by its overall emotional content. Many of the tools available for this purpose make use of advanced technology including machine learning techniques and AI more to effectively assess communication that takes place across a variety of channels.
As consumers become more aware of their options in a growing global marketplace, the call for a more comprehensive approach in understanding their needs has given rise to widespread use of sentiment analysis. In this buying guide, we cover the basics of sentiment analysis and provide actionable advice for choosing the best tools to handle the process.
Expert Advice for Choosing Sentiment Analysis Tools
1. Choose tools that can assess more complex expressions for better results.
“Using inaccurate sentiment analysis data can prove catastrophic. Choosing the right tool is essential. A tool that gets sarcasm. That reads the comment from John Doe and understands that he’s being sarcastic. He’s having a pop. He’s being negative. So you have to choose the best.” – Meg, The Best Sentiment Analysis Tools, TalkWalker; Twitter: @talkwalker
2. Do not discount free options.
“Sentiment analysis tools can be invaluable as far as reputation management is concerned. Even using the free ones, with all their limitations, can save your business from potential PR crises and financial losses.” – The 17 Best Sentiment Analysis Tools, Brand24; Twitter: @brand24
3. Try before you buy and gauge how much you actually need to assess.
“Volume of material: Estimate the amount you want to analyze – if your company is truly hoping for a deep, across-the-market sentiment analysis, you will need a larger, more robust tool.
“Test the software: Perhaps this one goes without saying, but…does the software give an accurate view of the market’s opinion? That is, if you look at the text yourself, does the software agree with human understanding?” – James Maguire, Top 8 Sentiment Analysis Tools, Datamation; Twitter: @datamation
4. Opt for options with incorporated report generating features.
“If you think about it, you have this awesome software that can magically turn unstructured data into structured data. Great! You know how it works, but are you able to show your co-workers and superiors that it’s working and what it means?
“Most of us working in business have some KPIs or metrics that we are working towards. So, it’s good if the analysis software you invest in allows you to you link the data received from customers to these KPIs or metrics (perhaps NPS or CSAT) that your company is trying to achieve. So that you can see what is driving the metric up and down and take action if necessary.” – Sofia Ohlson, How to Choose the Right Text Analysis Software, Lumoa; Twitter: @LumoaMe
5. Choose options that fit your project’s scope, goals and existing tools.
“Sentiment analysis is one of the hottest topics and research fields in machine learning and natural language processing (NLP). The possibility of understanding the meaning, mood, context and intent of what people communicate can offer businesses actionable insights into their current and future customers, as well as their competitors.
“Constructing an enterprise-focused sentiment analysis system out of the best available frameworks means making some hard choices about the scope, scalability, architecture and ultimate intent of your project.” – Martin Anderson, Choosing a Python Library for Sentiment Analysis, Iflexion; Twitter: @iflexion
6. Choose tools with keyword generating features.
“Research what your audience is searching, posting, and following on social media. Consider industry trends as well to develop a list of keywords and tags to monitor. This list should change over time to remain relevant. Most sentiment analysis tools provide help finding keywords and similar queries, or you can use free tools like Answer the Public.” – Brianne Schaer, Use Sentiment Analysis Win the Brand Comparison War, Business2Community; Twitter: @b2community
7. Look for tools that can handle many different data sources.
“Text analysis tools can consume text data from a variety of sources, including emails, phone transcripts, surveys, customer reviews, and other documents. By importing text data from these different sources, businesses are better equipped to understand and analyze customer or employee sentiment, intelligently classify documents, and improve written content. Text analysis software may be used in conjunction with other analytics tools, including big data analytics and business intelligence platforms.” – Best Text Analysis Software in 2019, G2; Twitter: @G2dotcom
8. Lexicon-leveraging tools may provide better results than machine learning options.
“The results of an ANOVA indicated that the lexicon-based approach (VADER) to sentiment analysis outperforms machine-learning in almost all product contexts regardless of review length[…]The results of this study provide evidence that there are differences in the accuracy of various SA tools, and that it is important to consider contextual factors when choosing a SA tool.” – A. Kiani, S. Natour and O. Turetken, A Comparison of Sentiment Analysis Tools, AIS
