Automated Public Opinion Report Generation

• OPOR-GEN is an automated framework that leverages large language models to synthesize structured public opinion reports from diverse digital sources.
• It employs modular pipelines for data ingestion, preprocessing, topic extraction, sentiment analysis, and retrieval-augmented synthesis enabling real-time monitoring and retrospective analysis.
• The system scales to multi-lingual, high-volume data while addressing challenges such as source bias, temporal reasoning, and verification through robust evaluation metrics.

Automated Online Public Opinion Report Generation (OPOR-GEN) describes the systematic use of computational pipelines—often powered by LLMs—to synthesize timely, structured reports on public opinions by integrating data from social media, forums, news, and other digital sources. OPOR-GEN enables both real-time monitoring and retrospective analysis, supporting domains such as public governance, policy research, crisis management, and commercial review aggregation. Systems adhering to the OPOR-GEN paradigm feature modular architectures for ingestion, preprocessing, topic and sentiment extraction, retrieval-augmented synthesis, and rigorous evaluation. They can scale to thousands of documents per event, adapt to multi-source and multi-lingual input, and expose outputs in structured, actionable formats (Yu et al., 1 Dec 2025, Wang et al., 29 May 2025, Liu et al., 16 May 2025, Saleiro et al., 2015, Nayeem et al., 30 Aug 2025, Wei et al., 30 Jul 2024).

1. Task Formalization and Dataset Construction

The OPOR-GEN task is defined as structured, multi-source summarization. For a given public opinion event $e_i$, the inputs comprise:

• News articles $X_i = \{x_{i,1},\ldots,x_{i,M}\}$,
• Social media posts $Y_i = \{y_{i,1},\ldots,y_{i,K}\}$,
• A metadata reference $Z_i$ with event attributes (e.g. time, location, impact).

The objective is to synthesize a report

$$R_i = \bigl(\text{Title},\,\text{Summary},\,\text{Timeline},\,\text{Focus},\,\text{Suggestions}\bigr)$$

mapping $(X_i, Y_i) \mapsto R_i$ via an LLM-driven function $f_{\theta}$ (Yu et al., 1 Dec 2025). OPOR-BENCH provides a benchmark dataset spanning 463 crisis events (2012–2025), each annotated with approximately 19 news articles and 400 tweets per event, as summarized in Table 1.

Dataset Attribute	Count / Mean	Notes
Crisis Events	463	Global coverage
News Articles	8,842 (avg 19/event)	Sourced via BM25 ranking
Social Media Posts	185,554 (avg 400/event)	Twitter/X API, time-window
Reference Annotation	463	Manual and LLM-based

Construction involves event identification (EM-DAT, Wikipedia), multimodal document collection, and annotation of references, timelines, and netizen/institution tweet classification (Yu et al., 1 Dec 2025).

2. Data Ingestion, Preprocessing, and Entity Extraction

The data pipeline begins with ingestion from diverse platforms (Reddit, Twitter/X, blogs, news RSS), leveraging API-based or dump-based harvesting (Wang et al., 29 May 2025, Saleiro et al., 2015, Nayeem et al., 30 Aug 2025). Preprocessing encompasses:

• Language detection and filtering (fastText, langid)
• Text cleaning (HTML/emojis/URLs removal)
• Deduplication (hashing, cosine-similarity), e.g. threshold $>0.95$
• Sampling and chunking to fit downstream LLM context constraints (default up to 10,000 posts/subreddit or batch of 10 for agentic workflows)

Entity recognition uses dictionary and prefix-tree approaches, with disambiguation via supervised classifiers such as SVMs. In political and policy contexts, entity–document pairs $(e_k, t_j)$ are classified using features including TF-IDF, semantic similarity, and reference KBs (Saleiro et al., 2015).

3. Topic, Theme, and Sentiment Extraction

LLM-powered agents or prompting pipelines extract topics, themes, and subtopics:

• Topic detection: LLMs or statistical models assign 1–3 salient topics per post (Liu et al., 16 May 2025), LDA-based topic modeling yields per-document distributions $\theta_d$ (Saleiro et al., 2015).
• Theme structuring: LLM prompts specify “generate a list of $N$ themes” with output in JSON (Wang et al., 29 May 2025).
• Quote and anecdote extraction: Filtering for explicit mention and experiential/risk content, summarized in JSON format (Wang et al., 29 May 2025).
• Subtopic coding: Hierarchical coding via LLM prompts; alternatively, vector-based clustering with cosine similarity $$s_{ij} = \frac{v_i \cdot v_j}{\|v_i\| \cdot \|v_j\|}$$ and agglomerative procedures at $\tau \geq 0.75$ (Wang et al., 29 May 2025).
• Sentiment scoring: For post $p$, $S(p) = \sum_i w_i \text{score}_i$, or for discrete labels, thresholds on LLM-inferred scores (Liu et al., 16 May 2025). In advanced setups, emotion dictionaries and ML regressors supply sentiment predictions and correlation to real-world trends (Wei et al., 30 Jul 2024).

