In our last article, we underscored the significance of sentiment analysis for gleaning valuable customer insights.
Today, we're going to dive deeper, turning our focus to the practical aspects of Sentiment Analysis Software. We'll explore the array of tools used for sentiment analysis and customer feedback interpretation and discuss how you can evaluate them to ensure you're picking the perfect data analytics software for your business needs.
We'll also help you identify the ideal software for your organization and introduce you to the top 5 sentiment analysis software currently showing high market potential.
What is sentiment analysis?
In a prior article entitled "Decoding Customer Feedback with Text & Sentiment Analysis", we delved into the nuances of sentiment analysis and its crucial role in interpreting customer feedback. However, for the sake of brevity and a quick refresher, we will provide a brief overview of this topic in this article as well.
Shortly, Sentiment Analysis is an intriguing area of research that delves into exploring public sentiments, attitudes, and emotions regarding various entities. These entities could range widely from products and services to individuals, organizations, events, or even specific topics of discussion.
What is sentiment analysis examples?
Sentiment analysis is a method used to analyze the emotions, attitudes, and opinions expressed in a piece of text. It's widely used in business, politics, and social science to understand people's perspectives on various topics. To put this in practice, here are a few examples:
- Product Reviews: Companies can analyze customer reviews to understand how people feel about their products. For example, if a new phone is released, sentiment analysis can be used to determine whether the majority of reviews are positive (e.g., "I love the new camera on this phone!") or negative (e.g., "The battery life on this phone is terrible.")
- Social Media Monitoring: Brands can use sentiment analysis to monitor social media platforms like Twitter and Facebook for mentions of their brand and assess whether the sentiment is positive, negative, or neutral. For example, a company might analyze tweets about its brand to identify and address negative emotions quickly.
- Customer Support: Businesses can use sentiment analysis to categorize customer support tickets or emails by sentiment. This can help prioritize responses (e.g., addressing negative sentiments first) and assess overall customer satisfaction.
- Political Analysis: In politics, sentiment analysis can be used to assess public opinion on certain issues or candidates. For example, sentiment analysis could be used to gauge public sentiment toward a proposed policy based on social media posts.
- Market Research: Companies can use sentiment analysis to understand public sentiment towards their brand, their competitors, or the market as a whole. For instance, a car manufacturer might analyze online discussions about electric cars to understand public sentiment toward this technology.
- Voice of the Customer (VoC) Programs: Sentiment analysis can help businesses understand the overall sentiment in customer feedback, identify trends over time, and uncover specific areas for improvement.
What tool is used for sentiment analysis?
Many software tools and libraries, each with unique strengths and applicable scenarios, are available for sentiment analysis. These tools harness the power of cutting-edge technologies like Natural Language Processing (NLP), machine learning, and artificial intelligence to dissect, comprehend, and categorize sentiments embedded in the text.
- Software Platforms: Various software platforms, like Brandwatch, Hootsuite, Sprout Social, and Talkwalker, offer sentiment analysis as a component of their social media management and customer experience suites. These platforms are typically easy to use, boasting user-friendly interfaces.
Pros:
- User-friendly interfaces make these platforms easy to use even for non-technical users.
- Integration with social media channels and other customer experience tools provides a holistic view of customer sentiment.
- They often come with additional features like social listening, trend analysis, and reporting tools.
Cons:
- These platforms can be pricey, particularly for small businesses or startups.
- Customization capabilities might be limited compared to other options.
- Data privacy can be a concern as you're sharing customer data with a third-party platform.
- Cloud-based AI Services: Many cloud service providers, including ClientZen, IBM Watson, Google Cloud's Natural Language API, and Amazon's Comprehend, provide sentiment analysis services that can be seamlessly incorporated into your existing software solutions.
Pros:
- They offer advanced AI capabilities without requiring in-house AI expertise.
- Scalability is a key advantage, as these services can handle large volumes of data.
- Integration capabilities allow these services to fit smoothly into your existing tech stack.
Cons:
- Recurring costs can add up over time.
- These services might require some technical expertise to integrate and use effectively.
- Dependence on internet connectivity as these are cloud-based solutions.
- Programming Libraries: For a more bespoke approach, numerous programming libraries enable you to create your sentiment analysis tool. Some notable examples are NLTK, TextBlob, VaderSentiment for Python, and Stanford's CoreNLP for Java.
Pros:
- Extremely customizable, allowing you to tailor the tool to your specific needs.
- Generally, programming libraries are free to use.
- You maintain full control over your data, alleviating privacy concerns.
Cons:
- Requires significant technical expertise to use effectively.
- Building and maintaining your own tool can be time-consuming.
- These solutions may not provide the same level of customer service assistance or possess as intuitive an interface as seen with commercially available software options.
- Open-source Tools: Open-source platforms such as RapidMiner, Knime, and Orange also offer sentiment analysis capabilities, providing ample room for customization.
Pros:
- Open-source tools are usually free to use.
- They often have an active community for support and regular updates.