9. Determine whether sentiment analysis is the kind of text analysis you need.
“[…]text analysis in businesses often takes one of five key forms:
- Summarization: Trying to find key content across either a range of sources or a single document
- Sentiment analysis: Assessing the tone, intent, and social context that’s relevant to a document
- Explicative: Finding the reason for said sentiment analysis in a given document
- Investigative: Reviewing the sources of a specific issue
- Classification: Confirming the subject(s) that a text source discusses”
10. Consider whether your chosen tool is trained extensively if it uses AI.
“A good sentiment analysis tool should be able to accurately identify each tweet. But how do you get there? You have to provide thousands of examples of pre-labelled data to train your system. This manual annotation of sentences forms the basis of machine learning. The more data and the better labeled it is, the more accurate the tool.” – Meiryum Ali, What is Sentiment Analysis?, Lionbridge; Twitter: @LionbridgeAI
11. Lexicon-based options require constant maintenance for consistent accuracy.
“Traditional sentiment analytics leverages manual configuration. Also known as Rule-Based Text Classification, manual configuration involves leveraging keyword dictionaries and lexicons to teach a system how it should assign sentiment and topics to text.
“A pitfall of this approach is that keyword dictionaries and lexicons must be continuously updated manually to ensure the system correctly categorizes any future texts it analyzes.” – Sentiment Analysis, iPerceptions; Twitter: @iPerceptions
12. Consider options that draw on semantic analytics strengths as well.
“Sentiment analysis and semantic analysis have similarities and differences. Semantic analysis basically studies the meaning of language and how the language can be understood. It can be used to extract relevant and useful information from large amounts of text and thereafter analyze the information. Sentiment analysis basically measures emotions behind the information studied.” – Akshata Chandrasekhar, Sentiment Analysis – All You Need to Know About It, MarketMotive; Twitter: @MarketMotive
13. Look for tools that allow you to train them manually.
“It’s important to remember the extent to which sentiment analysis improves your business will depend on how far you’re willing to go when integrating it with your systems. Most large businesses that want to reap the full rewards of sentiment analysis actually train their own tools with either private data or data specific to their domain. While this takes longer to build out than simply purchasing a general tool, it’s worth the time and effort.” – Charly Walther, Sentiment Analysis in Marketing: What Are You Waiting For?, CMSwire; Twitter: @CMSWire
Expert Advice for Leveraging Sentiment Analysis Tools
14. Try analyzing your competition, too.
“Let’s say you’re a company that makes shoes. You might want to take a look at the social sentiment of brands like Nike, Adidas, or Puma to see what they’re doing to bring in positive sentiment. And, more importantly, what they’re doing to bring in negative sentiment.
“What types of social media posts are audiences responding to? Are they funny? Serious? Helpful? How can you emulate the posts with positive sentiment on your social account?” – Tony Tran, A Guide to Social Media Sentiment (Includes 5 Sentiment Analysis Tools), Hootsuite; Twitter: @hootsuite
15. Use sentiment analysis to take action in real-time.
“We can use sentiment analysis to identify critical information that allows situational awareness during specific scenarios in real-time. Is there a PR crisis in social media about to burst? An angry customer that is about to churn? A sentiment analysis system can help you immediately identify these kinds of situations and take action.” – Sentiment Analysis: The Only Guide You’ll Ever Need, MonkeyLearn; Twitter: @monkeylearn
16. Pair sentiment analysis with other ROI metrics for greater utility.
“Measuring the ROI of marketing campaigns can be a real challenge. By gathering sentiment during a marketing campaign, businesses can unearth insights into how prospective customers feel about a brand. Marketers can then measure how that sentiment changes over the course of the campaign. They can also find the demographic segments that most closely align with a brand, their buying habits, and how to communicate more effectively to them. All of this allows teams to tailor their campaigns better to improve perceptions, set expectations, and scale for the best ROI.” – What Is Sentiment Analysis and Why Is It Important?, Ascribe; Twitter: @GoAscribe
