Sentiment Analysis: Steps & Challenges in 2025

Sentiment analysis is growing in popularity as it turns raw, unstructured text data into interpretable insights for business through sentiment analysis. However, tangible use cases for sentiment analysis and the fundamental steps of this method may not be clear.

Learn the top business use cases, provided a step by step guide and also top challenges of sentiment analysis:

What is sentiment analysis?

Sentiment analysis is the practice of measuring the negative, neutral or positive attitude in a text. Using natural language processing, the online text data about a certain keyword is analyzed in terms of the intensity of negative or positive words that they contain. The result of this analysis can be an average score of overall positivity, a word cloud of the most popular words in a text or a detailed analysis of associations that can be inferred from the data.

What are the top business use cases of sentiment analysis?

More than 50% of IT professionals consider using natural language processing for business use cases.¹

• Product design and improvement
• Call center analysis
• Measuring customer satisfaction
• Monitoring brand reputation

To learn more about real-life examples of sentiment analysis, feel free to check out our detailed article on this topic.

How does sentiment analysis work?

Step 1) Acquire data:

Sentiment analysis is applied on text data which often requires a rigorous cleaning and processing. Regardless of using a scraping API or web scraping bot, the text data collected from the web will first need to be cleaned from parts that convey no meaning, such as “the” or conjugations of a word. After that, the text needs to be tokenized into words or word groups that can be labeled as positive or negative.

Figure 1. Data acquisition

Source: Vilademir Matula²

Step 2) Select your model:

1. A rule-based model

A rule-based model is a simple and straightforward way to perform sentiment analysis. It relies on predefined rules and natural language processing techniques to label textual data based on the presence of specific positive or negative words.

Pros:

• Provides a quick, high-level overview of positive, negative, or neutral sentiments.
• Easy to set up and use for simple sentiment analysis work.

Cons:

• Struggles with fine-grained sentiment analysis, such as figurative expressions or complex comments.
• Limited by predefined keywords and misses less frequent or nuanced terms.

Figure 2. Words are tagged negative or positive from a sentence

Source: Vilademir Matula³

2. A machine learning model

A machine learning (ML) model goes beyond simple rules by leveraging algorithms to analyze unstructured data like social media mentions, customer feedback, or online reviews.

Pros:

• Highly accurate and adaptable to new data.
• Handles complex comments and fine-grained sentiment analysis.

Cons:

• Requires significant labeled data for training.
• More complex to implement than rule-based methods.

Figure 3. Whole sentences are tagged negative or positive in the sentiment analysis

Source: Vilademir Matula⁴

3. Hybrid sentiment analysis approach

The hybrid approach combines the strengths of rule-based models with machine learning-based sentiment analysis for optimal accuracy and speed.

Pros:

• More accurate than rule-based or machine learning models alone.
• Excels in opinion mining and feature-based sentiment analysis.

Cons:

• Requires more resources, including time and technical expertise.
• Higher setup costs compared to simpler approaches.

Feel free to check our article to learn more about sentiment analysis methods.

Step 3) Analyze and evaluate:

Both rule-based and machine learning models can be improved over time. For example, a dictionary of negative and positive words can be updated as a live source of reference to classify the new data more accurately. Similarly, there are multiple machine learning models that you can apply on your data and compare to each other in order to fine tune your models over time.

Challenges of sentiment analysis

Sentiment analysis is a vital tool for understanding customer feedback, analyzing trends on social media platforms, and managing brand reputation. However, due to the complexity of human language and the limitations of analysis algorithms, there are several challenges that affect its accuracy and reliability. Below are key challenges, their implications, and recommendations for improvement.⁵

1. Lack of context

Understanding the sentiment expressed in text requires proper context. Without it, sentiment analysis tools can misinterpret the meaning, leading to errors in overall sentiment classification. For instance, identical words may convey different meanings depending on the surrounding text or the specific question being answered.

Issues

• Lack of contextual information reduces accuracy in analysis, especially in opinion mining and competitive analysis.
• Example: Responses like “UX” or “design” might be positive or negative depending on the question’s context.

Impact

Misclassifications may lead to incorrect sentiment scores, impacting decision-making based on such data.

Recommendation

• Preprocess data to include the original context of text inputs.
• Employ semantic analysis techniques to better understand relationships between data points.

2. Use of irony and sarcasm

Irony and sarcasm are challenging for sentiment analysis systems because they rely on tone and context, which are often absent in text. As a result, even advanced sentiment analysis models can misinterpret sarcastic comments as positive sentiment or vice versa.