- These tools allow for extensive customization to suit your needs.
Cons:
- They often require technical expertise to use to their full potential.
- Open-source tools might not be as polished or user-friendly as commercial software.
- Support might not be as readily available or as comprehensive as it is with commercial options.
The selection of the appropriate tool hinges on several factors, including your particular requirements, budget, the volume of text data you need to process, and the precision you desire in the results.
Sentiment analysis and its relevance in business.
Often termed as opinion mining, sentiment analysis is a key player in the realm of business intelligence tools. It establishes a vital link between businesses and the thoughts and emotions of their customers. In the broad universe of customer feedback, sentiment analysis acts as a navigational tool, directing companies toward meaningful insights that can substantially improve their offerings.
Sentiment analysis works its magic on unstructured data - such as social media chatter, customer reviews, and survey results - to extract hidden sentiments. Whether these feelings are positive, negative, or neutral, they all carry significant value for a business. They offer an unfiltered view of how customers perceive a company's products or services, what they value, and areas they think could use enhancement.
The advantages of deploying sentiment analysis software extend well beyond just decoding customer feedback. It can also offer a competitive advantage in a constantly evolving market. By keeping track of customer sentiments, businesses can stay a step ahead of market trends, predict customer requirements before they manifest openly, and proactively fine-tune their strategies.
How do you determine customer sentiment?
Customer sentiment can be determined through a variety of methods and tools. Sentiment analysis, which is part of the broader field of Natural Language Processing (NLP), is a primary method used to understand and classify customer sentiments. Let's outline the following steps that you might take into account as a general process that is used in best practice to determine customer sentiment:
- Collect Customer Feedback: The first step, as in any analytical approach, is to collect data, in our case, customer feedback. This could be in the form of online reviews, social media posts, emails, survey responses, or any other type of customer communication.
- Preprocess the Data: This step involves cleaning the text data and preparing it for analysis. If you're processing data manually, this may include removing irrelevant information, correcting typos, converting text to lowercase, etc. If you are already using customer feedback software, this is done automatically.
- Sentiment Analysis: This is where the actual sentiment analysis happens. Depending on the complexity of the tool or method being used, this could involve:
- Polarity Detection: Identifying whether the sentiment in the text is positive, negative, or neutral.
- Emotion Detection: Going a step further to identify specific emotions like happiness, sadness, anger, surprise, etc.
- Aspect-Based Sentiment Analysis: Identifying specific aspects or features mentioned in the text (e.g., "price," "customer service") and the sentiment towards each of them.
- Interpretation and Action: The final step is to interpret the sentiment analysis results and take appropriate action. This could involve addressing negative feedback, identifying improvement opportunities, or celebrating positive feedback with your team.
Best sentiment analysis tools in 2023.
- ClientZen
Delivering top-tier sentiment analysis, ClientZen.io is a smart, budget-friendly choice. Its intuitive dashboard ensures efficient usage, revealing insights quickly and affordably.
Key features include Automated Tagging, which uses AI to categorize customer feedback, and Insight Discovery, which uncovers data-driven insights from customer responses. The Sentiment Analysis feature deciphers the intention behind feedback, while User Segmentation lets you tailor products to specific customer needs.
Unique offerings like MRR Tracking and Release Impact Tracking provide financial and product insights based on customer sentiment. Plus, with automation, ClientZen offers a time-saving, cost-effective sentiment analysis solution. Its blend of affordability, user-friendliness, and rich features make ClientZen an industry-leading choice.
- MonkeyLearn
MonkeyLearn provides a collection of text analysis services, one of which is a sentiment analysis tool, well-regarded for its exceptional precision.
The MonkeyLearn product range integrates smoothly with utilities such as Zendesk and Google Sheets, similar to ClientZen. For those well-versed in coding, the MonkeyLearn sentiment analysis API can be employed to connect to your existing tech stack.
Furthermore, there's the potential to craft a custom-made sentiment analysis model without the necessity to write any code. A significant advantage of devising your own sentiment analysis tool lies in its improved accuracy.
- Critical Mention
Critical Mention is a tool designed to capture the emotion within social media mentions. Its real-time tracking feature enables users to observe their brand mentions closely, distinguishing between positive mentions and potential issues.
This tool allows users to monitor mentions and brand-specific terms on various platforms including Facebook, YouTube, and Twitter, and also offers keyword searches pertaining to particular topics. Sentiment analysis features can be used for assessing brand health within the marketplace and conducting competitive analyses. For a more cohesive workflow, Critical Mention can be integrated with other tools via its API.
4. Sentiment Analyzer
Utilizing Sentiment Analyzer is a straightforward process. Users visit their website, copy the text they wish to analyze, and paste it into the provided box. After selecting "Analyze!", the platform processes the text and generates a "sentiment score."
Far from magic, Sentiment Analyzer employs "computational linguistics and text mining" to ascertain the sentiment encompassed within a piece of text.
