Open research questions in Sentiment Analysis and Opinion Mining
348 gap statements mined from Sentiment Analysis and Opinion Mining papers in our 7.9M-paper local library, which holds 2,230 papers on the topic — drawn mostly from each paper's own stated research gap, future-work, challenge and limitation notes, and its abstract. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one.
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What the literature leaves open
Prior research on sentiment's impact on perceived helpfulness shows mixed results. Sentiment analysis alone has a limited ability to predict customer behavior. The study aims to address the gap by examining the impact of specific emotions on review helpfulness.
The Influence of Sentiment and Emotion on Helpful Reviews: Machine Learning Analysis of Emotion Dynamics in Online Reviews · 2026 · DOIThe generated fused representations lack sufficient emotional discriminative power and are unable to effectively predict sentiment polarity. Prior work has not effectively addressed the issue of incomplete modality information.
Dual-path fusion network for multimodal sentiment analysis under uncertain missing modalities · 2026 · DOIExisting labeled data is insufficient to cover new target distributions when models are transferred from review platforms to social media. Prior studies have not effectively addressed the issue of inconsistent labels from weak-supervision sources.
MSRA-CCL: Multi-Source Reliability-Aware Aggregation and Cross-Domain Consistency Learning for Weakly Supervised Sentiment Classification · 2026 · DOIThe paper identifies a limitation in representation learning and supervision design, where strong contrastive constraints are often imposed before the shared and specific subspaces have stabilized. The paper also notes that prior methods may not effectively disentangle modality-invariant and modality-specific sentiment information.
Cross-modal consistency gating for reliable LLM reasoning in multimodal sentiment analysis · 2026 · DOIThe lack of high-quality annotated datasets in the educational domain. The need for automation of educational content structuring. The potential of artificial intelligence technologies in higher education.
Existing sentiment analysis methods for Danmu have limitations in capturing colloquial and diverse sentiment expressions. The need for a model that can accurately distinguish between similar sentiments. The lack of a model that can capture the multi-dimensional sentiment features of Danmu.
Dual-Channel ADCMix–BiLSTM Model with Attention Mechanisms for Multi-Dimensional Sentiment Analysis of Danmu · 2025 · DOIThe current approach to sentiment analysis lacks an organized framework for maximizing insights that aid in drawing robust conclusions concerning customer sentiments. The study identifies a gap in the existing literature on the integration of topic modeling and association rule mining for sentiment analysis.
A Cross-Product Analysis of Earphone Reviews Using Contextual Topic Modeling and Association Rule Mining · 2024 · DOIOur findings highlight that arbitrary use of lexicons may produce biased and inconsistent results across lexicons, limiting generalizability, and support complementary use of GenAI and lexicons.
Bridging the gap: addressing measurement bias and integrating lexicons to generative AI · 2026 · DOIWe provide detailed guidelines to help researchers avoid empirical bias that can lead to inconsistent results in lexicon-based CATA.
Bridging the gap: addressing measurement bias and integrating lexicons to generative AI · 2026 · DOIHowever, it remains unclear whether such unsolicited narratives can serve as valid indicators of perceived health outcomes, particularly in non-Anglophone contexts.
Tracking subjective symptom improvement from patient narratives in mobile health: An observational natural language processing study · 2026 · DOIThis study addresses a gap in literature by integrating embedding-based topic modeling and sentiment analysis to explore the association between topics and sentiments.
A spatiotemporal analytical framework for dynamic public opinion and sentiment analysis on mega infrastructure project · 2025 · DOIFeline pruritus is a common disease in the domestic cat with easily observable symptoms, yet remains poorly understood regarding its impact on pet owners' lives and quality of life for affected animals.
MSR5 Leveraging Machine Learning To Understand Pet Owner Experiences of Feline Pruritus Through Social Media Listening · 2024 · DOIHowever, the competitive analysis of product features and more specific improvement strategies have not been explored deeply.
Product Redesign and Innovation Based on Online Reviews: A Multistage Combined Search Method · 2024 · DOIFuture research can explore other approaches for emotion identification from Arabic textual data. Future research can investigate the application of the proposed approach to other languages.
From Machine Learning to Heterogeneous Models: A Comprehensive Study on Fine-Grained Emotion Detection in Arabic Text · 2026 · DOIFine-grained emotion detection in Arabic text is a challenging task. There is a need for a comprehensive study on fine-grained emotion detection in Arabic text.
From Machine Learning to Heterogeneous Models: A Comprehensive Study on Fine-Grained Emotion Detection in Arabic Text · 2026 · DOIMost existing studies typically focus only on classification accuracy without connecting results to actual market intelligence systems. Prior approaches do not address uncertainty in a unified manner. There is a need for a reliability-aware framework to extract sentiment signals from large-scale unlabeled texts in multiple languages.
Reliability-Aware Multilingual Sentiment Analytics for Agricultural Market Intelligence · 2026 · DOIThese findings highlight the model's capability to discern nuanced emotional expressions, such as the 30% Empathetic sentiment that fosters compassion and solidarity among audiences, and the 15% Critique of Power, which underscores the need for accountability in global responses.
