Paying attention to customer communication is essential if you want to improve your brand awareness and the user experience. 

Collecting data from different channels is only the first step and sentiment analysis tools are the way to go to help scale and efficiently target your businesses pain points and its MVP characteristics. Do you want to learn more about the value of sentiment analysis and how to get going? Then you’re at the right place!  

Key Highlights

• Sentiment Analysis is the process of identifying the sentiment behind text

• There are several approaches to use sentiment analysis and they might depend on your end goal 

• The current models still face challenges based on context and  double meanings and it is important to consider other complementary techniques and how the technology evolves

• AI tools can help you scale and automate part of the process enabling you to dedicate time to design the right strategies for your findings

What is Sentiment Analysis?

Sentiment analysis is the process of categorising the sentiment behind a piece of content, whether positive, negative, or neutral. It does so by using machine learning techniques such as natural language processing (NLP), and text mining techniques. These types of analysis extract meaning from many sources, e.g. from surveys, reviews, social media, and articles on the internet and attribute a score to it.

Why is Sentiment Analysis Important?

Did you ever wonder what kind of data you can use from sentiment analysis to improve customer experience? 

Imagine knowing your customers’ feelings about your brand and offering. You can understand which specific characteristics influence your audience’s opinions. When you know what’s working well and what’s not working so well, you can take targeted action to change what needs to be corrected and help improve overall brand sentiment.

Monitoring sentiment can help organizations better understand how their actions are being perceived by the public and how they can better mitigate the negative consequences of media, campaigns, product launches and other events that may not produce the expected outcome. Improving brand sentiment can lead to increased sales, greater customer loyalty and better customer retention. Sentiment analysis enables companies to:

• Monitor brand reputation in real time: to a daily analysis or focus on specific events or product launches.

• Gain insights into customer satisfaction and pain points: Identify the customers you need to nurture the closest.

• Improve decision-making based on data-driven insights: define strategies to address your weakest points and strengthen your strongest. 

• Analyze market trends and competitive positioning: see where the public’s opinion is and how do you position against it.

• Processing employee feedback: keep track of work satisfaction in larger companies and address possible issues quickly.

What Are Sentiment Analysis Use Cases?

Sentiment analysis is widely used across industries for various applications, such as:

• Customer Feedback Analysis: Understanding customer sentiments from surveys, reviews, and feedback forms.

• Social Media Monitoring: Tracking public perception of a brand or product on platforms like Twitter, Facebook, and LinkedIn.

• Market Research: Analyzing consumer sentiments towards competitors and industry trends.

• Healthcare Sentiment Analysis: Evaluating patient feedback to improve medical services and healthcare quality.

• Product & Brand Reputation Management : Businesses monitor online discussions and customer sentiments to protect their brand image. Helps in crisis management by identifying negative trends early.

• Customer Support & Chatbot Optimization: AI-powered chatbots use sentiment analysis to provide empathetic responses. Helps prioritize urgent customer issues based on sentiment intensity.

• Product Launch and Improvement: text sentiment analysis using product review data provide an overview on how a product is perceived and what are possible improvements to be made.

Customer Feedback Analysis

Customer reviews are crucial in shaping a brand’s reputation. With tools like Fivetran and Snowflake, businesses can integrate data and leverage AI-driven tools to automate analysis, providing actionable insights that enhance customer satisfaction and reduce churn.

In a previous article, we demonstrated how, using a TripAdvisor dataset, how businesses can predict customer churn based on review data. By utilizing Snowflake’s existing functions, we classified reviews and identified potential churners based on their feedback.

We observed that even positive reviews can signal potential churn when negative aspects, such as dissatisfaction with services, are present. This highlights the complexity of customer sentiment and the importance of analyzing feedback in detail.

How To: Sentiment Analysis Process

Steps of the process

Sentiment analysis involves multiple steps, including:

1. Data Collection: Gathering textual data from various sources such as social media, customer reviews, chatbots and emails.This will depend on your goals and customer base

2. Text Preprocessing: Cleaning and standardizing text data by removing noise, stopwords, and irrelevant symbols.

3. Feature Extraction: Identifying key features such as word frequency, word embeddings, and syntactic patterns.

4. Sentiment Classification: Categorizing text into positive, negative, or neutral sentiments using machine learning models or rule-based approaches.

5. Result Interpretation: Visualizing and analyzing sentiment trends for business decision-making.

Approaches for Sentiment Analysis

The approaches to classify the sentiment of a text can be, among others: 

• Rule-Based Approach: Uses predefined words and rules to determine sentiment. This approach is very common when doing manual sentiment analysis and on early stages of sentiment analysis computation. The possibility of breaking is big because the rules might miss the context in which certain words are used. 

• Hybrid Approach: Combines rule-based and machine learning techniques for more accurate results. This was used mostly in the early stages of using machine learning for sentiment analysis.

• Machine Learning Approach: Employs supervised or unsupervised learning models such as Support Vector Machines (SVM), Naïve Bayes, or Deep Learning techniques. This approach allow the algorithms to understand context and make the techniques more robust (see https://fastdatascience.com/natural-language-processing/trends-in-sentiment-analysis/).

Types of Sentiment Analyses 

Depending on your end goal for the sentiment analysis task you have different types including: 

• Document-Level Sentiment Analysis: Determines the overall sentiment of an entire document or article.