4. Retrieval-Augmented Generation and Synthesis

OPOR-GEN systems frequently utilize Retrieval-Augmented Generation (RAG):

• Review, thread, or post segments are embedded (Sentence-Transformer, 384d) and indexed (BM25 for lexical, dense for semantic) (Nayeem et al., 30 Aug 2025).
• For a given query $q$, retrieve Top-$K$ evidence sentences $S_q$ via scoring (BM25 formula, cosine similarity):

$$\text{score}_{\cos}(q,d) = \frac{\mathbf{q} \cdot \mathbf{d}}{\|\mathbf{q}\| \|\mathbf{d}\|}$$

• Synthesis: LLM receives prompt $\{q, S_q\}$, style guidelines $\mathcal{C}$, and template $\mathcal{P}$ enforcing output structure (e.g., PROS/CONS keys for reviews, five-section report for policy events) (Nayeem et al., 30 Aug 2025, Yu et al., 1 Dec 2025).
• In agent-based frameworks, opinion-leader agents in multiple domains (politics, economics, etc.) simulate responses and emotional reactions, updating per event vector embeddings $$s_{d,i}(t+1) = \alpha s_{d,i}(t) + \beta Enc(a_{d,i})$$ (Wei et al., 30 Jul 2024).

5. Report Generation, Output Formats, and Verification

OPOR-GEN reports are highly structured. Typical templates include:

Section	Content	Output Format
Executive Summary	High-level findings	Markdown, PDF, JSON
Methodology	Data sources, time frame, pipeline
Thematic Sections	Theme titles, subtopic hierarchy, quotations	JSON, Markdown tables
Sentiment & Buzz	Quantitative time-series, heatmaps	Charts, tables
Recommendations	Actionable suggestions, unexpected insights	Bullet lists
Appendices	Raw quotes, prompts, provenance

Verification employs reference-free metrics over aspect–opinion–sentiment (AOS) triplets: Aspect Relevance (AR), Sentiment Factuality (SF), Opinion Faithfulness (OF), computed via ABSA models. $\mathbb{E}[AR]$, $\mathbb{E}[SF]$, and $\mathbb{E}[OF]$ are reported (typically AR $\approx$ 78%, SF $\approx$ 88%, OF $\approx$ 80% in review contexts) (Nayeem et al., 30 Aug 2025).

OPOR-EVAL is an agent-based evaluation suite that decomposes report scores $S_i = (s_{i,1}, \ldots, s_{i,15})$ across factual accuracy, opinion mining, and solution-counseling dimensions using simulated expert agents. Spearman $\rho$, Pearson $r$, and MAE are used for alignment measurement versus human experts (Yu et al., 1 Dec 2025).

6. Benchmarking, Performance, and Implementation Strategies

Multiple LLMs have been benchmarked for OPOR-GEN on both end-to-end and modular pipelines. Gemini 2.5 Pro, DeepSeek-R1/V3, GPT-4o, and Llama-3.3-70B achieve overall structured report scores from 3.52 to 3.71 (Likert, Table 6). End-to-end generation is superior for succinct sections (Title, Summary), whereas modular pipelines attain better timeline and focus accuracy (Yu et al., 1 Dec 2025). Temporal reasoning is a universal weakness among current systems (timeline date accuracy mean 1.25/5).

Implementation guidelines specify batching (batch = 10 posts for efficiency), cross-platform adaptation via standard field interfaces, multilingual support (translation via LLMs), and streaming modes for real-time monitoring (Liu et al., 16 May 2025). OPOR-GEN can be deployed as a Jupyter Notebook for analysis or exposed as a Flask/FastAPI RESTful service, supporting easy integration and extension (Wang et al., 29 May 2025).

7. Limitations, Challenges, and Future Research

Current OPOR-GEN systems exhibit several limitations:

• Data source bias: Reddit- and Twitter-centric analyses skew toward younger, tech-savvy demographics (Wang et al., 29 May 2025).
• Lack of demographic and geographic metadata caps representativeness and impedes weighted analysis (Wang et al., 29 May 2025).
• Verification: Anonymous sources are prone to misinformation; automated fact-checking modules and link-backs are in development (Wang et al., 29 May 2025).
• Quantitative dashboards: Most systems remain qualitative-first but are actively incorporating time-series, volume, and sentiment plots (Saleiro et al., 2015).
• Information overload: Excessive source documents degrade synthesis quality; source-balance mechanisms and filtering are needed (Yu et al., 1 Dec 2025).
• Hallucinations and sentiment drift: Reference-free verification via AOS triplets and human-in-the-loop correction are recommended for robustness (Nayeem et al., 30 Aug 2025).
• Temporal reasoning: Dedicated date-extraction and trend modules are open challenges (Yu et al., 1 Dec 2025).

A plausible implication is the necessity for hybrid, retrieval-augmented generation strategies, with evaluator normalization and interactive correction remaining active areas of research (Yu et al., 1 Dec 2025, Nayeem et al., 30 Aug 2025). Enhancing data representativeness, transparency, and quantitative integration is a core focus for future OPOR-GEN architectures (Wang et al., 29 May 2025, Yu et al., 1 Dec 2025).

• "PolicyPulse: LLM-Synthesis Tool for Policy Researchers" (Wang et al., 29 May 2025)
• "Can AI automatically analyze public opinion? A LLM agents-based agentic pipeline for timely public opinion analysis" (Liu et al., 16 May 2025)
• "POPmine: Tracking Political Opinion on the Web" (Saleiro et al., 2015)
• "OpinioRAG: Towards Generating User-Centric Opinion Highlights from Large-scale Online Reviews" (Nayeem et al., 30 Aug 2025)
• "Mimicking the Mavens: Agent-based Opinion Synthesis and Emotion Prediction for Social Media Influencers" (Wei et al., 30 Jul 2024)
• "OPOR-Bench: Evaluating LLMs on Online Public Opinion Report Generation" (Yu et al., 1 Dec 2025)