ChatGPT 4.0 vs Claude 3.5

With the growing demand for accurate sentiment classification, researchers have utilized various sentiment analysis models, techniques, and datasets to advance the field. However, achieving precise labeling of emotions and sentiments, as well as detecting irony, hatefulness, and offensiveness, remains a challenge, requiring further testing and refinement.

Explore the sentiment analysis benchmark performance of ChatGPT 4.0 and Claude 3.5, and the details of the experimental testing:

Benchmark dataset and methodology

Analysis dataset

The TweetEval dataset was selected due to its relevance for sentiment analysis techniques applied to real-world Twitter messages. The dataset is part of the association for computational linguistics (ACL) initiative and is widely used in semantic evaluation and text classification tasks. It consists of pre-labeled training data and test sets covering several dimensions of sentiment and contextual understanding:

• Emotion detection: Identifying emotional tones such as anger, joy, optimism, or sadness in tweets.

Example tweet and label: The tweet “#Deppression is real. Partners w/ #depressed people truly dont understand the depth in which they affect us. Add in #anxiety &makes it worse” is labeled as sad.

• Hatefulness detection: Evaluating the presence of hate speech in given tweets.

Example tweet and label: The tweet “Trump wants to deport illegal aliens with ‘no judges or court cases’ #MeTooI am solidly behind this actionThe thought of someone illegally entering a country & showing no respect for its laws,should be protected by same laws is ludacris!#DeportThemAll” is labeled as hateful.

• Irony detection: Recognizing ironic intent in textual content.

Example tweet and label: The tweet “People who tell people with anxiety to “just stop worrying about it” are my favorite kind of people #not #educateyourself” is labeled as irony.

• Offensiveness detection: Classifying tweets with offensive language.

Example tweet and label: The tweet “#ConstitutionDay It’s very odd for the alt right conservatives to say that we are ruining the constitution just because we want #GunControlNow but they are the ones ruining the constitution getting upset because foreigners are coming to this land who are not White wanting to live” is labeled as offensive.

• Sentiment classification: Assigning positive, negative, or neutral labels to tweets.

Example tweet and label: The tweet “Can’t wait to try this – Google Earth VR – this stuff really is the future of exploration….” is labeled as positive.

These tasks align with real-world machine-learning approaches, making them ideal for evaluating the experimental results of the two models.

Analysis methodology

The two models, ChatGPT 4.0 and Claude 3.5, represent state-of-the-art systems in natural language processing and deep learning. Both models have been fine-tuned for sentiment analysis, leveraging extensive training data and advanced architectures.

1. ChatGPT 4.0: Based on OpenAI’s GPT-4 framework, this model utilizes a large-scale machine learning architecture optimized for multimodal sentiment analysis and contextual understanding.
2. Claude 3.5: Developed by Anthropic, this model focuses on ethical AI interactions and precise text classification with an emphasis on conversational context and association-driven learning.

Experimental setup

To ensure consistency and reliability in the experiments, the following methodology was employed:

Input volume

• Two input volumes were tested: 50 tweets and 10 tweets per task.
• This variation aimed to determine how input size impacts model performance, particularly in tasks like based sentiment analysis and hatefulness detection where data volume can influence accuracy.

Task-specific evaluation

Each task from the TweetEval dataset was tested separately. The tasks and corresponding outputs were analyzed using the models’ sentiment analysis models, and accuracy scores were recorded.

Metrics used

Accuracy scores were computed for each task to ensure reliable experimental results.

Setup limitations

We have used datasets where ground truths were publicly available. This may have led to data poisoning (i.e. LLMs being trained on the ground truth). However, we assumed that this is not the case, since accuracies were not close to perfect. For the next version, we may consider using tweets for which ground truth has not been published.

Experimental results: sentiment analysis benchmark

Sentiment analysis benchmark performance trends

• Both models achieved their highest scores with 10 sentences per analysis, suggesting reduced input volume enhances precision.

