AI Empowered Sentiment Analysis

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 20045

Special Issue Editors

School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen 528406, China
Interests: computational social sciences; AI IoT; smart cities
Associate Professor, School of Software, Dalian University of Technology, Dalian 116024, China
Interests: artificial intelligence; machine learning; data mining

Special Issue Information

Dear Colleagues,

Data on various Internet platforms, which contain valuable information that is helpful for decision making, are growing explosively. Sentiment analysis aims to extract and analyze people’s attitudes toward opinion targets. However, due to the large amount of data, quick and accurate completion of sentiment analysis is still challenging. The application of AI technology has greatly promoted the development of sentiment analysis. Traditional sentiment analysis mainly relies on manpower, a process which is not only time-consuming and laborious but also unable to analyze sentiment comprehensively and accurately. The advantage of artificial intelligence lies in the integration and utilization of big data technology, which can adopt an automatic coding mode to classify and summarize sentiment colors and comprehensively improve the functionality of the algorithm. By making use of intelligent analysis, identification and judgment in artificial intelligence, accurate recognition and analysis of sentiment can be realized. In order to improve AI applications in sentiment analysis, new theories, technologies, architectures, algorithms and mechanisms are needed. This Special Issue aims to gather relevant research from industry and academia detailing the latest findings and developments in the field of AI for sentiment analysis. We invite high-quality paper submissions of theoretical and experimental nature on topics including, but not limited to: multi-modal sentiment analysis; aspect-based sentiment analysis; resources for sentiment analysis; transfer learning for sentiment analysis; and sentiment-controlled text generation.

Prof. Dr. Xiangjie Kong
Prof. Dr. Wei Wang
Dr. Han Liu
Guest Editors

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Keywords

  • artificial intelligence technology
  • sentiment analysis
  • opinion mining
  • deep learning
  • natural language processing

Published Papers (12 papers)

