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Between History and Natural Language Processing: Study, Enrichment and Online Publication of French Parliamentary Debates of the Early Third Republic (1881-1899)
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In: ParlaCLARIN III at LREC2022 - Workshop on Creating, Enriching and Using Parliamentary Corpora ; https://hal.archives-ouvertes.fr/hal-03623351 ; ParlaCLARIN III at LREC2022 - Workshop on Creating, Enriching and Using Parliamentary Corpora, Jun 2022, Marseille, France ; https://www.clarin.eu/ParlaCLARIN-III (2022)
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Chinese-Uyghur Bilingual Lexicon Extraction Based on Weak Supervision
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In: Information; Volume 13; Issue 4; Pages: 175 (2022)
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Investigating the Efficient Use of Word Embedding with Neural-Topic Models for Interpretable Topics from Short Texts
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In: Sensors; Volume 22; Issue 3; Pages: 852 (2022)
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Analysis of the Effects of Lockdown on Staff and Students at Universities in Spain and Colombia Using Natural Language Processing Techniques
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In: International Journal of Environmental Research and Public Health; Volume 19; Issue 9; Pages: 5705 (2022)
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An Enhanced Neural Word Embedding Model for Transfer Learning
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In: Applied Sciences; Volume 12; Issue 6; Pages: 2848 (2022)
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Deep Sentiment Analysis Using CNN-LSTM Architecture of English and Roman Urdu Text Shared in Social Media
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In: Applied Sciences; Volume 12; Issue 5; Pages: 2694 (2022)
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Abstract:
Sentiment analysis (SA) has been an active research subject in the domain of natural language processing due to its important functions in interpreting people’s perspectives and drawing successful opinion-based judgments. On social media, Roman Urdu is one of the most extensively utilized dialects. Sentiment analysis of Roman Urdu is difficult due to its morphological complexities and varied dialects. The purpose of this paper is to evaluate the performance of various word embeddings for Roman Urdu and English dialects using the CNN-LSTM architecture with traditional machine learning classifiers. We introduce a novel deep learning architecture for Roman Urdu and English dialect SA based on two layers: LSTM for long-term dependency preservation and a one-layer CNN model for local feature extraction. To obtain the final classification, the feature maps learned by CNN and LSTM are fed to several machine learning classifiers. Various word embedding models support this concept. Extensive tests on four corpora show that the proposed model performs exceptionally well in Roman Urdu and English text sentiment classification, with an accuracy of 0.904, 0.841, 0.740, and 0.748 against MDPI, RUSA, RUSA-19, and UCL datasets, respectively. The results show that the SVM classifier and the Word2Vec CBOW (Continuous Bag of Words) model are more beneficial options for Roman Urdu sentiment analysis, but that BERT word embedding, two-layer LSTM, and SVM as a classifier function are more suitable options for English language sentiment analysis. The suggested model outperforms existing well-known advanced models on relevant corpora, improving the accuracy by up to 5%.
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Keyword:
deep learning; LSTM; machine learning; Roman Urdu language; sentiment analysis; word embedding
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URL: https://doi.org/10.3390/app12052694
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Predicting Academic Performance: Analysis of Students’ Mental Health Condition from Social Media Interactions
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In: Behavioral Sciences; Volume 12; Issue 4; Pages: 87 (2022)
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Vec2Dynamics: A Temporal Word Embedding Approach to Exploring the Dynamics of Scientific Keywords—Machine Learning as a Case Study
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In: Big Data and Cognitive Computing; Volume 6; Issue 1; Pages: 21 (2022)
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Methods, Models and Tools for Improving the Quality of Textual Annotations
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In: Modelling; Volume 3; Issue 2; Pages: 224-242 (2022)
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Creating multi-scripts sentiment analysis lexicons for Algerian, Moroccan and Tunisian dialects
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In: 7th International Conference on Data Mining (DTMN 2021) Computer Science Conference Proceedings in Computer Science & Information Technology (CS & IT) ; https://hal.archives-ouvertes.fr/hal-03308111 ; 7th International Conference on Data Mining (DTMN 2021) Computer Science Conference Proceedings in Computer Science & Information Technology (CS & IT), Sep 2021, Copenhagen, Denmark (2021)
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Bilingual English-German word embedding models for scientific text ...
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Bilingual English-German word embedding models for scientific text ...
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以《Cofacts 真的假的》資料庫為基礎建立中文科學假訊息之探勘模型 ; Text Mining Model for Detecting Chinese Fake Scientific Messages based on Cofacts Open Data
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Automatic Part-of-Speech Tagging for Security Vulnerability Descriptions ...
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Automatic Part-of-Speech Tagging for Security Vulnerability Descriptions ...
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Text ranking based on semantic meaning of sentences ; Textrankning baserad på semantisk betydelse hos meningar
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Efficient Estimate of Low-Frequency Words’ Embeddings Based on the Dictionary: A Case Study on Chinese
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In: Applied Sciences ; Volume 11 ; Issue 22 (2021)
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Acoustic Word Embeddings for End-to-End Speech Synthesis
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In: Applied Sciences ; Volume 11 ; Issue 19 (2021)
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