17. Free up time and energy by complimenting other processes with sentiment analysis.
“If your company provides an omni-channel experience, a sentiment analysis tool can save your team valuable time organizing and reporting customer feedback. Rather than going through each tweet and comment one-by-one, a sentiment analysis tool processes your feedback and automatically interprets whether it’s positive, negative, or neutral. Then, it compounds your data and displays it in charts or graphs that clearly outline trends in your customer feedback. This not only gives your team accurate information to work with but frees up time for your employees to work on other tasks in their day-to-day workflow.” – Clint Fontanella, The Best 8 Sentiment Analysis Tools in 2019, Hubspot; Twitter: @HubSpot
18. Use sentiment analysis to track your customers’ brand perceptions as they develop.
“Here’s why so many are using sentiment analysis: They can identify negative mentions and comments, allowing them to assess the situation and see if they are able to make improvements to the way their company is run.
“They can track customer reactions to changes in products, as well as try to stop social media crisis from occurring.
“They can track users that are incredibly happy, showing them where they are going right, but also giving them the chance to find potential brand ambassadors for further promotion of their products.” – Milosz Krasinski, Sentiment Analysis: Turning Your Social Media into a Marketing Dream, State of Digital; Twitter: @stateofdigital
19. Take neutral feedback seriously.
“Sometimes people share their points of view without emotions. For instance, the author of the sentence I think everyone deserves a second chance expresses their subjective opinion. However, it’s hard to understand how exactly the writer feels about everyone. So, the sentence doesn’t express a sentiment and is neutral. Neutral sentences – the ones that lack sentiment – belong to a standalone category that should not be considered as something in-between.” – Sentiment Analysis: Types, Tools, and Use Cases, AltexSoft; Twitter: @AltexSoft
20. Use sentiment analysis to monitor the impact of new offerings.
“One of the most well documented uses of sentiment analysis is to get a full 360 view of how your brand, product, or company is viewed by your customers and stakeholders. Widely available media, like product reviews and social, can reveal key insights about what your business is doing right or wrong. Companies can also use sentiment analysis to measure the impact of a new product, ad campaign, or consumer’s response to recent company news on social media.” – Introduction to Sentiment Analysis, Algorithmia; Twitter: @algorithmia
21. Assess feedback altogether instead of as isolated cases.
“Bear in mind that your mentions, whether they be positive or negative, don’t happen in a vacuum. Rather than obsess over a one-off compliment or complaint, brands should look at the bigger picture of their customers’ feelings. For example, a flurry of praise is definitely a plus…” – Brent Barnhart, The importance of social media sentiment analysis (and how to conduct it), SproutSocial; Twitter: @sproutsocial
22. Use sentiment analysis software to improve consistency of interpretation overall.
“We all read things differently, and we only really agree on the sentiment behind text around 60% of the time. An algorithm like this one means that the tone is no longer seen in a subjective manner and a more accurate reading can be taken.” – Craig Campbell, Sentiment Analysis: The Only Guide You’ll Ever Need, Craig Campbell SEO; Twitter: @craigcampbell03
23. Try to assess data from a variety of available sources whenever possible.
“There are many sources of public and private information out of which you can harness an insight into the customer’s perception of the product and general market situation. To name a few:
- Customer support correspondence (regarding your product)
- User-generated Product reviews
- Professional product reviews (as in The Verge or Wired)
- Social Media tractions
- General and special-purpose forums”
– What is Sentiment Analysis: Definition, Key Types and Algorithms, The APP Solutions; Twitter: @TheAPPSolutions
24. Curb a potential public crisis more effectively (and quickly).
“Sentiment analysis allows you to track online mentions in real time, making it a helpful tool for identifying a potential PR crisis that may be unfolding. If you see a spike in negative sentiment, you can investigate it further and, if needed, take immediate action to defuse it.” – Milosz Krasinski, Sentiment Analysis: What Marketers Need to Know, Social Media Examiner; Twitter: @SMExaminer
25. Keep timing in mind when assessing sentiment of certain user groups.
“[…]opinions may change over time, depending on a lot of factors, both subjective (e.g. user perception) or objective (e.g. change in pricing). Hence, it’s imperative that not only sentiment but also time is taken into account during sentiment analysis.” – Ben Ellis, On Social Sentiment and Sentiment Analysis, brnrd.me; Twitter: @FR314
Has your company improved its offerings through sentiment analysis?