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Research

21 pages, 1115 KiB  
Article
CCDA: A Novel Method to Explore the Cross-Correlation in Dual-Attention for Multimodal Sentiment Analysis
Appl. Sci. 2024, 14(5), 1934; https://doi.org/10.3390/app14051934 - 27 Feb 2024
Viewed by 197
Abstract
With the development of the Internet, the content that people share contains types of text, images, and videos, and utilizing these multimodal data for sentiment analysis has become an important area of research. Multimodal sentiment analysis aims to understand and perceive emotions or [...] Read more.
With the development of the Internet, the content that people share contains types of text, images, and videos, and utilizing these multimodal data for sentiment analysis has become an important area of research. Multimodal sentiment analysis aims to understand and perceive emotions or sentiments in different types of data. Currently, the realm of multimodal sentiment analysis faces various challenges, with a major emphasis on addressing two key issues: (1) inefficiency when modeling the intramodality and intermodality dynamics and (2) inability to effectively fuse multimodal features. In this paper, we propose the CCDA (cross-correlation in dual-attention) model, a novel method to explore dynamics between different modalities and fuse multimodal features efficiently. We capture dynamics at intra- and intermodal levels by using two types of attention mechanisms simultaneously. Meanwhile, the cross-correlation loss is introduced to capture the correlation between attention mechanisms. Moreover, the relevant coefficient is proposed to integrate multimodal features effectively. Extensive experiments were conducted on three publicly available datasets, CMU-MOSI, CMU-MOSEI, and CH-SIMS. The experimental results fully confirm the effectiveness of our proposed method, and, compared with the current optimal method (SOTA), our model shows obvious advantages in most of the key metrics, proving its better performance in multimodal sentiment analysis. Full article
(This article belongs to the Special Issue AI Empowered Sentiment Analysis)
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16 pages, 4178 KiB  
Article
Implementing and Evaluating a Font Recommendation System Through Emotion-Based Content-Font Mapping
Appl. Sci. 2024, 14(3), 1123; https://doi.org/10.3390/app14031123 - 29 Jan 2024
Viewed by 447
Abstract
Rapid digital content growth demands pivotal font selection for design and communication. Our study focuses on a font recommendation system that aligns fonts with content emotions. To achieve this, we define font-emotions and quantify them. Additionally, we leverage deep learning techniques for content [...] Read more.
Rapid digital content growth demands pivotal font selection for design and communication. Our study focuses on a font recommendation system that aligns fonts with content emotions. To achieve this, we define font-emotions and quantify them. Additionally, we leverage deep learning techniques for content analysis. Understanding common emotional perceptions, we aimed to align fonts with content emotions. After evaluating diverse mapping methods, we determined a correlation analysis-based model to be most effective. Implementing this model, we verified its utility through usability evaluations. Our proposed system not only assists users with limited design knowledge in receiving contextually fitting font suggestions but also extends its application across various digital content realms. Full article
(This article belongs to the Special Issue AI Empowered Sentiment Analysis)
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20 pages, 5387 KiB  
Article
M2ER: Multimodal Emotion Recognition Based on Multi-Party Dialogue Scenarios
Appl. Sci. 2023, 13(20), 11340; https://doi.org/10.3390/app132011340 - 16 Oct 2023
Viewed by 679
Abstract
Researchers have recently focused on multimodal emotion recognition, but issues persist in recognizing emotions in multi-party dialogue scenarios. Most studies have only used text and audio modality, ignoring the video modality. To address this, we propose M2ER, a multimodal emotion r [...] Read more.
Researchers have recently focused on multimodal emotion recognition, but issues persist in recognizing emotions in multi-party dialogue scenarios. Most studies have only used text and audio modality, ignoring the video modality. To address this, we propose M2ER, a multimodal emotion recognition scheme based on multi-party dialogue scenarios. Addressing the issue of multiple faces appearing in the same frame of the video modality, M2ER introduces a method using multi-face localization for speaker recognition to eliminate the interference of non-speakers. The attention mechanism is used to fuse and classify different modalities. We conducted extensive experiments in unimodal and multimodal fusion using the multi-party dialogue dataset MELD. The results show that M2ER achieves superior emotion recognition in both text and audio modalities compared to the baseline model. The proposed method using speaker recognition in the video modality improves emotion recognition performance by 6.58% compared to the method without speaker recognition. In addition, the multimodal fusion based on the attention mechanism also outperforms the baseline fusion model. Full article
(This article belongs to the Special Issue AI Empowered Sentiment Analysis)
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20 pages, 1474 KiB  
Article
Know an Emotion by the Company It Keeps: Word Embeddings from Reddit/Coronavirus
Appl. Sci. 2023, 13(11), 6713; https://doi.org/10.3390/app13116713 - 31 May 2023
Cited by 1 | Viewed by 947
Abstract
Social media is a crucial communication tool (e.g., with 430 million monthly active users in online forums such as Reddit), being an objective of Natural Language Processing (NLP) techniques. One of them (word embeddings) is based on the quotation, “You shall know a [...] Read more.
