A Deep Convolutional Neural Network-based Model for Aspect and Polarity Classification in Hausa Movie Reviews

• URL: http://arxiv.org/abs/2405.19575v1
• Date: Wed, 29 May 2024 23:45:42 GMT
• Title: A Deep Convolutional Neural Network-based Model for Aspect and Polarity Classification in Hausa Movie Reviews
• Authors: Umar Ibrahim, Abubakar Yakubu Zandam, Fatima Muhammad Adam, Aminu Musa,
• Abstract summary: This paper introduces a novel Deep Convolutional Neural Network (CNN)-based model tailored for aspect and polarity classification in Hausa movie reviews. The proposed model combines CNNs with attention mechanisms for aspect-word prediction, leveraging contextual information and sentiment polarities. With 91% accuracy on aspect term extraction and 92% on sentiment polarity classification, the model outperforms traditional machine models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Aspect-based Sentiment Analysis (ABSA) is crucial for understanding sentiment nuances in text, especially across diverse languages and cultures. This paper introduces a novel Deep Convolutional Neural Network (CNN)-based model tailored for aspect and polarity classification in Hausa movie reviews, an underrepresented language in sentiment analysis research. A comprehensive Hausa ABSA dataset is created, filling a significant gap in resource availability. The dataset, preprocessed using sci-kit-learn for TF-IDF transformation, includes manually annotated aspect-level feature ontology words and sentiment polarity assignments. The proposed model combines CNNs with attention mechanisms for aspect-word prediction, leveraging contextual information and sentiment polarities. With 91% accuracy on aspect term extraction and 92% on sentiment polarity classification, the model outperforms traditional machine models, offering insights into specific aspects and sentiments. This study advances ABSA research, particularly in underrepresented languages, with implications for cross-cultural linguistic research.

Related papers

• Bias-Free Sentiment Analysis through Semantic Blinding and Graph Neural Networks [0.0]
  The SProp GNN relies exclusively on syntactic structures and word-level emotional cues to predict emotions in text. By semantically blinding the model to information about specific words, it is robust to biases such as political or gender bias. The SProp GNN shows performance superior to lexicon-based alternatives on two different prediction tasks, and across two languages.
  arXiv  Detail & Related papers  (2024-11-19T13:23:53Z)
• Understanding Cross-Lingual Alignment -- A Survey [52.572071017877704]
  Cross-lingual alignment is the meaningful similarity of representations across languages in multilingual language models. We survey the literature of techniques to improve cross-lingual alignment, providing a taxonomy of methods and summarising insights from throughout the field.
  arXiv  Detail & Related papers  (2024-04-09T11:39:53Z)
• A Hybrid Approach To Aspect Based Sentiment Analysis Using Transfer Learning [3.30307212568497]
  We propose a hybrid approach for Aspect Based Sentiment Analysis using transfer learning. The approach focuses on generating weakly-supervised annotations by exploiting the strengths of both large language models (LLM) and traditional syntactic dependencies.
  arXiv  Detail & Related papers  (2024-03-25T23:02:33Z)
• Convolutional Neural Networks for Sentiment Analysis on Weibo Data: A Natural Language Processing Approach [0.228438857884398]
  This study addresses the complex task of sentiment analysis on a dataset of 119,988 original tweets from Weibo using a Convolutional Neural Network (CNN) A CNN-based model was utilized, leveraging word embeddings for feature extraction, and trained to perform sentiment classification. The model achieved a macro-average F1-score of approximately 0.73 on the test set, showing balanced performance across positive, neutral, and negative sentiments.
  arXiv  Detail & Related papers  (2023-07-13T03:02:56Z)
• Investigating Fairness Disparities in Peer Review: A Language Model Enhanced Approach [77.61131357420201]
  We conduct a thorough and rigorous study on fairness disparities in peer review with the help of large language models (LMs) We collect, assemble, and maintain a comprehensive relational database for the International Conference on Learning Representations (ICLR) conference from 2017 to date. We postulate and study fairness disparities on multiple protective attributes of interest, including author gender, geography, author, and institutional prestige.
  arXiv  Detail & Related papers  (2022-11-07T16:19:42Z)
• Aspect-Based Sentiment Analysis using Local Context Focus Mechanism with DeBERTa [23.00810941211685]
  Aspect-Based Sentiment Analysis (ABSA) is a fine-grained task in the field of sentiment analysis. Recent DeBERTa model (Decoding-enhanced BERT with disentangled attention) to solve Aspect-Based Sentiment Analysis problem.
  arXiv  Detail & Related papers  (2022-07-06T03:50:31Z)
• Initial Study into Application of Feature Density and Linguistically-backed Embedding to Improve Machine Learning-based Cyberbullying Detection [54.83707803301847]
  The research was conducted on a Formspring dataset provided in a Kaggle competition on automatic cyberbullying detection. The study confirmed the effectiveness of Neural Networks in cyberbullying detection and the correlation between classifier performance and Feature Density.
  arXiv  Detail & Related papers  (2022-06-04T03:17:15Z)
• A Latent-Variable Model for Intrinsic Probing [93.62808331764072]
  We propose a novel latent-variable formulation for constructing intrinsic probes. We find empirical evidence that pre-trained representations develop a cross-lingually entangled notion of morphosyntax.
  arXiv  Detail & Related papers  (2022-01-20T15:01:12Z)
• A Deep Convolutional Neural Networks Based Multi-Task Ensemble Model for Aspect and Polarity Classification in Persian Reviews [0.0]
  We propose a multi-task learning model based on Convolutional Neural Networks (CNNs) creating a model alone may not provide the best predictions and lead to errors such as bias and high variance. This article is to create a model based on an ensemble of multi-task deep convolutional neural networks to enhance sentiment analysis in Persian reviews.
  arXiv  Detail & Related papers  (2022-01-17T09:54:35Z)
• Weakly-Supervised Aspect-Based Sentiment Analysis via Joint Aspect-Sentiment Topic Embedding [71.2260967797055]
  We propose a weakly-supervised approach for aspect-based sentiment analysis. We learn sentiment, aspect> joint topic embeddings in the word embedding space. We then use neural models to generalize the word-level discriminative information.
  arXiv  Detail & Related papers  (2020-10-13T21:33:24Z)
• Sentiment Analysis Based on Deep Learning: A Comparative Study [69.09570726777817]
  The study of public opinion can provide us with valuable information. The efficiency and accuracy of sentiment analysis is being hindered by the challenges encountered in natural language processing. This paper reviews the latest studies that have employed deep learning to solve sentiment analysis problems.
  arXiv  Detail & Related papers  (2020-06-05T16:28:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.