Issue

• Irony and sarcasm are often misunderstood by sentiment analysis algorithms, leading to incorrect results.
• Example: “Just great, another bug in the app” might be tagged as positive due to the word “great.”

Impact

Reduces the ability to perform sentiment analysis accurately, particularly for social media data or online reviews.

Recommendation

• Use training datasets annotated with examples of sarcasm and irony.
• Incorporate sentiment indicators like punctuation or emojis during data mining.

3. Negation

Negation words, such as “not” or “never,” change the sentiment of a sentence, but sentiment analysis tools often fail to account for this. Misclassifying negations can result in incorrect positive-negative sentiment evaluations.

Issue

• Negation is subtle and challenging to detect. For example, “The product is not bad” implies a positive sentiment, but many tools might classify it as neutral or negative.

Impact

Limits the effectiveness of sentiment analysis systems in analyzing nuanced text.

Recommendation

• Develop aspect-based sentiment analysis methods to identify specific attributes and how they are described.
• Train models on datasets with a focus on handling negations effectively.

4. Idiomatic Language

Idiomatic expressions pose a significant challenge because their literal meaning often differs from their intended sentiment. This confuses sentiment analysis algorithms, especially when analyzing unstructured data like reviews or social media data.

Issue

• Idiomatic phrases are frequently misinterpreted. For instance, “Break a leg” might be seen as negative sentiment despite its positive sentiment intention.

Impact

Affects the precision of sentiment analysis work, especially for global brands performing multilingual sentiment analysis.

Recommendation

• Use deep learning models trained on idiomatic and cultural language variations.
• Incorporate data sources that include idiomatic text examples.

5. Nuances and Punctuation

In online communication, punctuation and emojis convey tone and intensity. Ignoring these nuances leads to inaccuracies in fine-grained sentiment analysis, particularly on social media platforms.

Issue

• Emojis and punctuation are rich with meaning but are often disregarded by sentiment analysis models. For example, “Great! 😊” conveys a stronger positive sentiment than just “Great.”

Impact

Reduces the effectiveness of sentiment analysis tools when analyzing social media data or online reviews.

Recommendation

• Use emoji-based sentiment analysis techniques and open-source dictionaries to map emojis and punctuation to emotional intensities.⁶
• Incorporate tools that include sentiment-rich symbols in the analysis.

6. Fake reviews and misinformation

The growing prevalence of fake reviews and bot-generated content presents a significant challenge for sentiment analysis systems. Such fabricated data skews results and impacts trust in brand image and competitive analysis.

Issue

• Difficulties in detecting fake content lead to unreliable sentiment scores.
• Example: Positive bot-generated reviews may inflate the overall sentiment for a brand.

Impact

Undermines the accuracy of opinion mining and data-driven decision-making.

Recommendation

• Use data mining techniques to detect and filter fake content.
• Implement brand protection tools to identify and remove fraudulent reviews.
• Use the most up-to-date tips to identify and disseminate fake reviews right on the spot so that they are neither a part of your data set nor visible to your customers. Check out our detailed article about brand protection tools and methods.

7. Overfitting in sentiment analysis models

Overfitting occurs when an analysis model learns too specifically from its training data, limiting its ability to generalize to new data points. This often happens due to small or biased datasets.

Issue

• Overfitted models perform well on training data but fail when analyzing new social media data or reviews.

Impact

Reduces reliability in sentiment mining, especially for emerging trends or evolving languages.

Recommendation

• Use cross-validation techniques to detect overfitting.
• Fine-tune the model by using various methods such as using cross-validation, data augmentation or holding out some part of the data.
• Regularly test models on diverse datasets from multiple data sources.

To learn more about the challenges of sentiment analysis and the solutions, read our article.

Further reading

• Natural Language Generation (NLG): What it is & How it works
• Top Web Scraping Challenges Solved by AI
• Quick guide to data collection
• Benefits of Using Sentiment Analysis with Your Chatbots

External Links

• 1. IBM Newsroom - The future of hybrid cloud and AI is here.
• 2. Analýza sentimentu — VM.
• 3. Analýza sentimentu — VM.
• 4. Analýza sentimentu — VM.
• 5. Liu, B. (2020). Sentiment analysis: Mining opinions, sentiments, and emotions. Cambridge university press.
• 6. Lou, Y., Zhang, Y., Li, F., Qian, T., & Ji, D. (2020). Emoji-based sentiment analysis using attention networks. ACM Transactions on asian and low-resource language information processing (TALLIP), 19(5), 1-13.