Social media is a crucial communication tool (e.g., with 430 million monthly active users in online forums such as Reddit), being an objective of Natural Language Processing (NLP) techniques. One of them (word embeddings) is based on the quotation, “You shall know a word by the company it keeps,” highlighting the importance of context in NLP. Meanwhile, “Context is everything in Emotion Research.” Therefore, we aimed to train a model (W2V) for generating word associations (also known as embeddings) using a popular Coronavirus Reddit forum, validate them using public evidence and apply them to the discovery of context for specific emotions previously reported as related to psychological resilience. We used Pushshiftr, quanteda, broom, wordVectors, and superheat R packages. We collected all 374,421 posts submitted by 104,351 users to Reddit/Coronavirus forum between January 2020 and July 2021. W2V identified 64 terms representing the context for seven positive emotions (gratitude, compassion, love, relief, hope, calm, and admiration) and 52 terms for seven negative emotions (anger, loneliness, boredom, fear, anxiety, confusion, sadness) all from valid experienced situations. We clustered them visually, highlighting contextual similarity. Although trained on a “small” dataset, W2V can be used for context discovery to expand on concepts such as psychological resilience. Full article
(This article belongs to the Special Issue AI Empowered Sentiment Analysis)
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13 pages, 954 KiB  
Article
Predicting Consumer Personalities from What They Say
Appl. Sci. 2023, 13(10), 6148; https://doi.org/10.3390/app13106148 - 17 May 2023
Viewed by 1125
Abstract
This study mapped personality based on the newly proposed extraction method from consumers’ textual data and revealed the relevance (attention) and polarity (affection) of words associated with a specific personality trait. Furthermore, we illustrate how unique words are used to predict a consumer’s [...] Read more.
This study mapped personality based on the newly proposed extraction method from consumers’ textual data and revealed the relevance (attention) and polarity (affection) of words associated with a specific personality trait. Furthermore, we illustrate how unique words are used to predict a consumer’s behavior associated with certain personality traits. In this study, we employed the scales of the Kaggle MBTI Personality dataset to examine the methodology’s effectiveness, extract the personality traits from the textual data into features, and map them into the traits/dimensions of the existing scale. Based on the results obtained in this study, we assert that using the TF-IDF algorithm is a good way to generate a custom dictionary. Furthermore, sentiment scoring with an AI-empowered machine learning algorithm provides useful data to filter and validate more coherent words to understand and, thus, communicate a particular aspect of personality. Finally, we proposed that four situations involving the interaction between attention (frequency) and affection (sentiment) allow us to better understand the consumer and how to use the feature words in terms of the interaction between attention (TF-IDF score) and affection (sentiment score). Full article
(This article belongs to the Special Issue AI Empowered Sentiment Analysis)
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17 pages, 7470 KiB  
Article
A Pipeline for Story Visualization from Natural Language
Appl. Sci. 2023, 13(8), 5107; https://doi.org/10.3390/app13085107 - 19 Apr 2023
Viewed by 1340
Abstract
Generating automatic visualization from natural language texts is an important task for promoting language learning and literacy development for young children and language learners. However, translating a text into a coherent visualization matching its relevant keywords is a challenging problem. To tackle this [...] Read more.
Generating automatic visualization from natural language texts is an important task for promoting language learning and literacy development for young children and language learners. However, translating a text into a coherent visualization matching its relevant keywords is a challenging problem. To tackle this issue, we proposed a robust story visualization pipeline ranging from NLP and relation extraction to image sequence generation and alignment. First, we applied a shallow semantic representation of the text where we extracted concepts including relevant characters, scene objects, and events in an appropriate format. We also distinguished between simple and complex actions. This distinction helped to realize an optimal visualization of the scene objects and their relationships according to the target audience. Second, we utilized an image generation framework along with different versions to support the visualization task efficiently. Third, we used CLIP similarity function as a semantic relevance metric to check local and global coherence to the whole story. Finally, we validated the scene sequence to compose a final visualization using the different versions for various target audiences. Our preliminary results showed considerable effectiveness in adopting such a pipeline for a coarse visualization task that can subsequently be enhanced. Full article
(This article belongs to the Special Issue AI Empowered Sentiment Analysis)
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14 pages, 6263 KiB  
Article
SSEMGAT: Syntactic and Semantic Enhanced Multi-Layer Graph Attention Network for Aspect-Level Sentiment Analysis
Appl. Sci. 2023, 13(8), 5085; https://doi.org/10.3390/app13085085 - 19 Apr 2023
Cited by 3 | Viewed by 1113
Abstract
Aspect-level sentiment analysis aims to identify the sentiment polarity of specific aspects appearing in a given sentence or review. The model based on graph structure uses a dependency tree to link the aspect word with its corresponding opinion word and achieves significant results. [...] Read more.
