Customers are vital to business success, serving as the lifeblood, primary revenue source, and growth catalyst. For most companies, understanding customer needs is a crucial benchmark, with 66% of customers expecting businesses to understand them.
In this context, sentiment analysis emerges as a powerful tool to transform customer feedback data into actionable insights, enabling a deeper understanding of customers. But what is sentiment analysis, how does it work, and why is it important? These questions will be addressed in detail in this blog.
Sentiment Analysis 101: Definition, Use Cases and Challenges
In the current era of big data, the interconnectedness of various customer interaction channels has become paramount. Extracting insights from this wealth of data is crucial for delivering personalised experiences to customers.
Simultaneously, the rapid expansion of the internet and social media has made accessing customer reviews more efficient than ever, constituting a valuable resource comparable to a gold mine.
This customer-generated content spans product reviews, social media posts, forums, blog entries, surveys, and chatbot data.
To efficiently tap into these diverse channels, sentiment analysis emerges as a vital tool. It allows organisations to collect and analyse these gold mines in a scalable and cost-effective manner.
AWS defines sentiment analysis as the process of examining digital text to ascertain the emotional tone, categorising it as positive, negative, or neutral. Applied to customer experience management, sentiment analysis proves invaluable in customer feedback analysis.
How Does Sentiment Analysis Work?
Sentiment analysis involves analysing written or verbal customer feedback to glean valuable insights. By examining online conversations and feedback related to products, brands, or services, companies can gain a comprehensive understanding of customer sentiments. Various approaches are employed to discern the specific sentiment expressed in feedback.
Here are the different methods that can be applied:
Human review is crucial, especially in cases involving understanding context, resolving ambiguity, or grasping irony. Language encapsulates the history of society and continually evolves with new societal events. Consequently, interpreting the meaning of a text, which can vary depending on context, requires the discernment of a conscious mind.
This necessity arises when computer algorithms prove inadequate. However, this reliance on human annotators tends to result in costly and occasionally inconsistent solutions. Moreover, the exponential growth in the volume of textual data renders manual evaluation impractical.
Keyword processing can be regarded as a component of sentiment analysis within rule-based algorithms in natural language processing (NLP) models. Specific keywords are associated with sentiment scores, and these scores are utilised to assess the overall sentiment of longer texts.
The overall sentiment score of a text is derived from the weighted sum of sentiment scores assigned to its keywords, leading to a sentiment class for the text. Various approaches exist for determining sentiment scores.
For instance, one approach involves assigning human specialists to allocate points, but this method may result in subjective judgments. Another approach is to maintain counts or probabilities of keywords in texts, offering more consistent results. In any of these approaches, considering the context of keywords is crucial to prevent classification errors.
Natural Language Processing (NLP)
Natural language processing sentiment analysis utilises context, relations, and patterns to analyse text and extract sentiment values. It incorporates machine learning algorithms, encompassing morpho-syntactic and semantic processing steps.
These algorithms utilise provided datasets to train a classifier model. The objective is to construct a probabilistic model that leverages language features, captures text context, and interprets them to generalise predictions. This pipeline automates the learning of language and task-dependent properties.
Types of Sentiment Analysis
We’ve defined sentiment analysis as classifying a text as positive, negative, or neutral. However, in detail, it has different branches for various use cases. For example, simply classifying a text into one of the three sentiment classes is sufficient for some cases, while in others, it will be necessary to assign different sentiments to different parts of the same text.
Considering the diverse use cases of sentiment analysis, the most popular approaches in this field can be listed as follows:
Whether the text includes a subjective judgement is the first question to address in a sentiment analysis task. The text might be objective, in which case there is no sentiment to evaluate.
Objectivity/subjectivity analysis determines whether there are subjective judgments in the text or not. It does not focus on the type of sentiment but rather on the existence of subjective expression.
General Sentiment Analysis
General sentiment analysis involves assigning a single sentiment to a given text. This approach used to be very popular, yet it lost credibility after the observation of inaccurate or insufficient results from analyses with this approach. This method overlooks the fact that documents may contain more than one sentiment score.
Aspect-Based Sentiment Analysis
Aspect-based sentiment analysis assigns sentiment values to features or subjects in a text. It extracts different topics covered in a text and assigns sentiment values to each of these topics.
This type of sentiment analysis is the best approach for customer experience-oriented projects. Aspect sentiment analysis provides detailed information on which parts of your product customers use and on which parts they express satisfaction or dissatisfaction.
Fine-Grained Sentiment Analysis
This method defines the positive, neutral, or negative poles of documents, including surveys and social media comments. It can provide results based on both the document and the aspect. It defines local polarities and compares them internally to generate a global polarity score for each part.
This enables the classification of a positive comment as positive, while another positive comment might be classified as “very positive.” This kind of sentiment classification helps detect edge cases.
This type of sentiment analysis covers more detailed sentiment types. It does not use standard types, such as positive, neutral, or negative; instead, it employs classes like happiness, sadness, anger, and disappointment.