Figure 1. General trend in the sentiment analysis performance of Claude 3.5 and ChatGPT 4.0

• Claude 3.5 outperformed ChatGPT 4.0 in emotion and sentiment detection. In the general labeling task, ChatGPT 4.0’s sentiment analysis benchmark performance ranged between 64%-98%, showing notable improvements in lower-volume tests.
• ChatGPT 4.0 is more successful than Claude 3.5 on irony and offensive language detection. Claude 3.5 general labelling accuracy ranged between 62%-97%, performed slightly worse than ChatGPT 4.0, on average.

Figure 2. Performance for 10 statements per sentiment analysis

Figure 3. Performance for 50 statements per sentiment analysis

1. Emotion detection

Emotion detection is a challenging task in sentiment analysis, often requiring models to discern subtle cues in language. Here’s how the models performed:

• ChatGPT 4.0 achieved 72% accuracy when analyzing 50 statements, improving slightly to 74% when the input volume was reduced to 10 statements. This suggests its performance benefits from aspect-based sentiment analysis techniques when the task complexity is lowered, possibly due to reduced noise in the training data.
• Claude 3.5, on the other hand, excelled at 50 statements, scoring 77.5%, the highest of all tasks analyzed. However, with only 10 sentences, its accuracy dropped to 76%, indicating that while it is adept at processing larger volumes, it may not adapt as well to smaller, more focused datasets.

The disparity highlights Claude 3.5’s advantage in identifying emotional intensity.

2. Hatefulness detection

Detecting hateful content is crucial for Twitter sentiment classification and other moderation tasks. The results revealed notable differences:

• ChatGPT 4.0 exhibited a marked improvement in accuracy with reduced input volume, jumping from 64% with 50 statements to 74% with 10. This may indicate that its deep learning architecture benefits from focusing on individual instances rather than processing large-scale raw data at once.
• Claude 3.5 showed a steady but slightly lower sentiment analysis benchmark performance, scoring 67.5% with 50 statements and dropping to 62% with 10. This drop suggests potential limitations in recognizing patterns of hate speech when handling smaller datasets.

3. Irony detection

Irony detection is an area where semantic evaluation plays a pivotal role. Both models delivered high sentiment analysis benchmark performance, but ChatGPT 4.0 emerged as a clear leader:

• ChatGPT 4.0 maintained an exceptional 98% accuracy across both input volumes, demonstrating unparalleled consistency in identifying ironic expressions. This success can be attributed to its ability to interpret negative polarity within complex text classification scenarios.
• Claude 3.5 scored slightly lower, achieving 97% accuracy with 50 statements and dropping to 90% with 10 statements. This variance reflects potential challenges in maintaining accuracy with reduced training data or inputs.

Given the models’ overall high accuracy, both are well-suited for Twitter messages involving ironic or sarcastic content. However, ChatGPT 4.0’s consistency gives it a significant advantage for applications requiring standard reliability of benchmark for sentiment.

4. Offensiveness detection

Detecting offensive content is critical for maintaining healthy online communities. The models’ sentiment analysis benchmark performances in this task were as follows:

• ChatGPT 4.0 scored 76% with 50 statements and dipped slightly to 72% with 10, indicating stable sentiment analysis benchmark performance across different input sizes. This aligns with its strong machine learning approaches and ability to adapt to variations in data volume.
• Claude 3.5 achieved 66.5% accuracy with 50 statements but matched ChatGPT 4.0’s 72% accuracy with 10. This convergence suggests that while ChatGPT 4.0 is more consistent, Claude 3.5 can catch up when the methodology involves smaller input sets.

These results underscore the importance of context and training in designing models for offensive language detection, where patterns in the dataset can significantly impact outcomes.

