Data properties and the performance of sentiment classification for electronic commerce applications

Choi, Youngseok; Lee, Habin

doi:10.1007/s10796-017-9741-7

Cited by 47 publications

(27 citation statements)

References 64 publications

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“…From the analysis of overall accuracy, recall, precision, F-scores, AUC values, and CPU times, we highlight some patterns for high and low performance of the sentiment analysis methods. We are aware that different types of datasets influence the results of a sentiment analysis differently [76].…”

Section: Resultsmentioning

confidence: 99%

Sentiment Analysis Based on Deep Learning: A Comparative Study

2020

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The study of public opinion can provide us with valuable information. The analysis of sentiment on social networks, such as Twitter or Facebook, has become a powerful means of learning about the users' opinions and has a wide range of applications. However, the efficiency and accuracy of sentiment analysis is being hindered by the challenges encountered in natural language processing (NLP). In recent years, it has been demonstrated that deep learning models are a promising solution to the challenges of NLP. This paper reviews the latest studies that have employed deep learning to solve sentiment analysis problems, such as sentiment polarity. Models using term frequency-inverse document frequency (TF-IDF) and word embedding have been applied to a series of datasets. Finally, a comparative study has been conducted on the experimental results obtained for the different models and input features.

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Section: Resultsmentioning

confidence: 99%

Sentiment Analysis Based on Deep Learning: A Comparative Study

2020

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“…One implication of this is that the characteristics of datasets may influence the most appropriate one. This conjecture is supported by research from [71] which suggests the need to consider the characteristics of datasets when choosing a classification algorithm. However, this would need further validation, and thus, this is left for future work.…”

Section: ) Comparison Of Fitness Values Based On the Number Of Executed Cnnsmentioning

confidence: 92%

An Automatic Convolutional Neural Network Optimization Using a Diversity-Guided Genetic Algorithm

Fatyanosa

Aritsugi

2021

IEEE Access

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Hyperparameters and architecture greatly influence the performance of convolutional neural networks (CNNs); therefore, their optimization is important to obtain the desired results. One of the state-ofthe-art methods to achieve this is the use of neuroevolution that utilizes a genetic algorithm (GA) to optimize a CNN. However, the GA is often trapped into a local optimum resulting in premature convergence. In this study, we propose an approach called the "diversity-guided genetic algorithm-convolutional neural network (DGGA-CNN)" that uses adaptive parameter control and random injection to facilitate the search process by exploration and exploitation while preserving the population diversity. The alternation between exploration and exploitation is guided by using an average pairwise Hamming distance. Moreover, the DGGA fully handles the architecture of the CNN by using a novel finite state machine (FSM) combined with three novel mutation mechanisms that are specifically created for architecture chromosomes. Tests conducted on suggestion mining and twitter airline datasets reveal that the DGGA-CNN performs well with valid architectures and a comparison with other methods demonstrates its capability and efficiency.

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“…This metric allows you to determine the overall customer attitude to the product. Usually reviews are divided into 3 categories: positive, negative and neutral [9,[10][11][12][13].…”

Section: Decision Makingmentioning

confidence: 99%

“…Thus, a multidimensional table with a form [10,4,10] becomes a global multidimensional table with a form [10,10] after merging.…”

Section: The Neural Network Trainingmentioning

confidence: 99%

Neural Network Approach for Emotional Recognition in Text

Назаренко

Afanasieva

Golian

2019

Scientific journal “Bionics of Intelligence”

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The article is devoted to one of the most popular trends in the field of IT today – natural language processing, in particular, the extraction of emotions from the text using the neural network approach. The main task was to solve the problem of the high costs of time and human resources for companies to receive feedback from users and process emotional reactions of the second one. That to decide the task it was necessary to make modelling and learn neural network using own architecture based on the backpropagation algorithm that to recognize the emotional component in the text.The emotional component of reviews was used as a metric for evaluating user reactions. It was decided to work with five types of emotions that will help to provide better results. The neural network architecture consists of interconnected layers: embedding, bidirectional LSTM, pooling, dropout layers and two dense layers. For the neural network learning was selected an open dataset consisted of 47,288-tagged posts from Twitter. As a result, the F-measure on the test dataset was 0.62 and which is a worthy indicator in comparison with large business solutions.

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Data properties and the performance of sentiment classification for electronic commerce applications

Cited by 47 publications

References 64 publications

Sentiment Analysis Based on Deep Learning: A Comparative Study

Sentiment Analysis Based on Deep Learning: A Comparative Study

An Automatic Convolutional Neural Network Optimization Using a Diversity-Guided Genetic Algorithm

Neural Network Approach for Emotional Recognition in Text

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