Emotion Classification With Visibility Graphs

Integrating external domain knowledge into emotion recognition models is usually a complex and time-consuming process.

“However, integrating external domain knowledge into emotion recognition models is usually a complex and timeconsuming process [19].”

• cites [19] sourcePartly supported

  The Introduction (‘Focus of the paper’; page not provided) describes increasing complexity in ways of incorporating domain knowledge, and ‘Challenges and concluding remarks’ identifies technical difficulties. The passages do not establish that integration is usually time-consuming or address emotion recognition models specifically. (LLM, conf=0.84)

  Experimental — accuracy not yet measured

Visibility graphs offer an effective means of representing sequential signals by connecting data points according to their value-based visibility, thereby preserving the underlying temporal structure while introducing geometric relationships.

“To this end, visibility graphs offer an effective means of representing sequential signals by connecting data points according to their value-based visibility, thereby preserving the underlying temporal structure while introducing geometric relationships [22], [23].”

• cites [22] sourceSupported
• cites [23] sourceNot checked — no full text of the source

Incorporating a significant number of graph convolution layers in network architectures can result in over-smoothing of dataset features, thereby diminishing the performance of the training process.

“As for the GNN-based approaches, incorporating a significant number of graph convolution layers in network architectures can result in over-smoothing of dataset features, thereby diminishing the performance of the training process [20].”

• cites [20] sourceSupported

EmoVis captures semantic relations previously not emphasized by existing sequential models.

“It also captures semantic relations previously not emphasized by existing sequential models [28], [29].”

• cites [28] sourcePartly supported

  The Introduction says GNNs capture relations between graph nodes, and Section 3.1 describes BertGCN using graph structure to update document representations. The paper does not assess EmoVis or establish that existing sequential models neglect the semantic relations claimed here. (LLM, conf=0.80)

  Experimental — accuracy not yet measured
• cites [29] Michel, 2019, Are sixteen heads really better than one? sourceNot checked — no full text of the source

GCNs primarily focus on local graph structures, which restricts their ability to utilize global context within graphs fully.

“Furthermore, GCNs primarily focus on local graph structures, which restrict their ability to utilize global context within graphs fully [20].”

• cites [20] sourceSupported

Regular graph-based methods ignore the sequential nature of underlying texts.

“However, regular graph-based methods ignore the sequential nature of underlying texts.”

• Also stated in: § Abstract · plain

  “Furthermore, GCNs primarily focus on local graph structures, which restrict their ability to utilize global context within graphs fully [20].”

• cites [20] sourcePartly supported

  The Experimental Setup (p. 6) describes documents as bag-of-words vectors recording word presence or absence, which do not capture word order. The paper does not establish the broader claim that regular graph-based methods generally ignore the sequential nature of texts. (LLM, conf=0.82)

  Experimental — accuracy not yet measured

Results on the GoEmotions dataset show improved performance of transformer-based models when integrated with EmoVis_lin.

“results on the GoEmotions dataset showing improved performance of transformer-based models when integrated with EmoVis lin [30], [49]”

• cites [30] sourcePartly supported

  The paper introduces GoEmotions and reports that its BERT model outperforms a biLSTM model (Section 2.3, p. 2). It does not report integrating a transformer-based model with EmoVis_lin or attribute any performance improvement to that integration. (LLM, conf=0.88)

  Experimental — accuracy not yet measured
• cites [49] sourceNot checked — no full text of the source

• AssumptionRests on an assumption: The SemEval2018 and GoEmotions datasets and their partitions are suitable and representative for evaluating multilingual multi-label emotion classification.

Most existing models for MLEC do not capture emotion-specific associations that can be useful for the classification of emotions.

“However, most existing models for MLEC do not capture emotion-specific associations that can be useful for the classification of emotions [7], [9], [10].”