Systems with emotion detection algorithms not only analyse customer opinions as positive sentiments or negative sentiments but also assess the mood of the customer, taking actions considering the customer’s psychological state.
Benefits of Sentiment Analysis
Sentiment analysis plays a crucial role in enabling companies to make well-informed and strategic decisions in marketing and development. This versatile tool detects the polarity of user-defined assets and concepts, offering flexibility in various consumer scenarios.
It assesses the subjectivity or objectivity of processed text and identifies traces of irony on a global scale. The analysis provides additional insights into the reliability of polarity obtained from emotion analysis.
By leveraging customer voice, social media, chatbot interactions, and survey data, sentiment analysis taps into authentic feelings and opinions expressed directly and spontaneously. Its applications extend to diverse fields, such as scientific research, political campaigns, public relations, and monitoring public perception during election periods.
The power of sentiment analysis becomes evident when applied to the voice of the customer (VoC) and the voice of the employee (VoE).
Business analysts, product managers, customer support teams, human resources professionals, and workforce analysts, among other stakeholders, utilise emotion analysis to gain a comprehensive understanding of how customers and employees feel and the reasons behind their sentiments.
Sentiment Analysis Use Cases
Now that we’ve explored all the ins and outs of sentiment analysis, let’s move on with sentiment analysis use cases to understand the topic more deeply.
Social Media Monitoring
In the age of social media, a lone viral review can tarnish the entire brand. With sentiment analysis, brands can automatically scrutinise customer feedback, ranging from survey responses to social media conversations.
This enables them to glean insights into what matters most from the users’ perspective, empathise with their audience, and tailor products or services to meet their needs.
Emotion analysis aids workforce analysts and Human Resources (HR) managers in listening to employees’ concerns at their source, comprehending the topics of discussion and the emotions involved.
HR teams leverage emotion analysis to proactively address pain points and boost morale through a thorough analysis of employee surveys, team messages, emails, and other communications.
Customer Feedback – NPS (Net Promoter Score)
NPS surveys are the most commonly used methods for obtaining feedback on fundamental questions such as, “Would you recommend this company, product, or service to a friend?” These surveys typically employ point scoring systems, enabling companies to categorise customers into promoters, neutrals, and detractors based on their scores.
These classifications aim to assess the overall customer experience and identify ways to potentially elevate customers to full supporters, a level where they are likely to buy more, stay longer, or refer other customers.
Quantitative survey data is easily collected and evaluated. However, the subsequent question in NPS surveys seek the reasons behind the scores given by customers. This initiates a series of open-ended responses that are more challenging to analyse.
Nonetheless, sentiment analysis allows these textual responses to be categorised as positive or negative, providing more insight into the reasons behind customers’ scores.
Real-time sentiment analysis tools play a crucial role in enhancing customer satisfaction. Analysts extract valuable information by scrutinising social media comments or news data on various scales. Customer support directors and social media leaders address potential issues before they become viral, guiding these cases to product managers.
Challenges of Sentiment Analysis
Machine learning/deep learning-based sentiment analysis approaches depend on the usage of big data. Therefore, a sufficient amount of data is needed to build such a sentiment analysis system.
It is inevitable for the model to make an erroneous evaluation when there is not enough data to keep the system alive or when the system is not kept up to date. The sentiment value of a text can be influenced by features of the text.
In the piece of text, emotion analysis is influenced by the object, attribute, visionary, orientation, and vision power elements.
Keyword processing usually relies on the sentiment of a single word; therefore, it fails to capture features of a text that affect the sentiment type. NLP uses data analysis, machine learning, and deep learning technologies to overcome difficulties, like language nuances.
Examples of these difficulties include grammatical nuances, misspellings, uncertainty, and regional differences. Sometimes, words can be used in ironic statements, and the sentence means the opposite of what you read.
In most cases, extracting the true meaning without context knowledge is impossible. Dealing with sarcastic statements is one of the most challenging problems in analysing the sentiment of a text, along with cases like the usage of contradicting words in a sentence, difficulty in extracting entities, and referential ambiguities.
Unlock the Power of Sentiment Analysis with Artiwise
Artiwise distinguishes itself as a cutting-edge platform in Voice of the Customer (VoC) analytics, leveraging advanced AI-driven methodologies and sentiment analysis to convert diverse customer feedback into actionable customer insights.
What sets Artiwise apart is its unique ability to seamlessly integrate and analyse data from a myriad of communication channels, including calls, texts, and online reviews. This approach captures a broader range of customer emotions and experiences, offering deeper and more timely insights.
In contrast to traditional surveys, Artiwise’s real-time feedback analysis offers a more dynamic and thorough comprehension of customer sentiments. The platform uses machine learning and deep learning algorithms to process textual data and extract aspect-based sentiment classes instantly.
Artiwise’s continuous analysis of spontaneous feedback circumvents issues of low response rates and bias, providing a more authentic and holistic view of customer opinions, thereby delivering accurate and actionable insights for businesses.
Ready to discover what your customers feel about your brand? Let’s meet for a POC (Proof of Concept) project to discuss how the Artiwise VoC platform can enable you to achieve sustainable customer satisfaction.