Evaluating Large Language Models for Fine-grained Sentiment Analysis of App Reviews

Keskeiset käsitteet

Large Language Models (LLMs) can effectively extract app features and their associated sentiments from user reviews, outperforming rule-based approaches, especially under few-shot learning scenarios.

Tiivistelmä

The study evaluates the performance of state-of-the-art LLMs, including GPT-4, ChatGPT, and LLama-2-chat variants, for extracting app features and associated sentiments from user reviews under zero-shot, 1-shot, and 5-shot scenarios.

Key highlights:

• In the zero-shot setting, the best-performing GPT-4 model outperforms rule-based approaches by 23.6% in f1-score for feature extraction.
• 5-shot further improves the f1-score of GPT-4 by 6% for feature extraction.
• GPT-4 achieves a 74% f1-score for predicting positive sentiment towards correctly predicted app features, with 5-shot enhancing it by 7%.
• LLama-2-70B demonstrates the best f1-score of 50.4% for predicting negative feature sentiment in the zero-shot setting.
• For neutral sentiment prediction, GPT-4 outperforms other models with the best f1-score of 41% in the zero-shot setting, and 5-shot improves it by 23%.
• The study suggests that LLM models are promising for generating feature-specific sentiment summaries of user reviews.

Tilastot

The dataset contains 1000 user reviews for 8 different mobile applications, with a total of 1521 labeled feature-sentiment pairs.

Lainaukset

"Recent advances in Large Language Models (LLMs) such as ChatGPT have shown impressive performance on several new tasks without updating the model's parameters i.e. using zero or a few labeled examples."
"Results indicate the best-performing GPT-4 model outperforms rule-based approaches by 23.6% in f1-score with zero-shot feature extraction; 5-shot further improving it by 6%."
"GPT-4 achieves a 74% f1-score for predicting positive sentiment towards correctly predicted app features, with 5-shot enhancing it by 7%."

Tärkeimmät oivallukset

A Fine-grained Sentiment Analysis of App Reviews using Large Language Models: An Evaluation Study

by Faiz Ali Sha... klo arxiv.org 09-12-2024

https://arxiv.org/pdf/2409.07162.pdf

Syvällisempiä Kysymyksiä

How can the performance of LLMs be further improved for feature-specific sentiment analysis of app reviews, especially for the challenging task of neutral sentiment prediction?

To enhance the performance of Large Language Models (LLMs) in feature-specific sentiment analysis, particularly for neutral sentiment prediction, several strategies can be employed:

Data Augmentation: Increasing the diversity and volume of training data can significantly improve model performance. This can be achieved by augmenting existing datasets with synthetic examples that include various expressions of neutral sentiment. Techniques such as paraphrasing or using generative models to create new review samples can help in this regard.

Fine-tuning with Domain-Specific Data: While LLMs like GPT-4 and ChatGPT perform well in zero-shot and few-shot settings, fine-tuning these models on a domain-specific dataset that includes a balanced representation of neutral sentiments can lead to better performance. This approach allows the model to learn the nuances of neutral sentiment in the context of app reviews.

Enhanced Prompt Engineering: Experimenting with different prompt structures can yield better results. For instance, providing more context or examples specifically related to neutral sentiments in the prompts can guide the model to better recognize and classify such sentiments.

Multi-Task Learning: Training LLMs on related tasks, such as emotion detection or intent recognition, alongside sentiment analysis can help the model develop a more nuanced understanding of language. This can improve its ability to discern subtle differences in sentiment, including neutral expressions.

Incorporating User Feedback: Implementing a feedback loop where user corrections and insights are used to retrain the model can help refine its understanding of neutral sentiments. This iterative process can enhance the model's adaptability to real-world language use.

Utilizing Ensemble Methods: Combining predictions from multiple models or approaches can improve overall accuracy. For instance, using a rule-based system alongside LLM predictions can help capture sentiments that the LLM might miss, particularly in ambiguous cases.

By focusing on these strategies, the performance of LLMs in feature-specific sentiment analysis can be significantly enhanced, especially for the challenging task of neutral sentiment prediction.

What other types of user-generated content, beyond app reviews, could benefit from the application of LLMs for fine-grained sentiment analysis?

LLMs can be effectively applied to various types of user-generated content for fine-grained sentiment analysis, including:

Social Media Posts: Platforms like Twitter, Facebook, and Instagram are rich sources of user-generated content. Analyzing sentiments in posts, comments, and replies can provide insights into public opinion on various topics, brands, or events.

Product Reviews: Beyond app reviews, LLMs can analyze reviews for physical products on e-commerce platforms. This includes extracting sentiments related to specific product features, which can inform manufacturers about customer preferences and areas for improvement.

Customer Support Interactions: Analyzing chat logs and emails from customer support can help identify common issues and sentiments expressed by users. This can enhance customer service strategies and improve user satisfaction.

Blog Comments: User comments on blogs can provide insights into audience reactions to content. LLMs can analyze these comments to gauge sentiment towards specific topics or articles, helping content creators understand their audience better.

Survey Responses: Open-ended responses in surveys can be analyzed for sentiment to understand user satisfaction and feedback on services or products. This can guide decision-making in product development and marketing strategies.

Online Forums and Communities: Platforms like Reddit and Quora host discussions where users express opinions and sentiments on various subjects. LLMs can extract sentiments from these discussions to identify trends and community sentiments.

Video Comments: Analyzing comments on platforms like YouTube can provide insights into viewer sentiments regarding specific videos or channels, helping content creators tailor their content to audience preferences.

By leveraging LLMs for fine-grained sentiment analysis across these diverse types of user-generated content, organizations can gain valuable insights into user opinions, preferences, and trends, ultimately enhancing their products and services.

How can the insights gained from this study on LLM performance be leveraged to enhance software engineering practices, such as app development and maintenance?

The insights gained from the study on LLM performance in feature-specific sentiment analysis can significantly enhance software engineering practices in several ways:

User-Centric Development: By understanding user sentiments towards specific app features, developers can prioritize enhancements and new features that align with user needs. This user-centric approach can lead to higher user satisfaction and retention.

Informed Decision-Making: The ability to automatically extract and analyze sentiments from user reviews allows software teams to make data-driven decisions. This can inform product roadmaps, feature prioritization, and resource allocation based on user feedback.

Continuous Improvement: Regular sentiment analysis can help identify recurring issues or areas of dissatisfaction among users. This feedback loop enables continuous improvement in app functionality and user experience, fostering a culture of responsiveness to user needs.

Enhanced Testing and Quality Assurance: Insights from sentiment analysis can guide testing efforts by highlighting areas where users have reported issues. This targeted approach can improve the efficiency of quality assurance processes and reduce the likelihood of negative user experiences.

Marketing and Communication Strategies: Understanding user sentiments can inform marketing strategies by highlighting positive aspects of the app that resonate with users. This can enhance promotional efforts and improve user acquisition strategies.

Feature Validation: Before launching new features, sentiment analysis can be used to gauge user reactions to beta versions or prototypes. This can help validate ideas and ensure that new features meet user expectations.

Stakeholder Engagement: Presenting insights from sentiment analysis to stakeholders can facilitate discussions around product direction and priorities. This transparency can foster collaboration and alignment among teams.

By integrating the insights from LLM performance into software engineering practices, organizations can create more user-friendly applications, enhance user satisfaction, and ultimately drive business success.