Lexicon-based vs. Machine Learning-based Sentiment Analysis

Lexicon-based vs. Machine Learning-based Sentiment Analysis For Contact Centers

Imagine a tool that could instantly tell you how your customers feel about your service, every time they call. This is no longer a futuristic fantasy but a present-day reality with sentiment analysis. According to recent studies, companies that effectively manage customer emotions can increase their revenue by up to 15%. Moreover, a report by Gartner indicates that by 2025, customer experience will overtake price and product as the key brand differentiator. Lexicon-based sentiment analysis is one such tool that uses predefined words and phrases to gauge customer emotions. This approach, though simple, can effectively categorize interactions as positive, negative, or neutral, helping businesses enhance customer satisfaction and operational efficiency.

Sentiment analysis plays a pivotal role in this transformation by analyzing call transcripts to gauge whether interactions are positive, negative, or neutral. This powerful tool not only helps improve customer experiences but also aids in achieving various business goals. But which approach to sentiment analysis is best for your contact center? Is it the straightforward lexicon-based method or the more sophisticated machine learning-based approach? In this blog, we will explore the differences between these two approaches and help you determine which one might be better suited for your contact center.

Read More: Sentiment Analysis with GPT vs. Traditional Methods: A Comprehensive Comparison

Understanding Sentiment Analysis

Sentiment analysis, also known as opinion mining, involves analyzing written or spoken texts to determine the emotional tone of the conversation. This technique is used extensively in contact centers to assess how customers feel during their interactions. By scoring calls as positive, negative, or neutral, businesses can better understand the Voice of the Customer.

For contact centers, sentiment analysis provides invaluable insights into customer satisfaction, agent performance, and overall brand perception. It helps in identifying trends, improving customer loyalty, and making informed business decisions. By leveraging these insights, contact centers can enhance customer experiences and operational efficiency.

Lexicon-based Sentiment Analysis

Lexicon-based sentiment analysis relies on a predefined set of words and phrases, known as a sentiment lexicon, to determine the emotional tone of the text. These lexicons categorize words as positive, negative, or neutral based on their semantic orientation.

Features of Lexicon-based Sentiment Analysis

1. Simplicity and Ease of Use

One of the main features of lexicon-based sentiment analysis is its simplicity. This method uses a predefined set of words and phrases, known as a sentiment lexicon, to determine the emotional tone of the text. Each word or phrase in the lexicon is assigned a sentiment score, which can be positive, negative, or neutral. This straightforward approach makes lexicon-based sentiment analysis easy to implement and understand.

  • Predefined Lexicons: Commonly used lexicons include LIWC, General Inquirer, and VADER. These lexicons contain thousands of words categorized by their emotional tone.
  • Direct Application: The lexicon can be directly applied to the text without the need for extensive data preprocessing or training.

2. Transparency and Interpretability

Lexicon-based sentiment analysis is highly transparent and interpretable. Users can easily see which words contributed to the sentiment score and understand how the final sentiment was determined. This makes it straightforward to modify and extend the lexicon if needed.

  • Inspection and Modification: Users can inspect the lexicon and make adjustments, adding new words or changing the sentiment scores of existing words.
  • Clear Attribution: It is easy to trace back the sentiment score to specific words, providing clear insights into the factors influencing the sentiment.

3. Efficiency in Processing

Since lexicon-based sentiment analysis does not require complex computations, it is efficient in processing large volumes of text. This feature makes it suitable for real-time applications where quick sentiment analysis is needed.

  • Fast Computation: The sentiment scores are calculated based on simple lookups in the lexicon, making the process fast and efficient.
  • Scalability: The method can be scaled to handle large datasets without significant increases in processing time.

Advantages of Lexicon-based Sentiment Analysis

  • Simplicity and Interpretability: The method is straightforward and easy to understand. The sentiment lexicon is directly accessible, allowing users to inspect, extend, and modify it as needed.
  • Transparency: Users can see exactly which words influenced the sentiment score and make adjustments if necessary.