• Sentence-Level Sentiment Analysis: Analyzes sentiment at the sentence level.

• Aspect-based sentiment analysis: Breaks down text into specific aspects or features and assesses sentiment towards each one. It can analyze sentiments related to various product attributes, such as design, performance, and price.

• Fine-grained sentiment analysis: Fine-grained sentiment analysis goes beyond the typical positive, negative, or neutral classifications. It aims to provide a more nuanced understanding of sentiment, often categorizing it into multiple levels, such as very positive, somewhat positive, neutral, somewhat negative, and very negative.

• Aspect-Based Sentiment Analysis: Identifies sentiments associated with specific aspects of a product or service (e.g., “battery life is poor, but the camera is excellent”).

• Emotion Detection: Recognizes emotions such as joy, anger, sadness, and fear in textual data. It also can help us distinguish subjective text from objective text.

• Intent-Based Sentiment Analysis: Identifies the intent behind a customer’s statement, such as a complaint, suggestion, or inquiry.

Typical Challenges in the analytical process

Challenges for users

Even when using the top tier tools, it is important to be aware that there are still challenges to the process specially on an initial phase of the tool use:

• Developing test hypotheses for your goals: It can be difficult to develop hypotheses that are clearly aligned with your goals and give you a clearly defined direction. Think about the results and expectations you want to achieve with these techniques.

• Designing questions: The questions you ask depend on your hypothesis. When conducting surveys, these questions should always be open-ended, allowing users to express their opinions and feelings in their own words.

• Defining data collection methods: This typically includes things like customer surveys, interviews, call logs, etc. As you determine your strategy, consider budget and time constraints. Combining multiple sources will help you develop a comprehensive understanding of your customers’ feelings.

The challenges of the tools

While sentiment analysis is powerful, it comes with several challenges:

• Recognition of sarcasm and irony: AI has difficulty understanding sarcastic or ironic statements.

• Contextuality: The meaning of a word can change depending on the context (e.g. “sick” can mean both “ill” and “amazing”).

• Multilingual analysis: Sentiment analysis in different languages and dialects requires extensive language knowledge.

• Aspect-based sentiment complexity: Identifying sentiment for multiple aspects within a single sentence is challenging.

• Data imbalance: Unequal distribution of positive, negative, and neutral sentiment in data sets can impact accuracy.

• Negation: The use of negative words to convey a reversal of meaning in a sentence. Sentence-level algorithms may struggle to interpret these sentences correctly, especially when negation occurs across two sentences, such as “I liked the product. It was not good”.

Going one step beyond: What is semantic analysis?

When considering the challenges of sentiment analysis, we can also consider alternatives such as semantic analysis. This approach goes beyond sentiment analysis by understanding the meaning and context behind words, phrases, and sentences. It includes:

• Entity recognition: identifying named entities such as people, brands and places.

• Topic modeling: Categorizing text into relevant topics based on semantic similarity.

• Context understanding: Interpreting the relationship between words in a given text. Semantic analysis enhances sentiment analysis by providing deeper insights into customers’ opinions and intentions.

How Kemb can help you with sentiment analysis

At Kemb GmbH, we specialize in data-driven insights to help companies leverage the potential of sentiment analysis. We offer free tools so you can directly start analysing customer’s sentiment, but of course we can also help you with customized analysis solutions that are tailored to the specifics of your business and can be easily integrated into your existing business intelligence infrastructure, combining sentiment analysis with BI tools for data-driven decision making.

Access all of our free tools and see the results and possibilities for yourself.

Conclusion

Sentiment analysis is a game-changer for companies that want to understand their customers’ emotions, improve their brand reputation, and make informed strategic decisions. This technique involves classifying the sentiment behind a piece of content as positive, negative, or neutral.

Analyzing the sentiment of your customers allows you to take specific actions to change what needs to be corrected and enhance your strengths. Sentiment analysis involves several steps, different approaches and techniques, depending on the goal. The challenges for users

It is important to be aware that there are still challenges for users and the process itself. This is where Kembs’ data expertise can elevate your company’s sentiment analysis, giving you a clear competitive edge. Let us now if you need any assistance in the process, we would be happy to support you! 

Frequently asked questions

1. What are the key best practices for implementing sentiment analysis?

Define clear goals and align sentiment analysis with your business objectives. Choose the right approach (rule-based, machine learning, or hybrid). Preprocess text data to remove noise and improve accuracy. Ensure that models are regularly updated and adapted to evolving language patterns.

2. How do I deal with sarcasm and irony in sentiment analysis?

Use deep learning models trained on datasets with sarcasm-related examples. Incorporate contextual word embeddings to understand word relationships. Train models on datasets that include sarcastic phrases commonly used in a specific language or region.

3. What preprocessing steps are needed for sentiment analysis?

When focusing on the technical steps of sentiment analysis, we can tokenize (split text into words or phrases), remove stop words (filter out common words that don’t contribute to sentiment), reduce stems (reduce words to their base forms), and treat special characters, slang, and emojis by assigning them sentiment values.

4. How can I integrate sentiment analysis into my business processes?

Use sentiment analysis for customer service to automate the processing of feedback. Monitor sentiment on social media for brand reputation management. Analyze market sentiment to adjust marketing strategies and product positioning. Integrate business intelligence (BI) tools to get real-time sentiment analysis.