The platform then amalgamates and contrasts its findings to generate an overall score. Its ability to quickly decode the intention behind a complex response from a customer makes it a potentially useful resource.
5. Brand24
Brand24 operates as a media monitoring tool that automatically conducts sentiment analysis related to your brand or any relevant business topic.
The tool gathers publicly accessible mentions from a variety of online sources, including social media platforms, blogs, forums, websites, news sites, customer reviews, videos, podcasts, and others.
Brand24 employs advanced machine learning algorithms and NLP techniques to analyze text in real-time, assigning the pertinent sentiment based on the words used.
With its sentiment analysis feature, users can rapidly ascertain if mentions are positive, negative, or neutral. Its comprehensive analytics capabilities offer insights into the frequency of brand-related discussions, social media reach, key influencers, and much more.
If you're driven by cost-effectiveness and feature-richness, we advise that you juxtapose your prospective choices. Evaluate to determine the most advantageous customer feedback software or sentiment analysis tool for your organization, just as illustrated here.
How do test Sentiment Analysis tools?
Like any investment, ensuring that the sentiment analysis solution you're considering aligns with your standards, objectives, and anticipations is crucial. It's always beneficial to take advantage of free trials, demos, and sample tools offered by most sentiment analysis and customer feedback software providers to grasp what they can deliver.
Let's illustrate this with a practical example by examining two platforms (MonkeyLearn and ClientZen) offering free tools on their platform, providing us a glimpse into their functionality.
We selected a random piece of feedback for this demonstration:
"The discourse with DEF was like a dance, wrapped in an enigmatic charm, showing a rare finesse in navigating troubled waters with the grace of an adaptive chameleon. At the helm, there was an air of tranquility, not without its share of cryptic serenity. The customer journey was a predictable play, lacking the thrill of plot twists but comforting in its even narrative.
The sands of time slipped occasionally, their rhythm dissonant with our agreed symphony. The price tag, with its surrealist tendencies, was a puzzle occasionally evading resolution. However, the final act was a masterpiece. The product was akin to a Swiss timepiece, flawless in function and form, a testament to DEF's artisan spirit, and a captivating climax to a unique engagement."
This text, generated by Artificial Intelligence, aimed to create a neutral sentiment review with subtly negative undertones about the pricing and customer experience, counterbalanced by positive sentiments toward the company.
As observed, ClientZen's free sentiment analysis tool successfully deciphered the subtle negative sentiment regarding price and customer experience, highlighted the positive sentiment towards the company and the final product, and identified the overall neutral sentiment conveyed in the text. On the other hand, MonkeyLearn interpreted an overall negative sentiment.
Free sentiment analysis tools like these can provide a general understanding of how sentiment analysis software processes your data and the type of insights it could potentially provide. This is particularly useful if you're hesitant about immediately diving into a free trial but are considering integrating a customer feedback or sentiment analysis tool into your daily operations to unearth and address customer sentiments, thereby enhancing your product or service line.
Which sentiment analysis tool is best for your business?
Choosing the best sentiment analysis tool for your business depends on a range of factors, including the size of your business, your budget, your technical capabilities, and the specific needs of your business.
For example, large corporations with a high volume of customer feedback might prefer a robust software platform like ClientZen, which provides comprehensive features such as Automated Tagging, Insight Discovery, Sentiment Analysis, and User Segmentation.
On the other hand, smaller businesses or startups might prefer open-source tools or programming libraries, which allow for customization and cost-effectiveness, although these might require more technical skills to set up and maintain.
Cloud-based AI services are also a popular choice due to their scalability, seamless integration capabilities, and the high level of accuracy they can provide.
Ultimately, the best sentiment analysis tool for your business is the one that aligns most closely with your business goals, customer feedback volume, and technical resources. It's always advisable to try out a few different tools using their free trials or demo versions before making a final decision.
See how sentiment analysis works
needed
on your dataset
analysis works
Conclusion
As we've journeyed through the landscape of sentiment analysis, it's clear to see its powerful role in a business's success. We've delved into the workings of the tools available and explored ways to ensure you're making the right choice for your company. From enhancing customer service to gaining a competitive edge, the benefits of sentiment analysis are indeed profound.
While each tool has its merits, our exploration wouldn't be complete without a nod to ClientZen. Designed with user needs in mind, ClientZen combines a plethora of powerful features into an easy-to-use, cost-effective solution. It's a tool that not only understands the emotions of your customers but also translates qualitative data into actionable steps that can bring about real change in your business.
In closing, the key to mastering sentiment analysis lies in understanding your business needs and finding a tool that aligns with those needs. We hope this article has brought you one step closer to making an informed decision.
Capitalize on the transformative potential of ClientZen today to deepen your understanding of your customers! To learn more about how ClientZen employs AI to pinpoint customer pain points 6X quicker, schedule a demo. Or, get a firsthand experience of the tool by setting up a free account here.
Customer feedback made easy
Customer feedback tagged automatically
Real-time customer sentiment scores
Pain-points evolution over time
