Improving Multilingual Social Media Insights: Aspect-based Comment Analysis
This work addresses the problem of extracting insights from multilingual social media data for NLP applications, but it is incremental as it builds on existing LLM techniques.
The paper tackled the challenge of analyzing diverse social media comments by proposing a method to generate aspect terms from individual comments, which improved performance on comment clustering and summarization tasks.
The inherent nature of social media posts, characterized by the freedom of language use with a disjointed array of diverse opinions and topics, poses significant challenges to downstream NLP tasks such as comment clustering, comment summarization, and social media opinion analysis. To address this, we propose a granular level of identifying and generating aspect terms from individual comments to guide model attention. Specifically, we leverage multilingual large language models with supervised fine-tuning for comment aspect term generation (CAT-G), further aligning the model's predictions with human expectations through DPO. We demonstrate the effectiveness of our method in enhancing the comprehension of social media discourse on two NLP tasks. Moreover, this paper contributes the first multilingual CAT-G test set on English, Chinese, Malay, and Bahasa Indonesian. As LLM capabilities vary among languages, this test set allows for a comparative analysis of performance across languages with varying levels of LLM proficiency.