A Modified Fuzzy Sentiment Analysis Approach Based on User Ranking Suitable for Online Social Networks

Madbouly, Magda M.; Essameldin, Reem; Darwish, Saad M.

doi:10.1007/978-3-030-31129-2_31

Cited by 5 publications

(6 citation statements)

References 15 publications

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“…According to the model trained on the training set, the test set is analyzed. In order to demonstrate the analytical performance of the proposed method on the test set, it is compared with the methods in References [12,17]. Te results are shown in Figure 3.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

“…Te analysis method based on the sentiment dictionary constructs the sentiment dictionary according to the diferent contexts of the text and makes rules to judge the sentiment tendency. For example, Reference [12] proposed a hybrid method based on dictionary technology and fuzzy classifcation technology to analyze the sentiment of the Twitter text. Using the UCINET tool for social network analysis, and combining artifcial neural network to rank users, the sentiment of tweet content is divided into seven categories, which efectively realizes text sentiment analysis.…”

Section: Related Workmentioning

confidence: 99%

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Social Network Text Sentiment Analysis Method Based on CNN-BiGRU in Big Data Environment

Shan

2023

Mobile Information Systems

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Aiming at the serious colloquialism of social network texts and the sparse semantic features, this article proposes a CNN-BiGRU-based sentiment analysis method for social network texts in the big data environment. First, the dependency syntax tree is introduced to represent the dependency relationship between words to construct the word vector to represent the text. Then, sentiment features with different granularity are extracted by multiple convolution kernels of different sizes in a convolution neural network (CNN). These sentiment features are input into bidirectional gated recurrent unit (BiGRU) network for analysis to obtain deeper sentiment features. Finally, a certain number of neurons are discarded by the Dropout method, and sentiment types are classified by the Sigmoid activation function. The Weibo_senti_100k Weibo data set is used to demonstrate the proposed method. The results show that if the Dropout value is set to 0.25 and the Adam optimizer is selected, the analysis performance is the best. The accuracy, precision, recall, and AUC are about 94.09%, 95.13%, 92.87%, and 0.953, respectively, which has certain application value.

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Section: Comparison With Other Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Social Network Text Sentiment Analysis Method Based on CNN-BiGRU in Big Data Environment

Shan

2023

Mobile Information Systems

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“…The weakness of the work is that it requires larger training time for even smaller datasets. Madbouly et al [11] proposed a hybrid classification approach of tweets based on user ranking for online social networks. The dataset consists of tweets with their feature used as input.…”

Section: Related Workmentioning

confidence: 99%

Rider Driven African Vulture Optimization with Multi Kernel Structured Text Convolutional Neural Network for Classifying e-Commerce Reviews

Zakir¹,

Jinny²

2022

IJACSA

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Opinion mining is a natural language processing based on sentiment classification technique to determine the sentiment of the reviews. The major existing text Convolutional Neural Network (CNN) algorithms are derived based on 𝟑 × 𝟑 size kernels which extract ineffective review text-features and lead to less classification accuracy. Moreover, most of the traditional CNN versions output three classes such as positive, negative, and neutral as their classification results. Hence, a novel algorithm namely 'RAVO driven Multi-Size Kernel structured Text CNN for classifying ecommerce reviews (MSK-TCNN-RAVO)' is proposed in this work. This proposed approach utilizes five multi-size kernels (𝟑 × 𝟕,𝟓 × 𝟕,𝟏 × 𝟑,𝟏 × 𝟓,𝟏 × 𝟕), multi-dimensional kernels (1D & 2D), and integrates varying size kernels to extract text-features effectively.In addition, the performance of multi-kernel CNN is highly enhanced by RAVO algorithm based on rider optimization. Moreover, the proposed approach is highly effective to process 'review-stop-words removal' that decrease the complexity and time consumption of the opinion mining process. Most existing systems use single pooling operations which reduce feature map processing performance, hence, dual pooling operations (both Max and Average pooling) are employed in this research. Furthermore, it is configured to generate five classification outputs such as bad, fair, neutral, good, and excellent to support better decisionmaking with 95.5% accuracy. This method is evaluated using different quality metrics and five review-databases to measure the performance, and the results reveal that the proposed method outperforms the other existing review classification algorithms.

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“…Sentiment analysis has recently become a popular research field. ere are many studies to specify and classify users' feelings by using social media platforms [42,43]. It is usually difficult to analyze texts for interpreting emotions.…”

Section: Related Workmentioning

confidence: 99%

Mood Detection from Physical and Neurophysical Data Using Deep Learning Models

Kilimci

Guven²,

Uysal

et al. 2019

Complexity

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Nowadays, smart devices as a part of daily life collect data about their users with the help of sensors placed on them. Sensor data are usually physical data but mobile applications collect more than physical data like device usage habits and personal interests. Collected data are usually classified as personal, but they contain valuable information about their users when it is analyzed and interpreted. One of the main purposes of personal data analysis is to make predictions about users. Collected data can be divided into two major categories: physical and behavioral data. Behavioral data are also named as neurophysical data. Physical and neurophysical parameters are collected as a part of this study. Physical data contains measurements of the users like heartbeats, sleep quality, energy, movement/mobility parameters. Neurophysical data contain keystroke patterns like typing speed and typing errors. Users’ emotional/mood statuses are also investigated by asking daily questions. Six questions are asked to the users daily in order to determine the mood of them. These questions are emotion-attached questions, and depending on the answers, users’ emotional states are graded. Our aim is to show that there is a connection between users’ physical/neurophysical parameters and mood/emotional conditions. To prove our hypothesis, we collect and measure physical and neurophysical parameters of 15 users for 1 year. The novelty of this work to the literature is the usage of both combinations of physical and neurophysical parameters. Another novelty is that the emotion classification task is performed by both conventional machine learning algorithms and deep learning models. For this purpose, Feedforward Neural Network (FFNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) neural network are employed as deep learning methodologies. Multinomial Naïve Bayes (MNB), Support Vector Regression (SVR), Decision Tree (DT), Random Forest (RF), and Decision Integration Strategy (DIS) are evaluated as conventional machine learning algorithms. To the best of our knowledge, this is the very first attempt to analyze the neurophysical conditions of the users by evaluating deep learning models for mood analysis and enriching physical characteristics with neurophysical parameters. Experiment results demonstrate that the utilization of deep learning methodologies and the combination of both physical and neurophysical parameters enhances the classification success of the system to interpret the mood of the users. A wide range of comparative and extensive experiments shows that the proposed model exhibits noteworthy results compared to the state-of-art studies.

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A Modified Fuzzy Sentiment Analysis Approach Based on User Ranking Suitable for Online Social Networks

Cited by 5 publications

References 15 publications

Social Network Text Sentiment Analysis Method Based on CNN-BiGRU in Big Data Environment

Social Network Text Sentiment Analysis Method Based on CNN-BiGRU in Big Data Environment

Rider Driven African Vulture Optimization with Multi Kernel Structured Text Convolutional Neural Network for Classifying e-Commerce Reviews

Mood Detection from Physical and Neurophysical Data Using Deep Learning Models

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