Subtopic Deep Dive
Emotion Recognition in Text
Research Guide
What is Emotion Recognition in Text?
Emotion Recognition in Text identifies and classifies fine-grained emotions such as joy, anger, and sadness in textual data using linguistic cues and machine learning models.
This subtopic extends sentiment analysis by modeling discrete emotions based on psychological frameworks like Plutchik's wheel. Key advances include emotion lexicons and neural classifiers applied to social media and conversations. Over 10 papers from the list, with Mohammad and Turney (2010) at 871 citations, establish core methods.
Why It Matters
Emotion recognition powers empathetic chatbots in mental health apps, analyzing user texts for distress signals (Mohammad and Turney, 2010). In customer service, it detects anger in reviews to prioritize responses (Kim, 2014). Human-computer interaction benefits from personality-linked emotion cues in dialogues (Mairesse et al., 2007). Cross-cultural studies improve global AI fairness.
Key Research Challenges
Context Dependency
Emotions depend on surrounding text, making isolated word analysis insufficient (Poria et al., 2017). Sarcasm and irony further complicate detection. Models must capture long-range dependencies.
Lexicon Limitations
Pre-built emotion lexicons miss domain-specific or evolving language (Mohammad and Turney, 2010). Crowdsourced lexicons like NRC help but require updates. Hybrid lexicon-neural approaches address gaps.
Multimodal Integration
Text-only models underperform when audio-visual cues are available (Zadeh et al., 2018; Hazarika et al., 2020). Fusion techniques like MISA improve accuracy but increase complexity. Scalable unimodal text methods remain essential.
Essential Papers
Convolutional Neural Networks for Sentence Classification
Yoon Kim · 2014 · 13.5K citations
We report on a series of experiments with convolutional neural networks (CNN) trained on top of pre-trained word vectors for sentence-level classification tasks.We show that a simple CNN with littl...
Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing
Pengfei Liu, Weizhe Yuan, Jinlan Fu et al. · 2022 · ACM Computing Surveys · 3.3K citations
This article surveys and organizes research works in a new paradigm in natural language processing, which we dub “prompt-based learning.” Unlike traditional supervised learning, which trains a mode...
Determining the sentiment of opinions
Soo-Min Kim, Eduard Hovy · 2004 · 1.5K citations
Identifying sentiments (the affective parts of opinions) is a challenging problem.We present a system that, given a topic, automatically finds the people who hold opinions about that topic and the ...
A survey on sentiment analysis methods, applications, and challenges
Mayur Wankhade, Annavarapu Chandra Sekhara Rao, Chaitanya Kulkarni · 2022 · Artificial Intelligence Review · 1.3K citations
Multimodal Language Analysis in the Wild: CMU-MOSEI Dataset and Interpretable Dynamic Fusion Graph
AmirAli Bagher Zadeh, Paul Pu Liang, Soujanya Poria et al. · 2018 · 1.1K citations
AmirAli Bagher Zadeh, Paul Pu Liang, Soujanya Poria, Erik Cambria, Louis-Philippe Morency. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Pa...
Using Linguistic Cues for the Automatic Recognition of Personality in Conversation and Text
François Mairesse, Marilyn Walker, Matthias R. Mehl et al. · 2007 · Journal of Artificial Intelligence Research · 978 citations
It is well known that utterances convey a great deal of information about the speaker in addition to their semantic content. One such type of information consists of cues to the speaker's personali...
Automatic deception detection: Methods for finding fake news
Nadia Conroy, Victoria L. Rubin, Yimin Chen · 2015 · Proceedings of the Association for Information Science and Technology · 961 citations
ABSTRACT This research surveys the current state‐of‐the‐art technologies that are instrumental in the adoption and development of fake news detection. “Fake news detection” is defined as the task o...
Reading Guide
Foundational Papers
Start with Mohammad and Turney (2010) for emotion lexicons via Mechanical Turk; Kim (2014) for CNN baselines (13488 cites); Mairesse et al. (2007) for linguistic cues.
Recent Advances
Study Poria et al. (2017) for context-dependent analysis; Zadeh et al. (2018) CMU-MOSEI dataset; Hazarika et al. (2020) MISA fusion.
Core Methods
Core techniques: lexicon-based (Mohammad and Turney, 2010), convolutional networks (Kim, 2014), dynamic fusion graphs (Zadeh et al., 2018), linguistic cue extraction (Mairesse et al., 2007).
How PapersFlow Helps You Research Emotion Recognition in Text
Discover & Search
Research Agent uses searchPapers('emotion recognition in text lexicon') to find Mohammad and Turney (2010), then citationGraph reveals 871 downstream works on emotion lexicons. findSimilarPapers on Kim (2014) uncovers CNN adaptations for multi-emotion tasks. exaSearch queries psychological emotion models in NLP.
Analyze & Verify
Analysis Agent runs readPaperContent on Poria et al. (2017) to extract context models, verifies claims with CoVe against Kim (2014) baselines, and uses runPythonAnalysis to plot F1-scores from emotion datasets via pandas. GRADE assigns A to lexicon methods in Mohammad and Turney (2010) for empirical rigor.
Synthesize & Write
Synthesis Agent detects gaps in cross-cultural emotion studies via contradiction flagging across papers, generates exportMermaid diagrams of hierarchical classifiers from Mairesse et al. (2007). Writing Agent applies latexEditText to draft methods, latexSyncCitations for 10+ references, and latexCompile for camera-ready surveys.
Use Cases
"Reproduce emotion lexicon accuracy from Mohammad and Turney 2010 on new data"
Analysis Agent → readPaperContent → runPythonAnalysis (load NRC lexicon, compute precision/recall on custom tweets) → matplotlib precision plot.
"Write survey section on CNNs for emotion classification citing Kim 2014"
Synthesis Agent → gap detection → Writing Agent → latexEditText (insert CNN architecture) → latexSyncCitations (Kim 2014 et al.) → latexCompile → PDF output.
"Find GitHub code for MISA multimodal emotion model"
Research Agent → paperExtractUrls (Hazarika et al. 2020) → paperFindGithubRepo → githubRepoInspect (fusion code, datasets) → exportCsv of implementations.
Automated Workflows
Deep Research workflow scans 50+ emotion papers via searchPapers, structures report with sections on lexicons (Mohammad and Turney, 2010) and CNNs (Kim, 2014). DeepScan applies 7-step CoVe to verify claims in Poria et al. (2017) context models. Theorizer generates hypotheses on lexicon-neural hybrids from citationGraph.
Frequently Asked Questions
What defines Emotion Recognition in Text?
It classifies fine-grained emotions like joy and anger in text using lexicons and neural models (Mohammad and Turney, 2010; Kim, 2014).
What are core methods?
Methods include crowdsourced lexicons (Mohammad and Turney, 2010), CNN classifiers (Kim, 2014), and context fusion (Poria et al., 2017).
What are key papers?
Foundational: Mohammad and Turney (2010, 871 cites) for lexicons; Kim (2014, 13488 cites) for CNNs. Recent: Hazarika et al. (2020, MISA) for multimodal.
What open problems exist?
Challenges: context dependency (Poria et al., 2017), lexicon coverage, and text-only multimodal alignment (Zadeh et al., 2018).
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