Customer experiences can determine a business’s success. Recent studies show that 61% of consumers are willing to pay a premium of at least 5% for a guaranteed positive experience. As a result, many companies are using advanced technologies to better address customer sentiment.
Customer sentiment provides insights into how customers perceive a brand. It can guide businesses in tailoring their strategies to meet customer expectations.
In this article, we will explore customer sentiment analysis, highlighting its role in modern business. Additionally, you will learn how to leverage AI-driven tools for customer sentiment analysis and to drive organizational success.
Customer sentiment refers to the overall attitude, feeling, or emotion of a customer towards a particular brand, product, or service. It usually derives from various channels.
Sentiment analysis, often termed “opinion mining,” is the process of determining and extracting subjective information from written or spoken language. Sentiment analysis categorizes opinions expressed in a piece of text as positive, negative, or neutral.
Here’s how customer sentiment analysis typically works.
Businesses collect customer feedback and comments from different channels. Comments can be in the form of text, emojis, or ratings.
The collected text data is “cleaned” and prepared for analysis. This may involve tasks like removing punctuation, converting text to lowercase, and tokenization (splitting text into individual words or tokens).
The core of customer sentiment analysis involves classifying each piece of text into one or more sentiment categories.
Businesses can choose from various techniques to analyze customer sentiment.
Each piece of text is assigned a sentiment score, indicating the strength and polarity (positive, negative, or neutral) of the sentiment expressed. Scores may range from a numerical value, such as −1 (negative) to 1 (positive), or they may be represented as categorical labels.
Analysts often aggregate and visualize sentiment analysis results to generate insights at various levels, such as:
Businesses use the results of sentiment analysis to make data-driven decisions. Positive sentiment may indicate areas of success businesses should leverage, while negative sentiment may highlight areas for improvement or customer concerns that need addressing.
Here are the most important benefits of customer sentiment analysis.
Before the advent of artificial intelligence (AI) tools, businesses relied mainly on methods like surveys, feedback forms, and focus groups to gauge customer sentiment. Sentiment analysis was predominantly rule-based, relying on predefined lists of words and manual categorizations.
The results, though useful, were often rudimentary and couldn’t handle complexities like sarcasm or context-based sentiments.
👎 Traditional customer sentiment analysis methods are often time-consuming, lack real-time responsiveness, and can miss out on spontaneous sentiment shared on platforms like social media or review sites.
With the advent of machine learning and deep learning in the late 2000s and early 2010s, the field of sentiment analysis experienced a significant shift. Systems began to “learn” from vast amounts of data, understanding context, tone, and even cultural nuances.
AI models started to decode human emotions with remarkable accuracy. This has led to the widespread adoption of AI-driven sentiment analysis tools by businesses worldwide. They enable businesses to get a more comprehensive and timely understanding of how their customers feel.
As AI-driven customer sentiment analysis tools continued to evolve, they became more powerful and versatile. Businesses can now gain even deeper insights into customer sentiment and respond more effectively to customer feedback.
💬 Modern sentiment analysis models are capable of handling the subtleties of language. They can recognize human feelings expressed in various forms.
The integration of AI into customer sentiment analysis has brought about more depth, accuracy, and efficiency. It has revolutionized how businesses understand and respond to their customers’ feelings and opinions.
These are the AI technologies that have proven to be most impactful in the analysis of customer sentiment:
NLP is a branch of AI that focuses on the interaction between humans and computers using natural language. Its main aim is to read, decipher, and understand human languages in a manner that is valuable.
In the case of sentiment analysis, NLP helps in identifying emotions, intentions, or opinions from text data. It converts unstructured data into actionable insights.
Machine learning algorithms are trained on labeled data (data where sentiment is already defined) to recognize patterns and relationships.
Once trained, they can analyze new, unseen data and classify it based on the learned patterns. Common algorithms used include decision trees, random forests, and neural networks.
Here is how machine learning helps in gauging customer sentiment:
Chatbots and virtual assistants interact directly with users and can collect feedback in real time. This immediate interaction can help businesses gauge sentiment instantly — no need to wait for post-interaction feedback.
AI-powered chatbots and virtual assistants can modify their responses based on the sentiment detected:
Now that you are aware of how you can benefit from using AI in sentiment analysis, it is time to select the ideal tool for your needs. In this segment, we will explore the top customer sentiment analysis tools and provide tips to help you choose between them.
Image source: Brandwatch.com
Brandwatch is a social media monitoring and consumer intelligence platform. It is used by organizations to track and analyze their online presence and brand reputation.
Businesses leverage Brandwatch to track real-time online mentions, sentiment shifts, and brand visibility. The platform offers machine learning classifiers, entity disambiguation for in-depth data analysis, and a distinctive “image insights” feature to monitor logo shares across the Internet.
Image source: Rosette.com
Rosette is an NLP and text analytics platform developed by Basis Technology. It is designed to analyze and process text data in multiple languages. By leveraging it, organizations can extract valuable insights from unstructured textual information.
Rosette is a great tool for examining social media data and specific mentions of products or entities. It excels in morphological analysis and lemmatization, ensuring accurate sentiment understanding.
Rosette can be trained to recognize customer sentiment in up to 30 languages.
Image source: BrandMentions.com
Social Mention is a social media monitoring and social listening tool. It allows users to track and analyze mentions of specific keywords, brand names, or topics across various social media platforms and online sources. Moreover, Social Mention provides real-time monitoring and analytics of social media content, including:
By entering a keyword, users get a comprehensive sentiment breakdown. This includes the overall mood, the sentiment per mention, and the count of positive, negative, or neutral comments associated with that keyword.
Image source: CriticalMention.com
Critical Mention is a cloud-based service that incorporates real-time monitoring and database capabilities. It allows businesses to track customer sentiment over:
Professionals in public relations, marketing, and communications rely on this global media intelligence platform to gain insights into their earned media performance. They can generate personalized, easily shareable reports based on that performance.
Source: Hootsuite.com
Hootsuite Insights is a social listening tool. It enhances brand strategy by seamlessly integrating with all social media networks to provide immediate access to real-time online conversations. Businesses can analyze sentiment by monitoring customer discussions about their brand and set alerts for unusual negative sentiment spikes or trending topics.
What’s more, users can assign tasks to team members for necessary actions or responses. They can utilize filters like date, demographics, and geography to access specific data when and where it’s needed.
Source: MonkeyLearn.com
MonkeyLearn is a text analysis and NLP platform that offers a range of tools and services for extracting insights from text data. It allows users to build custom machine learning models without the need for extensive coding or data science expertise.
MonkeyLearn can be used for various text analysis tasks, including:
Source: RapidMiner.com
RapidMiner is a data science and machine learning platform that provides a comprehensive set of tools for data preparation, analysis, modeling, and deployment. It is designed to help data scientists, analysts, and business professionals leverage data to make better decisions and build predictive models.
Industries like finance, healthcare, retail, and manufacturing actively use RapidMiner. Tasks include:
The era of AI-powered customer sentiment analysis has ushered in a transformative approach to customer relationship management. Listening to, understanding, and responding to the voice of the customer is now more attainable than ever before.
Adopt and adapt AI-driven sentiment analysis tools to unlock a treasure trove of insights from customer feedback, reviews, and social interactions.
Content Marketing Manager, passionately blending her expertise in content strategy, UX, and data analysis to drive impactful results. With a keen eye for SEO strategies, Cezarina focuses on optimizing content for maximum visibility and audience reach.
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