Small Language Model
News & Insights
Sentiment analysis

In a sentiment analysis use case, the goal is to analyze textual data (e.g., product reviews, social media posts, customer feedback) and determine whether the sentiment is positive, negative, or neutral. Small language models (SLMs) and large language models (LLMs) perform differently when it comes to efficiency, speed, and accuracy.
Use Case: Real-Time Sentiment Analysis of Social Media Posts
Scenario
A business wants to perform real-time sentiment analysis on customer social media posts to monitor brand perception, track campaign success, and quickly respond to negative feedback. This use case requires efficient, scalable sentiment classification of thousands of posts per second.
Key Metrics for Comparison
Latency: Time taken to analyze the sentiment of one social media post.
Memory Usage: RAM required for sentiment analysis.
Accuracy: Precision of sentiment classification (positive, negative, neutral).
Throughput: Number of posts analyzed per second.
Cost Efficiency: Compute resources required, impacting overall cost.
Metric
Model Size
Latency (average)
Memory Usage (RAM)
Compute Power
Accuracy
Throughput
Small Language Model (SLM)
65M parameters
0.03 seconds/post
180 MB
CPU only
84%
33 posts/second
Large Language Model (LLM)
1.5B parameters
1.8 seconds/post
8 GB
GPU/High-end CPU
95%
0.55 posts/second
Technical Insights
Latency: The SLM processes each social media post in 0.03 seconds, which is 60x faster than the LLM's 1.8 seconds. This latency difference is critical in real-time sentiment analysis applications, where fast insights are needed to respond to customer feedback or track live events.
Memory and Compute Efficiency: The SLM uses just 180 MB of RAM and operates on standard CPUs, making it highly suitable for on-device or edge deployments where compute resources are limited. In contrast, the LLM consumes 8 GB of RAM and typically requires high-end CPUs or GPUs, significantly increasing hardware and operational costs.
Throughput: The SLM can process 33 posts per second, providing higher throughput than the LLM's 0.55 posts per second. This makes the SLM much more efficient when dealing with large volumes of social media posts in real-time, enabling businesses to analyze sentiment continuously without bottlenecks.
Accuracy: While LLMs achieve higher accuracy (95%) due to their ability to capture nuanced language and context, the SLM’s 84% accuracy is still effective for many business applications where absolute precision is not critical. For example, identifying broad trends in customer sentiment or flagging highly negative posts for human review can be handled by the SLM with sufficient accuracy.
Business Insights
Cost Efficiency: Deploying an SLM for sentiment analysis is far more cost-effective than using an LLM. SLMs can run on standard hardware, eliminating the need for expensive cloud infrastructure, GPUs, or high-end processors. For businesses monitoring thousands of social media posts or customer feedback entries, this translates to lower operating costs while still achieving useful results.
Real-Time Performance: The SLM's ability to analyze sentiment 60x faster than an LLM ensures that businesses can track sentiment in real-time, making it easier to respond promptly to negative feedback or capitalize on positive trends. For example, if a product campaign starts generating negative sentiment, the SLM can flag these posts quickly, allowing the business to adjust its strategy or address customer concerns in near real-time.
Scalability: The SLM's high throughput (33 posts per second) allows businesses to scale sentiment analysis across multiple platforms and geographies without needing additional infrastructure. This makes the SLM a scalable solution for companies looking to monitor customer sentiment globally, without incurring substantial infrastructure costs.
Sufficient Accuracy: While the LLM's 95% accuracy is appealing, the 84% accuracy of the SLM is good enough for most sentiment analysis applications. For example, detecting broad shifts in sentiment (e.g., a rise in negative sentiment) is well within the capabilities of an SLM. Businesses can rely on the SLM for daily sentiment monitoring and only escalate complex or ambiguous cases to more expensive models or human reviewers.
Benchmarking Example
Consider a business monitoring 1,000,000 social media posts daily to track customer sentiment.
SLM Processing Time: 0.03 seconds/post → 8.33 hours to process the entire dataset.
LLM Processing Time: 1.8 seconds/post → 20.8 days to process the same dataset.
In this example, the SLM processes all posts in under a day, whereas the LLM would take nearly three weeks. The substantial difference in processing time highlights the efficiency advantage of the SLM for large-scale sentiment analysis.
Conclusion
For sentiment analysis use cases, particularly when dealing with high volumes of data in real time, small language models (SLMs) offer considerable advantages over large language models (LLMs) in terms of efficiency, speed, and scalability. While LLMs provide slightly higher accuracy, the significant latency, memory, and resource demands make them impractical for real-time or large-scale sentiment analysis.
SLMs, with their faster processing times, lower resource usage, and scalability, are ideal for businesses that need real-time insights and are working within cost or resource constraints. The trade-off in accuracy (84% vs. 95%) is acceptable in most business cases, especially where broad trends or immediate responses to sentiment are more important than perfect precision.