Challenges Of Sentiment Analysis Research Articles

A novel socio-pragmatic framework for sentiment analysis in Dravidian–English code-switched texts

The advent of global digital communication has accentuated the complexity of multilingual code-switching, presenting formidable challenges for sentiment analysis by necessitating nuanced interpretation of linguistic and socio-pragmatic cues. This research introduces MultiSwitchNet, a pioneering sentiment analysis model tailored for the intricate dynamics of Dravidian languages mixed with English code-switched texts. MultiSwitchNet leverages the linguistic prowess of XLM-RoBERTa, combined with socio-pragmatic insights through a novel Socio-Pragmatic Embedding Layer (SPEL) utilizing DistilBERT. Evaluated on the Dravidian Code-Mixed Dataset, MultiSwitchNet achieved superior performance metrics, including an accuracy of 72.3%, precision of 72.9%, recall of 72.1%, and an F1-score of 72.5%. Its effectiveness extends across varying degrees of code-switching complexity and demonstrates remarkable consistency in sentiment prediction across semantically similar yet syntactically diverse sentences. Statistical analyses, including ANOVA and post hoc tests, underscore the model’s significant outperformance compared to both baseline and state-of-the-art models, highlighting the indispensability of integrating socio-pragmatic awareness for accurate sentiment analysis. MultiSwitchNet’s innovative approach sets a new benchmark for sentiment analysis in multilingual contexts and opens avenues for future research into models capable of discerning the rich socio-pragmatic fabric of global digital communication.

Generative artificial intelligence in topic-sentiment classification for Arabic text: a comparative study with possible future directions

Social media platforms have become essential for disseminating news and expressing individual sentiments on various life topics. Arabic, widely used in the Middle East, presents unique challenges for sentiment analysis due to its complexity and multiple dialects. Motivated by the need to address these challenges, this article develops methods to overcome the lack of topic-based labeling techniques, compares different approaches for preparing extensive, annotated datasets, and analyzes the efficacy of machine learning (ML), deep learning (DL), and large language models (LLMs) in classifying Arabic textual data. Our research utilizes the topic-modeling technique to build a topic-based sentiment dataset of Arabic texts aimed at enhancing our understanding and processing capabilities. We present a comprehensive evaluation of dataset balancing techniques, including under-sampling, over-sampling, and using imbalanced datasets, providing insights into how these approaches impact classification outcomes. Additionally, we explore the influence of dataset sizes on the performance of various ML models, highlighting the importance of dataset scale in developing effective Arabic NLP applications. A further focus of our study is the comparative analysis of generative artificial intelligence (AI) models, including the emerging ChatGPT LLM, assessing their effectiveness in managing the complexities of Arabic language classification tasks. Our results show that support vector machines (SVM) achieved the highest performance, with F1-scores of 0.97 and 0.96 in classifying sentiment and topic, respectively, in Arabic tweets. This research not only benchmarks existing methodologies but also paves the way for more nuanced and robust models in the future, enhancing the application of generative AI in Arabic topic-based sentiment analysis.

Learning Modality Consistency and Difference Information with Multitask Learning for Multimodal Sentiment Analysis

The primary challenge in Multimodal sentiment analysis (MSA) lies in developing robust joint representations that can effectively learn mutual information from diverse modalities. Previous research in this field tends to rely on feature concatenation to obtain joint representations. However, these approaches fail to fully exploit interactive patterns to ensure consistency and differentiation across different modalities. To address this limitation, we propose a novel framework for multimodal sentiment analysis, named CDML (Consistency and Difference using a Multitask Learning network). Specifically, CDML uses an attention mechanism to assign the attention weights of each modality efficiently. Adversarial training is used to obtain consistent information between modalities. Finally, the difference among the modalities is acquired by the multitask learning framework. Experiments on two benchmark MSA datasets, CMU-MOSI and CMU-MOSEI, showcase that our proposed method outperforms the seven existing approaches by at least 1.3% for Acc-2 and 1.7% for F1.

Similar modality completion-based multimodal sentiment analysis under uncertain missing modalities

Recently, uncertain missing modalities in multimodal sentiment analysis (MSA) brings a new challenge for sentiment analysis. However, existing research cannot accurately complete the missing modalities, and fail to explore the advantages of the text modality in MSA. For the above problems, this work develops a Similar Modality Completion based-MSA model under uncertain missing modalities (termed as SMCMSA). Firstly, we construct the full modalities samples database (FMSD) by screening out the full modality samples from the whole multimodal dataset, and then predicting and marking the sentiment labels of each modality of the samples with three pre-trained unimodal sentiment analysis model (PTUSA). Next, for completing the uncertain missing modalities, we propose a set of missing modalities completion strategies based on the similar modalities selected from FMSD. For the completed multimodal data, we first encode the text, video and audio modality using the encoder of transformer, then we fuse the representation of text into the representations of video and audio under the guidance of a pre-trained model, thereby improving the quality of video and audio. Finally, we conduct sentiment classification based on the representations of text, video and audio with the softmax function respectively, and get the final decision with the decision-level fusion method. Based on benchmark datasets CMU-MOSI and IEMOCAP, extensive experiments have been conducted to verify that our proposed model SMCMSA has better performance than that of the state-of-the-art baseline models.The codes of our model are available at https://github.com/Astro2Sun/SMCMSA.

