Sentiment analysis via multi-layer perceptron trained by meta-heuristic optimisation

Alboaneen, Dabiah; Tianfield, Huaglory; Zhang, Yan

doi:10.1109/bigdata.2017.8258507

Cited by 26 publications

(14 citation statements)

References 12 publications

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“…Deep learning has shown robust capabilities for sentiment multi-class sentiment analysis [32], [45]. In our work, we attempt to use Multi-layer perception (MLP) and Long-Short-Term-Memory (LSTM) [46] architectures for our three-class sentiment analysis.…”

Section: Deep Learningmentioning

confidence: 99%

Light Gradient Boosting Machine for General Sentiment Classification on Short Texts: A Comparative Evaluation

2020

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Recently, the focus on sentiment analysis has been domain dependent even though the expressions used by the public are unsophisticatedly familiar regardless of the topics or domains. Online social media (OSNs) has been a daily venue for informal conversational contents from various domains ranging from sports and cooking to politics and human rights. Generating specific resources for every domain independently requires high cost and extensive efforts. In response, we propose to build a general multi-class sentiment classifier using our Domain-Free Sentiment Multimedia Dataset (DFSMD). Based on the proven capabilities of Light Gradient Boosting Machine (LGBM) in dealing with high dimensional and imbalance data, we have trained an LGBM model to recognize one of three sentiments of tweets: positive, negative, or neutral. We have conducted extensive comparisons and evaluations for six other standard sentiment classification algorithms and different sets of features including OSNs-specific ones. Our results have shown that LGBM model is the winner among the other six algorithms. It has been also shown that our dataset contains distinguishing characteristics in the three classes. Moreover, hashtag words are shown to be significantly important in capturing the sentiments of tweets. In addition, our findings have revealed the effectiveness of our approach in adapting general-domain sentiment to domain-specific sentiment analysis.

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Section: Deep Learningmentioning

confidence: 99%

Light Gradient Boosting Machine for General Sentiment Classification on Short Texts: A Comparative Evaluation

2020

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“…Several studies have been reported on text sentiment analysis using classical machine learning methods such as Bayesian Network [30], Multi Layer Perceptron [31], and Logistic Regression [32]. However, there have been numerous studies that use deep learning for sentiment analysis.…”

Section: Text-based Sentiment Analysismentioning

confidence: 99%

Multiview Sentiment Analysis with Image-Text-Concept Features of Indonesian Social Media Posts

Setiawan¹

2021

IJIES

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Social media development makes it possible for everyone to express their opinions and information through text, speech, video, or images. Multiview sentiment analysis in current studies generally combines two modalities, text and image. It seeks to classify social media posts into two or more polarities, such as positive, neutral, or negative. To improve the performance of multiview Sentiment Analysis, we added another modality, which is concepts derived from text and image. Our proposed model integrates three views into a fusion with an ensemble approach by a metaclassifier. We performed text classification with Deep Convolutional Neural Networks. The input feature is Word2Vec for text representation in order to preserve semantic meaning. Additionally, we analyzed concepts from texts with SenticNet 5 as a knowledge base model and extracted concepts from images using the DeepSentiBank model. We obtained 2089 Adjective Noun Pairs and classified it with Multi-Layer Perceptron. Then we combined predicted probabilities from each classifier for Image, Text, and Concept by Ensemble Learning. A meta-classifier was implemented to predict the final sentiment from a fusion of Image-Text-Concept features. The fusion for multiview sentiment analysis works well and could achieve the best accuracy of 70% by applying the ensemble approach with Logistic Regression as the meta-classifier.

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“…These kinds of networks have provided good results in SA tasks. For example, [45] used a deep-learning network based on multi-layer perceptrons to implement a classifier that outperformed other baseline methods based on genetic algorithms.…”

Section: Feature Engineeringmentioning

confidence: 99%

Ontology-driven aspect-based sentiment analysis classification: An infodemiological case study regarding infectious diseases in Latin America

García-Díaz

Cánovas-García

Valencia‐García

2020

Future Generation Computer Systems

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Infodemiology is the process of mining unstructured and textual data so as to provide public health officials and policymakers with valuable information regarding public health. The appearance of this new data source, which was previously unimaginable, has opened up a new way in which to improve public health systems, resulting in better communication policies and better detection systems. However, the unstructured nature of the Internet, along with the complexity of the infectious disease domain, prevents the information extracted from being easily understood. Moreover, when dealing with languages other than English, for which some of the most common Natural Language Processing resources are not available, the correct exploitation of this data becomes even more difficult. We intend to fill these gaps proposing an ontology-driven aspect-based sentiment analysis with which to measure the general public's opinions as regards infectious diseases when expressed in Spanish by employing a case study of tweets concerning the Zika, Dengue and Chikungunya viruses in Latin America. Our proposal is based on two technologies. We first use ontologies in order to model the infectious disease domain with concepts such as risks, symptoms, transmission methods or drugs, among other concepts. We then measure the relationship between these concepts in order to determine the degree to which one concept influences other concepts. This new information is subsequently applied in order to build an aspect-based sentiment analysis model based on statistical and linguistic features. This is done by applying deep-learning models. Our proposal is available on a web platform, where users can see the sentiment for each concept at a glance and analyse how each concept influences the sentiment of the others.

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Sentiment analysis via multi-layer perceptron trained by meta-heuristic optimisation

Cited by 26 publications

References 12 publications

Light Gradient Boosting Machine for General Sentiment Classification on Short Texts: A Comparative Evaluation

Light Gradient Boosting Machine for General Sentiment Classification on Short Texts: A Comparative Evaluation

Multiview Sentiment Analysis with Image-Text-Concept Features of Indonesian Social Media Posts

Ontology-driven aspect-based sentiment analysis classification: An infodemiological case study regarding infectious diseases in Latin America

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