Understanding Customer Emotion and Intent With Sentiment Analysis
Identifying the underlying emotion and intent in unstructured text can be invaluable for marketing and customer support teams. Sentiment analysis makes understanding emotion and intent possible. Sentiment analysis is an AI-enabled technology that mines subjective data sources such as online product reviews, social media posts and interactions with support agents for valuable insights. Let’s look at the various types of sentiment analysis and how they’re being used to solve business problems across a variety of industries. We’ll share several commonly encountered hurdles that make accurately extracting value from unstructured text data more difficult—and techniques for overcoming them.
What is sentiment analysis?
Sentiment analysis, sometimes referred to as opinion mining, is the process of analyzing digital text at scale to uncover the emotional tone—or sentiment—of the message. This technique is a subset of AI that combines natural language processing (NLP), machine learning (ML) and data mining to accurately determine the thinking, attitude or emotions behind unstructured text data. As video and audio data become more common, methods for extracting sentiment from these data types are also being developed.
Types of sentiment analysis
Sentiment analysis extends far beyond simply gauging how customers feel about a brand, product or service. This versatile technique can be used for everything from basic polarity detection to more advanced emotion and aspect-based analysis.
Intent-based sentiment analysis
Intent-based analysis is used to identify the underlying meaning contained within text, uncovering the intention behind a user’s statement. Gathering information using this method may include categorizing the text as a question, statement, opinion or desire; detecting the sentiment it conveys; and ranking the intensity with which it is being expressed. For example, a company might evaluate employee feedback on recent changes to benefits or compensation and the intensity behind those responses.
Aspect-based sentiment analysis
Aspect-based sentiment analysis categorizes data by aspect, identifying the sentiment attached to a specific category, feature or topic. This is a more advanced form of sentiment analysis used to identify how customers feel about a certain aspect of a product or customer service experience. Examples include identifying how users felt about a newly added feature and gauging how effective a customer support representative was at resolving an issue.
Fine-grained sentiment analysis
As the name implies, fine-grained sentiment analysis extracts specific opinion elements from text. This form of sentiment analysis moves beyond more basic methods that classify sentiment simply as positive, negative or neutral. It divides text intent into multiple levels and incorporates more nuanced opinion elements to reveal user preferences as they relate to specific product aspects and corresponding emotional words. An application of this approach could include analyzing online news coverage to gauge how people perceive and react to specific events.
Emotion detection
Emotion detection involves identifying specific emotional states such as anger, fear, sadness and excitement. This approach may be used to monitor social media channels for customer reactions to new products or service launches.
Challenges to effective sentiment analysis
Written language and the meaning behind it is full of complexity. Teasing out contextual, cultural and linguistic nuances to arrive at an accurate conclusion can be a significant challenge. But these barriers can be overcome, including through these four methods.
Detecting negative text
Text that includes negation doesn’t necessarily convey a negative sentiment. A statement such as “This vacuum does not leave pet hair on the couch like my old one!” is actually a positive assessment of a product’s performance. But sentiment analysis algorithms can struggle to interpret text that contains negations accurately. Using large, annotated datasets that include a broad number of negation words or phrases during model training can improve the ML model’s ability to distinguish between negated and non-negated text.
Understanding context
Human language is filled with complexity. Sentiment analysis tools can struggle with text where the meaning is context-dependent. Irony and sarcasm are two examples that present significant challenges. Training models to use contextual clues such as linguistic markers, punctuation, emojis and hashtags to detect and interpret statements that contain irony or sarcasm can help them label content-dependent text with greater accuracy.
Accommodating language and cultural differences
Many organizations operate in multiple countries, making it essential to account for linguistic and cultural differences when conducting sentiment analysis. Many of today’s natural language processing systems provide support for multiple languages, streamlining the process of analyzing user content written in more than one language.
Translating emojis
Emojis pose a unique challenge for sentiment analysis tools that are designed to work with written text. These graphical representations of sentiment or emotion are often a primary means of expression for many customers. Ignoring them during text analysis can significantly decrease the accuracy of your result. Converting emojis to their textual description can improve sentiment classification accuracy and resolve out-of-vocabulary issues that occur when a model encounters information that was not part of the training data.
Applications of sentiment analysis
With the rapid expansion of online user-generated content, sentiment analysis has become an increasingly critical tool for supporting data-driven decisions. The following five real-world applications highlight how sentiment analysis is being used across domains to solve complex business problems.
Product or service improvement
Sentiment analysis plays an important role helping organizations detect and address emerging issues quickly. By actively monitoring customer feedback, brands can proactively identify specific aspects of a product that are causing frustration or areas where customer service is falling short. Sentiment analysis can also be used to track customer sentiment over time, helping decision-makers identify positive or negative long-term trends.
Reputation monitoring
Perception is reality. Sentiment analysis can help brands monitor multiple channels including online review sites, social media channels, video-sharing websites and more. By identifying and correcting issues that are negatively impacting reputation, businesses can mitigate the damage and take concrete steps to convert disaffected customers into advocates for the brand
Competitive intelligence
Sentiment analysis isn’t limited to monitoring your own brand. It can also be used to monitor competitors’ reputation, new products and product updates, incentive programs, and marketing methods.
Market research
Survey results, customer reviews, social media mentions and chat conversations are all valuable data for market research. Sentient analysis tools can help businesses categorize and interpret this data, using it to identify market gaps that leave customer needs unfulfilled or underserved, or to evaluate the viability of a new product or service.
Trend prediction
Trend prediction helps organizations anticipate what’s next, allowing them to make decisions today to meet the needs of their customers tomorrow. By using sentiment analysis to identify specific aspects of customer-generated content and news coverage, businesses can track long-term trends such as consumers' perception of economic conditions and their plans to adjust their spending accordingly.
Harness sentiment analysis with Snowflake
Snowflake empowers organizations to capture the potential of sentiment analysis. With Snowpark ML, you can pre-process data; and train, manage and deploy machine learning models—including sentiment analysis programs—all within a single secure location. Unlock the benefits of Snowflake’s proven performance, scalability, stability and governance at every stage of the machine learning workflow.