Lexicon-based sentiment analysis has its limitations:

  • Context Ignorance: It does not account for the context in which words are used, leading to potential inaccuracies. For example, the word “catch” can have different meanings based on the context.
  • Sarcasm Detection: It struggles to understand sarcasm and other nuanced language elements.
  • Domain-Specific Performance: Lexicons are often created for specific domains and may perform poorly when applied to different contexts, such as spoken language in contact centers.

Machine Learning-based Sentiment Analysis

Machine learning-based sentiment analysis, on the other hand, utilizes advanced algorithms to learn from data and determine sentiment. This approach is a subfield of Natural Language Processing (NLP) and can understand contextual nuances in language.

Advantages of Machine Learning-based Sentiment Analysis

  • Contextual Understanding: It can comprehend the context in which words are used, making it more accurate in identifying sentiment.
  • Higher Accuracy: Studies have shown that machine learning models can achieve over 85% accuracy in sentiment analysis, compared to 49.8% for some lexicon-based tools.
  • Adaptability: Models can be fine-tuned to suit specific organizational needs, allowing for better customization and accuracy.


However, this approach also has challenges:

  • Data Requirements: Training machine learning models requires large datasets, which can be time-consuming and resource-intensive to prepare.
  • Complexity: The algorithms operate as a black-box, making it difficult to understand how they arrive at their conclusions.

Using Sentiment Analysis for ROI in Contact Centers

Effective sentiment analysis can drive significant ROI for contact centers. By analyzing customer emotions, businesses can enhance their service quality and customer satisfaction. Here’s how sentiment analysis can be leveraged:

  • Customer Experience Improvement: Identifying negative sentiments early allows for prompt resolution, improving overall customer satisfaction.
  • Agent Performance Evaluation: Sentiment scores help in assessing agent performance, enabling targeted training and support.
  • Trend Identification: Analyzing sentiment trends helps in understanding customer needs and preferences, guiding strategic decisions.

Comparative Analysis of Sentiment Analysis Tools

Tool Comparison

In the rapidly evolving field of sentiment analysis, several tools have emerged, each offering unique features and capabilities. Here, we compare some of the most popular sentiment analysis tools available in the market: VADER, TextBlob, IBM Watson, Google Cloud Natural Language, and Lexalytics.

Comparative Analysis of Sentiment Analysis Tools

VADER (Valence Aware Dictionary and sEntiment Reasoner)

VADER is a lexicon and rule-based sentiment analysis tool specifically attuned to sentiments expressed in social media. It is designed to perform well on short texts, such as tweets and social media posts, where sentiment is often expressed informally with slang and emojis. VADER is known for its simplicity and effectiveness in analyzing social media content.

Key Features

  • Easy to Use and Integrate with Python: VADER is straightforward to implement, with minimal setup required. It is part of the NLTK package, making it accessible to those familiar with Python and NLP tools.
  • Designed for Social Media Text: The tool is optimized for the nuances of social media language, including slang, abbreviations, and emoticons. This makes it particularly effective for analyzing sentiment in social media posts and comments.
  • Compound Sentiment Score: VADER provides a compound sentiment score that reflects the overall sentiment intensity. This score ranges from -1 (most extreme negative) to +1 (most extreme positive), allowing for a nuanced understanding of sentiment.


  • Simplicity: Easy to implement and use with a clear, straightforward methodology.
  • Speed: Fast processing due to its rule-based approach.
  • Specificity for Social Media: Effective in handling social media text and informal language.


  • Limited Context Understanding: May struggle with context-dependent meanings and sarcasm.
  • Domain-Specific: Primarily designed for social media, it may not perform as well in other domains like formal text or spoken language.


TextBlob is a simple library for processing textual data, providing a consistent API for diving into common natural language processing (NLP) tasks. It is built on top of the NLTK (Natural Language Toolkit) and Pattern libraries, offering a range of text processing capabilities, including sentiment analysis.

