Sentiment Analysis and Classification Using Deep Semantic Information and Contextual Knowledge

Al-Absi, Ahmed Abdulhakim; Kang, Dae-Ki; Al-Absi, Mohammed Abdulhakim

doi:10.32604/cmc.2023.030262

Cited by 4 publications

(3 citation statements)

References 33 publications

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“…This method is called aspect-based sentiment analysis. As presented in [37], aspect-based sentiment analysis was provided to identify sentiment polarities based on different attributes or aspects. The authors applied a framework for extracting aspects from Twitter datasets, and sentiment analysis was applied based on machine learning methods.…”

Section: Machine Learning-based Approachmentioning

confidence: 99%

Innovative Forward Fusion Feature Selection Algorithm for Sentiment Analysis Using Supervised Classification

et al. 2023

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Sentiment analysis is considered one of the significant trends of the recent few years. Due to the high importance and increasing use of social media and electronic services, the need for reviewing and enhancing the provided services has become crucial. Revising the user services is based mainly on sentiment analysis methodologies for analyzing users’ polarities to different products and applications. Sentiment analysis for Arabic reviews is a major concern due to high morphological linguistics and complex polarity terms expressed in the reviews. In addition, the users can present their orientation towards a service or a product by using a hybrid or mix of polarity terms related to slang and standard terminologies. This paper provides a comprehensive review of recent sentiment analysis methods based on lexicon or machine learning (ML). The comparison provides a clear vision of the number of classes, the used dialect, the annotated algorithms, and their performance. The proposed methodology is based on cross-validation of Arabic data using a k-fold mechanism that splits the dataset into training and testing folds; subsequently, the data preprocessing is executed to clean sentiments from unwanted terms that can affect data analysis. A vectorization of the dataset is then applied using TF–IDF for counting word and polarity terms. Furthermore, a feature selection stage is processed using Pearson, Chi2, and Random Forest (RF) methods for mapping the compatibility between input and target features. This paper also proposed an algorithm called the forward fusion feature for sentiment analysis (FFF-SA) to provide a feature selection that applied different machine learning (ML) classification models for each chunk of k features and accumulative features on the Arabic dataset. The experimental results measured and scored all accuracies between the feature importance method and ML models. The best accuracy is recorded with the Naïve Bayes (NB) model with the RF method.

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Section: Machine Learning-based Approachmentioning

confidence: 99%

Innovative Forward Fusion Feature Selection Algorithm for Sentiment Analysis Using Supervised Classification

et al. 2023

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“…Sentiment analysis is a natural language processing technology designed to automatically identify and extract the emotion or emotional color contained in text, audio, images, and other data sources [7]. The significance of sentiment analysis is that it can help people better understand and analyze a large amount of content on social media, such as users' attitudes and opinions on a certain product, brand, political event, etc.…”

Section: Related Workmentioning

confidence: 99%

Context-Dependent Multimodal Sentiment Analysis Based on a Complex Attention Mechanism

Deng,

Liu,

et al. 2023

Electronics

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Multimodal sentiment analysis aims to understand people’s attitudes and opinions from different data forms. Traditional modality fusion methods for multimodal sentiment analysis con-catenate or multiply various modalities without fully utilizing context information and the correlation between modalities. To solve this problem, this article provides a new model based on a multimodal sentiment analysis framework based on a recurrent neural network with a complex attention mechanism. First, after the raw data is preprocessed, the numerical feature representation is obtained using feature extraction. Next, the numerical features are input into the recurrent neural network, and the output results are multimodally fused using a complex attention mechanism layer. The objective of the complex attention mechanism is to leverage enhanced non-linearity to more effectively capture the inter-modal correlations, thereby improving the performance of multimodal sentiment analysis. Finally, the processed results are fed into the classification layer and the sentiment output is obtained using the classification layer. This process can effectively capture the semantic information and contextual relationship of the input sequence and fuse different pieces of modal information. Our model was tested on the CMU-MOSEI datasets, achieving an accuracy of 82.04%.

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“…SA is a subset of natural language processing (NLP) that can be used in various realworld applications, including fnancial and stock price forecasting [2], politics [3], and medicine [4,5]. Many researchers have dedicated signifcant eforts to investigating textual SA [6][7][8][9][10] through various methodologies, resulting in notable advancements on social media platforms. One signifcant constraint of current sentiment classifcation systems is their predominant dependence on a post or tweet's textual content.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Sentiment Classification Method with the Help of Neighbors and a Hybrid of RNN Models

Salman Al-Tameemi,

Feizi-Derakhshi,

Pashazadeh

et al. 2023

Complexity

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Some texts that are challenging to recognize on their own may become more understandable in a neighborhood of related texts with similar contexts. Motivated by this intuition, a novel deep text sentiment classification (DTSC) model is proposed to improve the model’s performance by incorporating the neighborhood of related texts. Our framework uses a nonparametric approach to construct neighborhoods of related texts based on Jaccard similarities. Then, a new deep recurrent neural network architecture is proposed, comprising two distinct modules: bidirectional long short-term memory (Bi-LSTM) and gated recurrent unit (GRU). The proposed model aims to effectively capture informative features from the input text and its neighbors. The result of each module is processed through the maximum operation, which selects the most pertinent data. Finally, the extracted features are concatenated and subjected to classification to achieve accurate sentiment prediction. Previous studies have commonly employed a parametric approach to represent textual metadata. However, our approach utilizes a nonparametric approach, enabling our model to perform strongly even when the text vocabulary varies between training and testing. The proposed DTSC model has been evaluated on five real-world sentiment datasets, achieving 99.60% accuracy on the Binary_Getty (BG) dataset, 98.32% accuracy on the Binary_iStock (BIS) dataset, 96.13% accuracy on Twitter, 82.19% accuracy on the multi-view sentiment analysis (MVSA) dataset, and 87.60% accuracy on the IMDB dataset. These findings demonstrate that the proposed model outperforms established baseline techniques in terms of model evaluation criteria for text sentiment classification.

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Sentiment Analysis and Classification Using Deep Semantic Information and Contextual Knowledge

Cited by 4 publications

References 33 publications

Innovative Forward Fusion Feature Selection Algorithm for Sentiment Analysis Using Supervised Classification

Innovative Forward Fusion Feature Selection Algorithm for Sentiment Analysis Using Supervised Classification

Context-Dependent Multimodal Sentiment Analysis Based on a Complex Attention Mechanism

An Efficient Sentiment Classification Method with the Help of Neighbors and a Hybrid of RNN Models

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