EmoFusion: An integrated machine learning model leveraging embeddings and lexicons to improve textual emotion classification
Human emotions are complicated and intertwined with cognitive processes, influencing mental health, learning, and decision-making. The Web 2.0 era has seen a remarkable spike in the number of people sharing their experiences and emotions on online social media, mostly through posts or text messages....
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-09-01
|
| Series: | Machine Learning with Applications |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666827025000763 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849429777178427392 |
|---|---|
| author | Anjali Bhardwaj Muhammad Abulaish |
| author_facet | Anjali Bhardwaj Muhammad Abulaish |
| author_sort | Anjali Bhardwaj |
| collection | DOAJ |
| description | Human emotions are complicated and intertwined with cognitive processes, influencing mental health, learning, and decision-making. The Web 2.0 era has seen a remarkable spike in the number of people sharing their experiences and emotions on online social media, mostly through posts or text messages. Due to inherent challenges associated with textual data, the issue of discovering the intricate relationships between texts and its inherent emotions is still an increasingly prevalent topic in AI and NLP. This paper presents EmoFusion, an integrated machine learning model that improves emotion classification in textual data by integrating pre-trained word embeddings and emotion lexicons. Instead of relying on a single emotion lexicon, EmoFusion integrates multiple emotion lexicons since a single lexicon might not fully cover all possible words or phrases linked with emotions. The proposed approach uses semantically related features to bridge the semantic gap between words and emotions, capturing a wide range of emotional nuances and resulting in better classification performance. The efficacy is further improved by employing emotion-specific pre-processing techniques. EmoFusion is evaluated using three benchmark datasets, namely Google AI GoEmotions, CBET, and TEC. The evaluation results demonstrate a significant improvement compared to six baselines and a state-of-the-art technique using different classifiers. |
| format | Article |
| id | doaj-art-25ae2f61322a4e3b9adf42717a5a57f5 |
| institution | Kabale University |
| issn | 2666-8270 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Machine Learning with Applications |
| spelling | doaj-art-25ae2f61322a4e3b9adf42717a5a57f52025-08-20T03:28:14ZengElsevierMachine Learning with Applications2666-82702025-09-012110069310.1016/j.mlwa.2025.100693EmoFusion: An integrated machine learning model leveraging embeddings and lexicons to improve textual emotion classificationAnjali Bhardwaj0Muhammad Abulaish1Department of Computer Science, South Asian University, New Delhi, IndiaCorresponding author.; Department of Computer Science, South Asian University, New Delhi, IndiaHuman emotions are complicated and intertwined with cognitive processes, influencing mental health, learning, and decision-making. The Web 2.0 era has seen a remarkable spike in the number of people sharing their experiences and emotions on online social media, mostly through posts or text messages. Due to inherent challenges associated with textual data, the issue of discovering the intricate relationships between texts and its inherent emotions is still an increasingly prevalent topic in AI and NLP. This paper presents EmoFusion, an integrated machine learning model that improves emotion classification in textual data by integrating pre-trained word embeddings and emotion lexicons. Instead of relying on a single emotion lexicon, EmoFusion integrates multiple emotion lexicons since a single lexicon might not fully cover all possible words or phrases linked with emotions. The proposed approach uses semantically related features to bridge the semantic gap between words and emotions, capturing a wide range of emotional nuances and resulting in better classification performance. The efficacy is further improved by employing emotion-specific pre-processing techniques. EmoFusion is evaluated using three benchmark datasets, namely Google AI GoEmotions, CBET, and TEC. The evaluation results demonstrate a significant improvement compared to six baselines and a state-of-the-art technique using different classifiers.http://www.sciencedirect.com/science/article/pii/S2666827025000763Machine learningText miningEmotion AIEmotion classificationWord embeddingNLP |
| spellingShingle | Anjali Bhardwaj Muhammad Abulaish EmoFusion: An integrated machine learning model leveraging embeddings and lexicons to improve textual emotion classification Machine Learning with Applications Machine learning Text mining Emotion AI Emotion classification Word embedding NLP |
| title | EmoFusion: An integrated machine learning model leveraging embeddings and lexicons to improve textual emotion classification |
| title_full | EmoFusion: An integrated machine learning model leveraging embeddings and lexicons to improve textual emotion classification |
| title_fullStr | EmoFusion: An integrated machine learning model leveraging embeddings and lexicons to improve textual emotion classification |
| title_full_unstemmed | EmoFusion: An integrated machine learning model leveraging embeddings and lexicons to improve textual emotion classification |
| title_short | EmoFusion: An integrated machine learning model leveraging embeddings and lexicons to improve textual emotion classification |
| title_sort | emofusion an integrated machine learning model leveraging embeddings and lexicons to improve textual emotion classification |
| topic | Machine learning Text mining Emotion AI Emotion classification Word embedding NLP |
| url | http://www.sciencedirect.com/science/article/pii/S2666827025000763 |
| work_keys_str_mv | AT anjalibhardwaj emofusionanintegratedmachinelearningmodelleveragingembeddingsandlexiconstoimprovetextualemotionclassification AT muhammadabulaish emofusionanintegratedmachinelearningmodelleveragingembeddingsandlexiconstoimprovetextualemotionclassification |