A New Framework for Nuanced Sentiment Analysis in Political Communication: A Machine Learning Approach to the Case Study of His Majesty King Abdullah II's UN Speech · 2026 · DOIIndiscriminate Modality Contribution: existing fusion strategies often rely on static or implicitly embedded modality weights, - Representational Bottleneck: human emotional expressions are highly compositional, ranging from coarse-grained affective polarity to fine-grained emotional cues.
AHSD: adaptive multimodal fusion with hierarchical semantic disentanglement for sentiment analysis · 2026 · DOIAnalyzing Twitter data manually is very difficult and time-consuming. The existing systems for sentiment analysis mainly depend on basic machine learning techniques.
Early work by Pang and Lee demonstrated that machine learning classifiers (SVM, Naïve Bayes) could achieve high accuracy (≈83%) on movie review sentiment data using unigram features. Subsequently, many researchers have explored varied approaches: lexicon-based, ML-based, and deep learning. A. ML and Preprocessing Symeonidis et al. conducted a comprehensive study on Twitter sentiment preprocessing.
Sentiment Analysis of Social Media Reviews: A Machine Learning and Deep Learning Approach · 2026 · DOIResearch in this field is shifting from laboratory settings to real-world scenarios - One core challenge is to address information loss and enhance model…
Dual-path fusion network for multimodal sentiment analysis under uncertain missing modalities · 2026 · DOIFurther research on Trainable Fractional Fourier Transform, - Exploration of Self-Modulating Convolutional Neural Network applications, - Investigation of domain-specific polarity and entropy…
Enhancing recommendation systems with weighted aspect based opinion mining utilizing advanced deep learning for adaptive personalization of user preferences · 2026 · DOIFurther research on reliability-aware modality calibration and hierarchical semantic representation - Exploring the application of AHSD in other multimodal…
AHSD: adaptive multimodal fusion with hierarchical semantic disentanglement for sentiment analysis · 2026 · DOIInvestigating the effect of different textual reference weights on consistency gating, - Examining the contribution of each modality and modality combination in different…
Cross-modal consistency gating for reliable LLM reasoning in multimodal sentiment analysis · 2026 · DOIEvaluating the dataset with other deep learning architectures - Exploring the application of the dataset in other educational contexts - Investigating the use of the dataset for other natural language processing…
Most-cited papers in Sentiment Analysis and Opinion Mining
- A survey on sentiment analysis methods, applications, and challenges · Artificial Intelligence Review · 2022 · 1,344 citations
- Sentiment strength detection in short informal text · Journal of the American Society for Information Science and Technology · 2010 · 1,190 citations
- Techniques and applications for sentiment analysis · Communications of the ACM · 2013 · 1,107 citations
- Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web · Management Science · 2007 · 1,004 citations
- A comprehensive survey on sentiment analysis: Approaches, challenges and trends · Knowledge-Based Systems · 2021 · 789 citations
- Annotating Expressions of Opinions and Emotions in Language · Computers and the Humanities · 2005 · 777 citations
- Sentiment strength detection for the social web · Journal of the American Society for Information Science and Technology · 2011 · 761 citations
- A review on sentiment analysis and emotion detection from text · Social Network Analysis and Mining · 2021 · 757 citations
- ABCDM: An Attention-based Bidirectional CNN-RNN Deep Model for sentiment analysis · Future Generation Computer Systems · 2020 · 676 citations
- Sentiment Analysis Based on Deep Learning: A Comparative Study · Electronics · 2020 · 599 citations
Most recent work
- SemCap: Sentiment-aware semantic captioning for multimodal aspect-based sentiment analysis · Journal of King Saud University - Computer and Information Sciences · 2026
- EUPA: Embodied Uncertainty-Aware Multimodal Affect Perception for Consumer Electronic Systems · IEEE Transactions on Consumer Electronics · 2026
- GPPLEA: Guided Polarity Prompt Learning for Enhanced Emotion Analysis in Low-sample Social Media Environments · Cognitive Computation · 2026
- Mixture of disentangled experts with missing modalities for robust multimodal sentiment analysis · Information Fusion · 2026
- An information processing framework for education: Supporting automatic question generation with NLP to minimize human intervention · Information Processing & Management · 2026
- Causal Prompting for Implicit Sentiment Analysis With Large Language Models · IEEE Transactions on Computational Social Systems · 2026
- Capsule-enhanced RoBERTa for hierarchical sentiment analysis on social media texts · Discover Artificial Intelligence · 2026
- MulMoSenT: Multimodal sentiment analysis for a low-resource language using textual-visual cross-attention and fusion · Information Fusion · 2026
- SiAraSent: From Features to Deep Transformers for Large-Scale Arabic Sentiment Analysis · Big Data and Cognitive Computing · 2026
- Enhancing Sentiment Analysis and Error Prediction in Lawyer Questioning Through Legal NLP Models: A Mixed-Methods Study in Australian Courtrooms · Journal of Mixed Methods Research · 2026
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