UrduAspectNet: Fusing Transformers and Dual GCN for Urdu Aspect-Based Sentiment Detection

Urdu, characterized by its intricate morphological structure and linguistic nuances, presents distinct challenges in computational sentiment analysis. Addressing these, we introduce ”UrduAspectNet” – a dedicated model tailored for Aspect-Based Sentiment Analysis (ABSA) in Urdu. Central to our approach is a rigorous preprocessing phase. Leveraging the Stanza library, we extract Part-of-Speech (POS) tags and lemmas, ensuring Urdu’s linguistic intricacies are aptly represented. To probe the effectiveness of different embeddings, we trained our model using both mBERT and XLM-R embeddings, comparing their performances to identify the most effective representation for Urdu ABSA. Recognizing the nuanced inter-relationships between words, especially in Urdu’s flexible syntactic constructs, our model incorporates a dual Graph Convolutional Network (GCN) layer.Addressing the challenge of the absence of a dedicated Urdu ABSA dataset, we curated our own, collecting over 4,603 news headlines from various domains, such as politics, entertainment, business, and sports. These headlines, sourced from diverse news platforms, not only identify prevalent aspects but also pinpoints their sentiment polarities, categorized as positive, negative, or neutral. Despite the inherent complexities of Urdu, such as its colloquial expressions and idioms, ”UrduAspectNet” showcases remarkable efficacy. Initial comparisons between mBERT and XLM-R embeddings integrated with dual GCN provide valuable insights into their respective strengths in the context of Urdu ABSA. With broad applications spanning media analytics, business insights, and socio-cultural analysis, ”UrduAspectNet” is positioned as a pivotal benchmark in Urdu ABSA research.

Cross-Domain Aspect Based Sentiment Analysis

Abstract: Sentiment analysis, a crucial aspect of natural language processing, faces significant challenges in diverse domains. This research focuses on cross-domain aspect-based sentiment analysis, aiming to enhance accuracy across various fields. We propose a novel framework tailored for this purpose, addressing existing limitations. Through comprehensive evaluations on diverse datasets, our approach demonstrates improved efficacy compared to established models. The discussion highlights the strengths and limitations of our framework, contributing valuable insights to the field of sentiment analysis. Our research emphasizes the need for domain-specific considerations in sentiment analysis, offering practical implications for real-world applications. In conclusion, this study advances our understanding of cross-domain sentiment analysis challenges and provides a practical solution for improved accuracy in diverse domains

Manifesto of Deep Learning Architecture for Aspect Level Sentiment Analysis to extract customer criticism

Sentiment analysis, a critical task in natural language processing, aims to automatically identify and classify the sentiment expressed in textual data. Aspect-level sentiment analysis focuses on determining sentiment at a more granular level, targeting specific aspects or features within a piece of text. In this paper, we explore various techniques for sentiment analysis, including traditional machine learning approaches and state-of-the-art deep learning models. Additionally, deep learning techniques has been utilized to identifying and extracting specific aspects from text, addressing aspect-level ambiguity, and capturing nuanced sentiments for each aspect. These datasets are valuable for conducting aspect-level sentiment analysis. In this article, we explore a language model based on pre-trained deep neural networks. This model can analyze sequences of text to classify sentiments as positive, negative, or neutral without explicit human labeling. To evaluate these models, data from Twitter's US airlines sentiment database was utilized. Experiments on this dataset reveal that the BERT, RoBERTA and DistilBERT model outperforms than the ML based model in accuracy and is more efficient in terms of training time. Notably, our findings showcase significant advancements over previous state-of-the-art methods that rely on supervised feature learning, bridging existing gaps in sentiment analysis methodologies. Our findings shed light on the advancements and challenges in sentiment analysis, offering insights for future research directions and practical applications in areas such as customer feedback analysis, social media monitoring, and opinion mining.

Arabic Sentiment Analysis Evaluation of Saudi Arabia's Telecommunication Using Several Deep Learning Algorithms

Sentiment analysis (SA) is a technique that applies natural language processing (NLP) in order to analyze and classify the emotion in sentiment reviews. SA is responsible for analyzing people's feelings, opinions, and experiences that are shared through the Internet and social networks. In this paper, we focus on investigating, evaluating, and improving Arabic sentiment analysis (ASA) models, datasets, and challenges. ASA has several difficulties, like language’s morphological features, many dialects, no clear and uniform corpora, low accuracy, and restricted ASA material. In order to do that, we do a full analysis and evaluation of Arabic sentiment analysis models and datasets that target e-marketing services such as telecommunication, health, and books. We evaluate our data set, called Sara, with several Arabic sentiment datasets in terms of brief description, dataset size, source of collecting data, field type, and abbreviation. We enhanced our previous models by using ensemble learning average techniques. The accuracy of our enhanced model has increased and now reaches around 97%. Also, we evaluate our developed ASA using deep learning (DL) algorithms with other ASA models in the field of e-marketing. Our models have significant improvements in terms of performance compared with other works, where our three models, CNN-Model, LSTM-Mode2, and CNN+LSTM-Model3, have accuracy of 96.83%, 94.74%, and 96.91%, respectively.

