Although sentiment analysis is a powerful tool in helping businesses understand customer desires, this technology certainly has a number of challenges that you need to face.
These challenges arise because the language we use daily is very complex to learn, for example, words that need to be interpreted based on context. This is why sentiment analysis needs to prepare training data and the model to be used more thoroughly.
In this article, KLIK Group will introduce to you the main challenges in implementing sentiment analysis.
1. Ensuring Context
To understand customers more deeply, we need to understand the context they intend to convey in reviews and comments. Even if a sentence appears positive, it can have a negative meaning if intended as sarcasm or irony.
There are many contexts that sentiment analysis needs to recognize, such as sarcasm, words with alternate meanings, and ambiguity. This makes it difficult for the system to read the true emotion and causes the sentiment score to be inaccurate.
Solution: You can prepare training data according to the cases found. This can help the model learn and understand context in the future based on the experience accumulated by the sentiment analysis. This training data process and results need to be monitored periodically to ensure the performance and accuracy of the sentiment analysis outcomes.
2. Analyzing Diverse Languages
Your business may face customer comments in various languages. Since sentiment analysis is typically trained on only one language, this can cause the meaning and intent the customer wants to convey to be difficult for the sentiment analysis to understand.
Solution: Consider designing a system capable of multilingual learning, so it can make predictions regardless of the language used.
3. Identifying Emojis and Informal Language
In expressing their opinions, customers often use emojis, abbreviations, and slang. This should not be underestimated, as these emojis and slang are forms of customer expression that can help us identify sentiment.
Solution: You can increase the accuracy of sentiment analysis by tagging emoji sentiment scores.
4. Handling Bias in Data and Model
Sentiment analysis models learn from training data, so it can be said that training data is its main driving engine. However, if that data contains bias and is of low quality, then the analysis results will also be biased and inaccurate.
This is often referred to as "Garbage in, Garbage Out," which means: if you train a model with poor-quality data, the results will be poor too, but if you train a model with quality data, the results will be better and more accurate.
Solution: Don't just focus on model development, but make sure to maximize the initial stage of data preparation and cleaning, because this initial stage will influence the final results in the sentiment analysis process.
Key Points
Sentiment analysis is not just about technology, but about data quality, understanding context, and continuous monitoring. With the right approach, these challenges can be minimized so that businesses still gain relevant and actionable insights.
RELATED POST
Various Methods in Sentiment Analysis
Jan 13, 2026