Key Features

  • Easy-to-Use Interface: TextBlob offers an intuitive interface for performing sentiment analysis, making it accessible even to those new to NLP.
  • Built on Robust Libraries: Leveraging the strengths of NLTK and Pattern, TextBlob provides reliable text processing and analysis tools.
  • Polarity and Subjectivity Analysis: TextBlob can determine the polarity (positive or negative sentiment) and subjectivity (degree of personal opinion) of a given text.


  • User-Friendly: Extremely easy to use, with a straightforward API.
  • Integration: Seamlessly integrates with Python, making it a convenient choice for Python developers.
  • Comprehensive NLP Features: Offers a variety of text processing tools beyond sentiment analysis.


  • Accuracy: May not be as accurate as more advanced machine learning-based tools.
  • Limited Customization: Provides fewer options for customizing the sentiment analysis process compared to more sophisticated tools.

IBM Watson Natural Language Understanding

IBM Watson is a robust cloud-based NLP service that offers sentiment analysis among other features. It leverages advanced machine learning models to deliver high accuracy and supports a wide range of languages and text sources, making it a versatile tool for various applications.

Key Features

  • High Accuracy: Uses advanced machine learning algorithms to provide highly accurate sentiment analysis.
  • Multi-Language Support: Capable of analyzing text in multiple languages, broadening its applicability across different regions and languages.
  • Diverse Text Sources: Can analyze text from various sources, including social media, blogs, customer reviews, and more, making it highly versatile.


  • Accuracy: High level of accuracy due to sophisticated machine learning models.
  • Language Support: Can handle multiple languages, making it suitable for global applications.
  • Versatility: Effective across a wide range of text sources and applications.


  • Cost: Higher cost compared to simpler, lexicon-based tools.
  • Complexity: Requires more technical expertise to implement and utilize effectively.

Google Cloud Natural Language API

Google Cloud’s NLP API is a powerful tool for analyzing text, including sentiment analysis. It leverages Google’s machine learning models to offer high accuracy and can process large volumes of text efficiently.

Key Features

  • High Accuracy: Utilizes Google’s state-of-the-art machine learning models for accurate sentiment analysis.
  • Multi-Language Support: Supports sentiment analysis in multiple languages, catering to a global audience.
  • Scalability: Can handle large datasets efficiently, making it suitable for enterprises with significant text processing needs.


  • Scalability: Capable of processing large volumes of text quickly and efficiently.
  • Accuracy: Delivers high accuracy thanks to advanced machine learning models.
  • Language Support: Supports a wide array of languages, enhancing its usability worldwide.


  • Cost: Can be expensive, especially for large-scale use.
  • Setup Complexity: Requires technical expertise for proper setup and integration.


Lexalytics is an enterprise-level sentiment analysis tool that provides both on-premise and cloud-based solutions. It offers highly customizable sentiment analysis, leveraging both machine learning and rule-based methods to deliver detailed sentiment scores and categorizations.

Key Features

  • Customizable Sentiment Analysis: Allows for extensive customization to meet specific business needs, providing tailored sentiment analysis solutions.
  • Dual Analysis Methods: Offers both machine learning and rule-based sentiment analysis, enhancing its flexibility and accuracy.
  • Detailed Sentiment Scores: Provides comprehensive sentiment scores and categorizations, delivering deep insights into text data.


  • Customizability: Highly customizable to suit specific organizational requirements.
  • Dual Methods: Combines the strengths of machine learning and rule-based approaches.
  • Detailed Insights: Offers in-depth sentiment analysis and categorization.


  • Cost: More expensive compared to simpler tools.
  • Complex Setup: Requires significant setup and customization efforts.


Choosing the right sentiment analysis approach depends on the specific needs of your contact center. Lexicon-based sentiment analysis offers simplicity and transparency but lacks contextual understanding. Machine learning-based sentiment analysis provides higher accuracy and better contextual comprehension but requires more data and complex setup.

For most contact centers, the superior accuracy and contextual understanding of machine learning-based sentiment analysis make it the preferred choice. It enables deeper insights into customer interactions, helping businesses improve customer experiences and achieve their goals. Always consider the unique requirements of your contact center when selecting a sentiment analysis solution.

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