Aspect-level sentiment analysis aims to identify the sentiment polarity of specific aspects appearing in a given sentence or review. The model based on graph structure uses a dependency tree to link the aspect word with its corresponding opinion word and achieves significant results. However, for some sentences with ambiguous syntactic structure, it is difficult for the dependency tree to accurately parse the dependencies, which introduces noise and degrades the performance of the model. Based on this, we propose a syntactic and semantic enhanced multi-layer graph attention network (SSEMGAT), which introduces constituent trees in syntactic features to compensate for dependent trees at the clause level, exploiting aspect-aware attention in semantic features to assign the attention weight of specific aspects between contexts. The enhanced syntactic and semantic features are then used to classify specific aspects of sentiment through a multi-layer graph attention network. Accuracy and Macro-F1 are used as evaluation indexes in the SemEval-2014 Task 4 Restaurant and Laptop dataset and the Twitter dataset to compare the proposed model with the baseline model and the latest model, achieving competitive results. Full article
(This article belongs to the Special Issue AI Empowered Sentiment Analysis)
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19 pages, 5517 KiB  
Article
Integration of Multi-Branch GCNs Enhancing Aspect Sentiment Triplet Extraction
Appl. Sci. 2023, 13(7), 4345; https://doi.org/10.3390/app13074345 - 29 Mar 2023
Cited by 1 | Viewed by 1376
Abstract
Aspect Sentiment Triplet Extraction (ASTE) is a complex and challenging task in Natural Language Processing (NLP). It aims to extract the triplet of aspect term, opinion term, and their associated sentiment polarity, which is a more fine-grained study in Aspect Based Sentiment Analysis. [...] Read more.
Aspect Sentiment Triplet Extraction (ASTE) is a complex and challenging task in Natural Language Processing (NLP). It aims to extract the triplet of aspect term, opinion term, and their associated sentiment polarity, which is a more fine-grained study in Aspect Based Sentiment Analysis. Furthermore, there have been a large number of approaches being proposed to handle this relevant task. However, existing methods for ASTE suffer from powerless interactions between different sources of textual features, and they usually exert an equal impact on each type of feature, which is quite unreasonable while building contextual representation. Therefore, in this paper, we propose a novel Multi-Branch GCN (MBGCN)-based ASTE model to solve this problem. Specifically, our model first generates the enhanced semantic features via the structure-biased BERT, which takes the position of tokens into the transformation of self-attention. Then, a biaffine attention module is utilized to further obtain the specific semantic feature maps. In addition, to enhance the dependency among words in the sentence, four types of linguistic relations are defined, namely part-of-speech combination, syntactic dependency type, tree-based distance, and relative position distance of each word pair, which are further embedded as adjacent matrices. Then, the widely used Graph Convolutional Network (GCN) module is utilized to complete the work of integrating the semantic feature and linguistic feature, which is operated on four types of dependency relations repeatedly. Additionally, an effective refining strategy is employed to detect whether word pairs match or not, which is conducted after the operation of each branch GCN. At last, a shallow interaction layer is designed to achieve the final textual representation by fusing the four branch features with different weights. To validate the effectiveness of MBGCNs, extensive experiments have been conducted on four public and available datasets. Furthermore, the results demonstrate the effectiveness and robustness of MBGCNs, which obviously outperform state-of-the-art approaches. Full article
(This article belongs to the Special Issue AI Empowered Sentiment Analysis)
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11 pages, 1401 KiB  
Article
Sentiment Analysis of Comment Texts on Online Courses Based on Hierarchical Attention Mechanism
Appl. Sci. 2023, 13(7), 4204; https://doi.org/10.3390/app13074204 - 26 Mar 2023
Cited by 3 | Viewed by 1303
Abstract
With information technology pushing the development of intelligent teaching environments, the online teaching platform emerges timely around the globe, and how to accurately evaluate the effect of the “any-time and anywhere” teacher–student interaction and learning has become one of the hotspots of today’s [...] Read more.
With information technology pushing the development of intelligent teaching environments, the online teaching platform emerges timely around the globe, and how to accurately evaluate the effect of the “any-time and anywhere” teacher–student interaction and learning has become one of the hotspots of today’s education research. Bullet chatting in online courses is one of the most important ways of interaction between teachers and students. The feedback from the students can help teachers improve their teaching methods, adjust teaching content, and schedule in time so as to improve the quality of their teaching. How to automatically identify the sentiment polarity in the comment text through deep machine learning has also become a key issue to be automatically processed in online course teaching. The traditional single-layer attention mechanism only enhances certain sentimentally intense words, so we proposed a sentiment analysis method based on a hierarchical attention mechanism that we called HAN. Firstly, we use CNN and LSTM to extract local and global information, gate mechanisms are used for extracting sentiment words, and the hierarchical attention mechanism is then used to weigh the different sentiment features, with the original information added to the attention mechanism concentration to prevent the loss of information. Experiments are conducted on China Universities MOOC and Tencent Classroom comment data sets; both accuracy and F1 are improved compared to the baseline, and the validity of the model is verified. Full article
(This article belongs to the Special Issue AI Empowered Sentiment Analysis)
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25 pages, 4102 KiB  
Article
A Manifold-Level Hybrid Deep Learning Approach for Sentiment Classification Using an Autoregressive Model
Appl. Sci. 2023, 13(5), 3091; https://doi.org/10.3390/app13053091 - 27 Feb 2023
Cited by 5 | Viewed by 1322
Abstract