• cites [7] sourcePartly supported

  Section 4.2 (page unavailable) says certain words are highly indicative of particular emotions in the multi-label classification task, supporting the usefulness of emotion-specific cues. It does not say that most existing MLEC models fail to capture those associations; it reports that a bag-of-words baseline performs slightly better than a neural bag-of-words baseline on that task. (LLM, conf=0.79)

  Experimental — accuracy not yet measured
• cites [9] sourcePartly supported

  Section 6.2 (page unavailable) says task-specific affect-lexicon features can improve emotion-related models. It does not say that most existing multi-label emotion classification models fail to capture emotion-specific associations; indeed, it reports that most top-performing systems used affect-lexicon features. (LLM, conf=0.78)

  Experimental — accuracy not yet measured
• cites [10] sourceCould not verify

  The Related Work sections describe approaches to emotion classification, and §4.3 compares models used by the authors. Neither passage says that most existing MLEC models fail to capture emotion-specific associations. Searches for that phrase and for associations between emotions found no passage addressing the claim. (LLM, conf=0.72)

  Experimental — accuracy not yet measured

EmoVis schemes consistently improve the F1-Macro, F1-Micro, and JS metrics for each model tested in the MLEC tasks.

“Specifically, the EmoVis schemes consistently improve the F1-Macro, F1-Micro, and JS metrics for each model tested, as demonstrated in the MLEC tasks.”

• AssumptionRests on an assumption: The SemEval2018 and GoEmotions datasets and their partitions are suitable and representative for evaluating multilingual multi-label emotion classification.

EmoVis enables transformer-based models to outperform the state-of-the-art baselines across three diverse datasets in different languages in the SemEval2018 competition datasets and the GoEmotions dataset.

“We experimentally show that EmoVis enables transformerbased models to outperform the state-of-the-art baselines across three diverse datasets in different languages in the SemEval2018 competition datasets and the GoEmotions dataset.”

• Also stated in: § B. Results and Discussions · boosted

  “These results underscore the effectiveness of EmoVis in a multilingual setting and its superiority over the state-of-the-art models.”

• AssumptionRests on an assumption: The SemEval2018 and GoEmotions datasets and their partitions are suitable and representative for evaluating multilingual multi-label emotion classification.
• AssumptionRests on an assumption: The baseline performances taken from the literature are directly comparable to the paper's experimental setup and results.

EmoVis is the first generic graph-based neural network that utilizes visibility graphs, converting classical time-series information to graph representations, in the context of multi-label emotion classification in texts from various languages.

“In this paper, we propose EmoVis, the first generic graph-based neural network that utilizes visibility graphs, which converts classical time-series information to graph representations.”

• Also stated in: § Abstract · plain

  “EmoVis is the first generic graph-based model that utilizes visibility graphs in the context of MLEC in texts from various languages.”

Unlike conventional methods that generate graphs using predefined metrics like point-wise mutual information or cosine similarities, EmoVis preserves the inherent sequential topology of sentences.

“Unlike conventional methods that generate graphs using predefined metrics like point-wise mutual information or cosine similarities, EmoVis preserves the inherent sequential topology of sentences.”

• AssumptionRests on an assumption: The order of token embeddings in a transformer output can be treated as a time series whose visibility graph captures meaningful sequential and semantic relationships for emotion classification.

EmoVis preserves the topology of the input sentence.

“EmoVis (Emotion Classification with Visibility Graphs), a novel, highly modular graph-based model that preserves the topology of the input sentence, is easy to construct, and, most importantly, improves the performance of any transformer-based model to which it is added.”

• AssumptionRests on an assumption: The order of token embeddings in a transformer output can be treated as a time series whose visibility graph captures meaningful sequential and semantic relationships for emotion classification.

EmoVis improves the performance of any transformer-based model to which it is added.

“EmoVis (Emotion Classification with Visibility Graphs), a novel, highly modular graph-based model that preserves the topology of the input sentence, is easy to construct, and, most importantly, improves the performance of any transformer-based model to which it is added.”

• AssumptionRests on an assumption: The SemEval2018 and GoEmotions datasets and their partitions are suitable and representative for evaluating multilingual multi-label emotion classification.

Sequential models do not fully capture the inherent structural relationships and dependencies among emotions.

“Although some approaches utilize emotion-specific associations via treating the problem as a span-prediction problem [11], sequential models do not fully capture the inherent structural relationships and dependencies among emotions.”