5. Sentiment analysis

The overarching sentiment analysis task focused on classifying data into positive, negative, and neutral sentiments. Accuracy scores for this task varied significantly between the models:

• ChatGPT 4.0 scored 64% with 50 statements and improved to 70% with 10. This improvement highlights its ability to achieve better evaluation metrics when processing smaller benchmark datasets.
• Claude 3.5 showed slightly better sentiment analysis benchmark performance at 50 statements, with an accuracy of 67.5%, and improved slightly when input was reduced to 10 statements (70%).

Both models demonstrated competence in handling sentiment classification, but their strengths diverged, but Claude 3.5 shows robustness across larger datasets.

Overall accuracy

Combining all tasks, the models’ total accuracy scores provide a holistic view of their capabilities:

• ChatGPT 4.0 achieved an average accuracy of 74.8% with 50 statements and 77.6% with 10 statements, reflecting steady improvements with reduced input size. Its flexibility in adapting to diverse sentiment analysis techniques makes it a strong contender for a wide range of applications.
• Claude 3.5 performed slightly better with 50 statements, scoring 75.2%, but saw a decline to 74.0% with 10 statements. This suggests it may be better suited for large-scale semantic evaluation tasks rather than scenarios requiring granular inputs.

Observations and insights

Impact of input volume

Both models showed improved sentiment analysis benchmark performance with smaller input volumes in some tasks, emphasizing the importance of reducing noise in training data for tasks like hatefulness detection and sentiment classification.

Task-specific strengths

ChatGPT 4.0 dominated in irony detection and performed consistently well across all tasks. Claude 3.5, while slightly less consistent, excelled in tasks like emotion detection, especially with larger input volumes.

Broader implications

These experimental results validate the effectiveness of using benchmark datasets like TweetEval for text classification research. The findings can guide research community in selecting the right model based on their specific use case, whether it involves detecting nuanced sentiment intensity or analyzing negative polarity in Twitter messages.

Overview of ChatGPT 4.0 and Claude 3.5

Both ChatGPT 4.0 and Claude 3.5 represent significant advancements in the field of natural language processing (NLP), with applications spanning from sentiment analysis to conversational AI. These models are among the most widely recognized for their ability to interpret, process, and generate human-like text. Below is a detailed description of each model, highlighting their unique capabilities and relevance to sentiment classification and related machine learning tasks.

ChatGPT 4.0

ChatGPT 4.0, developed by OpenAI, is an enhanced version of its predecessor, GPT-3.5, and features significant improvements in deep learning architecture and language understanding. This model is optimized for a wide range of NLP tasks, including sentiment analysis models and aspect-based sentiment analysis.

Applications in sentiment analysis benchmark

ChatGPT 4.0 is frequently used in research community and industry for tasks such as:

• Twitter messages sentiment analysis for social media monitoring.
• Sentiment classification of customer feedback in e-commerce.
• Emotion detection in mental health applications.
• Aspect-based sentiment analysis for product reviews and surveys.

Limitations

Despite its strengths, ChatGPT 4.0 can occasionally overfit to specific sentiment patterns, leading to reduced accuracy in highly domain-specific contexts.

Claude 3.5

Claude 3.5, created by Anthropic, is an NLP model designed with a focus on safety, ethical behavior, and precise text generation. It is particularly well-suited for tasks requiring sensitivity to context and nuanced sentiment analysis techniques.

Applications in sentiment analysis

Claude 3.5 is utilized in scenarios such as:

• Hatefulness detection for monitoring social media and online platforms.
• Offensiveness detection in content moderation systems.
• Customer service interactions, with an emphasis on sentiment classification to improve user experience.
• Aspect-based sentiment analysis for identifying sentiment trends in business intelligence.

Limitations

While Claude 3.5 excels in ethical and contextual understanding, it sometimes underperforms in detecting highly subtle or implicit sentiments compared to its competitors. Additionally, its training dataset is less diverse than that of ChatGPT 4.0, which may result in reduced robustness across some benchmark datasets.

Further reading

External Links