A Deep Learning Approach to Deal with Data Uncertainty in Sentiment Analysis

Capua, Michele Di; Petrosino, Alfredo

doi:10.1007/978-3-319-52962-2_15

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…• Complex systems. These problems are computationally difficult to model, especially if related to human behavior in cooperative work, as discussed in [106][107][108][109]. • Optimization problems.…”

Section: Supervised Learningmentioning

confidence: 99%

Decision-Making under Uncertainty for the Deployment of Future Hyperconnected Networks: A Survey

Alzate-Mejía

Santos-Boada

Amazonas

2021

Sensors

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Among the several emerging dimensioning, control and deployment of future communication network paradigms stands out the human-centric characteristic that creates an intricate relationship between telematics and human activities. The hard to model dynamics of user behavior introduces new uncertainties into these systems that give rise to difficult network resource management challenges. According to this context, this work reviews several decision-making computational methods under the influence of uncertainties. This work, by means of a systematic literature review, focuses on sensor-based Internet of Things scenarios such as Smart Spaces and Industry 4.0. According to our conclusions, it is mandatory to establish a means for modeling the human behavior context in order to improve resource assignment and management.

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Section: Supervised Learningmentioning

confidence: 99%

Decision-Making under Uncertainty for the Deployment of Future Hyperconnected Networks: A Survey

Alzate-Mejía

Santos-Boada

Amazonas

2021

Sensors

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“…It uses word embedding technology on different length text, and automatically mines complex semantics from non-characteristic data [6]. So deep learning is excellent in emotional classification [7], emotion extraction [8], and emotional vocabulary learning [9] of sentence or document.…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis of Movie Review Based on LSTM

2019

Proceedings of 2019 the 9th International Workshop on Computer Science and Engineering

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This paper introduces a sentiment analysis model based on LSTM (Long Short-Term Memory), which can be used for emotional polarity classification of movie review data. The model consists of five levels, namely input layer, embedding layer, LSTM layer, softmax layer, and output layer. IMDb (Internet Movie Database) dataset is used for training, and SGD (stochastic gradient descent), Adadelta, and RMSprop for optimization. The experiment result shows that Adadelta optimization algorithm can get the best training model and the lowest prediction error rate.

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A review of semi-supervised learning for text classification

Duarte

Berton

2023

Artif Intell Rev

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A huge amount of data is generated daily leading to big data challenges. One of them is related to text mining, especially text classification. To perform this task we usually need a large set of labeled data that can be expensive, time-consuming, or difficult to be obtained. Considering this scenario semi-supervised learning (SSL), the branch of machine learning concerned with using labeled and unlabeled data has expanded in volume and scope. Since no recent survey exists to overview how SSL has been used in text classification, we aim to fill this gap and present an up-to-date review of SSL for text classification. We retrieve 1794 works from the last 5 years from IEEE Xplore, ACM Digital Library, Science Direct, and Springer. Then, 157 articles were selected to be included in this review. We present the application domain, datasets, and languages employed in the works. The text representations and machine learning algorithms. We also summarize and organize the works following a recent taxonomy of SSL. We analyze the percentage of labeled data used, the evaluation metrics, and obtained results. Lastly, we present some limitations and future trends in the area. We aim to provide researchers and practitioners with an outline of the area as well as useful information for their current research.

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A Deep Learning Approach to Deal with Data Uncertainty in Sentiment Analysis

Cited by 3 publications

References 16 publications

Decision-Making under Uncertainty for the Deployment of Future Hyperconnected Networks: A Survey

Decision-Making under Uncertainty for the Deployment of Future Hyperconnected Networks: A Survey

Sentiment Analysis of Movie Review Based on LSTM

A review of semi-supervised learning for text classification

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