With the recent expansion of social media in the form of social networks, online portals, and microblogs, users have generated a vast number of opinions, reviews, ratings, and feedback. Businesses, governments, and individuals benefit greatly from this information. While this information is intended [...] Read more.
With the recent expansion of social media in the form of social networks, online portals, and microblogs, users have generated a vast number of opinions, reviews, ratings, and feedback. Businesses, governments, and individuals benefit greatly from this information. While this information is intended to be informative, a large portion of it necessitates the use of text mining and sentiment analysis models. It is a matter of concern that reviews on social media lack text context semantics. A model for sentiment classification for customer reviews based on manifold dimensions and manifold modeling is presented to fully exploit the sentiment data provided in reviews and handle the issue of the absence of text context semantics. This paper uses a deep learning framework to model review texts using two dimensions of language texts and ideogrammatic icons and three levels of documents, sentences, and words for a text context semantic analysis review that enhances the precision of the sentiment categorization process. Observations from the experiments show that the proposed model outperforms the current sentiment categorization techniques by more than 8.86%, with an average accuracy rate of 97.30%. Full article
(This article belongs to the Special Issue AI Empowered Sentiment Analysis)
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14 pages, 1050 KiB  
Article
Sentiment Analysis of Text Reviews Using Lexicon-Enhanced Bert Embedding (LeBERT) Model with Convolutional Neural Network
Appl. Sci. 2023, 13(3), 1445; https://doi.org/10.3390/app13031445 - 21 Jan 2023
Cited by 22 | Viewed by 5852
Abstract
Sentiment analysis has become an important area of research in natural language processing. This technique has a wide range of applications, such as comprehending user preferences in ecommerce feedback portals, politics, and in governance. However, accurate sentiment analysis requires robust text representation techniques [...] Read more.
Sentiment analysis has become an important area of research in natural language processing. This technique has a wide range of applications, such as comprehending user preferences in ecommerce feedback portals, politics, and in governance. However, accurate sentiment analysis requires robust text representation techniques that can convert words into precise vectors that represent the input text. There are two categories of text representation techniques: lexicon-based techniques and machine learning-based techniques. From research, both techniques have limitations. For instance, pre-trained word embeddings, such as Word2Vec, Glove, and bidirectional encoder representations from transformers (BERT), generate vectors by considering word distances, similarities, and occurrences ignoring other aspects such as word sentiment orientation. Aiming at such limitations, this paper presents a sentiment classification model (named LeBERT) combining sentiment lexicon, N-grams, BERT, and CNN. In the model, sentiment lexicon, N-grams, and BERT are used to vectorize words selected from a section of the input text. CNN is used as the deep neural network classifier for feature mapping and giving the output sentiment class. The proposed model is evaluated on three public datasets, namely, Amazon products’ reviews, Imbd movies’ reviews, and Yelp restaurants’ reviews datasets. Accuracy, precision, and F-measure are used as the model performance metrics. The experimental results indicate that the proposed LeBERT model outperforms the existing state-of-the-art models, with a F-measure score of 88.73% in binary sentiment classification. Full article
(This article belongs to the Special Issue AI Empowered Sentiment Analysis)
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16 pages, 775 KiB  
Article
Fine-Grained Sentiment-Controlled Text Generation Approach Based on Pre-Trained Language Model
Appl. Sci. 2023, 13(1), 264; https://doi.org/10.3390/app13010264 - 26 Dec 2022
Cited by 1 | Viewed by 1914
Abstract
Sentiment-controlled text generation aims to generate texts according to the given sentiment. However, most of the existing studies focus only on the document- or sentence-level sentiment control, leaving a gap for finer-grained control over the content of generated results. Fine-grained control allows a [...] Read more.
Sentiment-controlled text generation aims to generate texts according to the given sentiment. However, most of the existing studies focus only on the document- or sentence-level sentiment control, leaving a gap for finer-grained control over the content of generated results. Fine-grained control allows a generated review to express different opinions toward multiple aspects. Some previous works attempted to generate reviews conditioned on aspect-level sentiments, but they usually suffer from low adaptability and the lack of an annotated dataset. To alleviate these problems, we propose a novel pre-trained extended generative model that can dynamically refer to the prompt sentiment, together with an auxiliary classifier that extracts the fine-grained sentiments from the unannotated sentences, thus we conducted training on both annotated and unannotated datasets. We also propose a query-hint mechanism to further guide the generation process toward the aspect-level sentiments at every time step. Experimental results from real-world datasets demonstrated that our model has excellent adaptability in generating aspect-level sentiment-controllable review texts with high sentiment coverage and stable quality since, on both datasets, our model steadily outperforms other baseline models in the metrics of BLEU-4, METETOR, and ROUGE-L etc. The limitation of this work is that we only focus on fine-grained sentiments that are explicitly expressed. Moreover, the implicitly expressed fine-grained sentiment-controllable text generation will be an important puzzle for future work. Full article
(This article belongs to the Special Issue AI Empowered Sentiment Analysis)
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