A hybrid neural network model based on transfer learning for Arabic sentiment analysis of customer satisfaction

AbstractSentiment analysis, a method used to classify textual content into positive, negative, or neutral sentiments, is commonly applied to data from social media platforms. Arabic, an official language of the United Nations, presents unique challenges for sentiment analysis due to its complex morphology and dialectal diversity. Compared to English, research on Arabic sentiment analysis is relatively scarce. Transfer learning, which applies the knowledge learned from one domain to another, can address the limitations of training time and computational resources. However, the development of transfer learning for Arabic sentiment analysis is still underdeveloped. In this study, we develop a new hybrid model, RNN‐BiLSTM, which merges recurrent neural networks (RNN) and bidirectional long short‐term memory (BiLSTM) networks. We used Arabic bidirectional encoder representations from transformers (AraBERT), a state‐of‐the‐art Arabic language pre‐trained transformer‐based model, to generate word‐embedding vectors. The RNN‐BiLSTM model integrates the strengths of RNN and BiLSTM, including the ability to learn sequential dependencies and bidirectional context. We trained the RNN‐BiLSTM model on the source domain, specifically the Arabic reviews dataset (ARD). The RNN‐BiLSTM model outperforms the RNN and BiLSTM models with default parameters, achieving an accuracy of 95.75%. We further applied transfer learning to the RNN‐BiLSTM model by fine‐tuning its parameters using random search. We compared the performance of the fine‐tuned RNN‐BiLSTM model with the RNN and BiLSTM models on two target domain datasets: ASTD and Aracust. The results showed that the fine‐tuned RNN‐BiLSTM model is more effective for transfer learning, achieving an accuracy of 95.44% and 96.19% on the ASTD and Aracust datasets, respectively.

Analysing the Sentiments using Natural Language Processing

Abstract: Sentiment analysis is an approach to identify, extract, quantify, and study affective states and subjective data that makes use of text analysis, computational linguistics, biometrics, and natural language processing. Customer materials, online and social media content, and medical materials all commonly use. Deep language models have made it possible to analyze more difficult data domains, such as news texts. The fundamental task is to categorize a text based on its polarity, that is, whether the expressed opinion is positive, negative, or neutral. In advanced sentiment classification, emotions like surprise, fear, anger, disgust, sadness, and pleasure are all looked at. Psychological studies and the General Inquirer are two sources of sentiment analysis's antecedents. Challenges in sentiment analysis include the possibility of opinion words being positive in one state being negative in another, and the fact that people may express opinions differently. Developing algorithms to identify and classify opinion or sentiment in electronic text is a goal.

Sentiment analysis of tweets using text and graph multi-views learning

With the surge of deep learning framework, various studies have attempted to address the challenges of sentiment analysis of tweets (data sparsity, under-specificity, noise, and multilingual content) through text and network-based representation learning approaches. However, limited studies on combining the benefits of textual and structural (graph) representations for sentiment analysis of tweets have been carried out. This study proposes a multi-view learning framework (end-to-end and ensemble-based) that leverages both text-based and graph-based representation learning approaches to enrich the tweet representation for sentiment classification. The efficacy of the proposed framework is evaluated over three datasets using suitable baseline counterparts. From various experimental studies, it is observed that combining both textual and structural views can achieve better performance of sentiment classification tasks than its counterparts.

Unsupervised Domain Adaptation via Weighted Sequential Discriminative Feature Learning for Sentiment Analysis

Unsupervised domain adaptation (UDA) presents a significant challenge in sentiment analysis, especially when faced with differences between source and target domains. This study introduces Weighted Sequential Unsupervised Domain Adaptation (WS-UDA), a novel sequential framework aimed at discovering more profound features and improving target representations, even in resource-limited scenarios. WS-UDA utilizes a domain-adversarial learning model for sequential discriminative feature learning. While recent UDA techniques excel in scenarios where source and target domains are closely related, they struggle with substantial dissimilarities. This potentially leads to instability during shared-feature learning. To tackle this issue, WS-UDA employs a two-stage transfer process concurrently, significantly enhancing model stability and adaptability. The sequential approach of WS-UDA facilitates superior adaptability to varying levels of dissimilarity between source and target domains. Experimental results on benchmark datasets, including Amazon reviews, FDU-MTL datasets, and Spam datasets, demonstrate the promising performance of WS-UDA. It outperforms state-of-the-art cross-domain unsupervised baselines, showcasing its efficacy in scenarios with dissimilar domains. WS-UDA’s adaptability extends beyond sentiment analysis, making it a versatile solution for diverse text classification tasks.

Noise Imitation Based Adversarial Training for Robust Multimodal Sentiment Analysis

As an inevitable phenomenon in real-world applications, data imperfection has emerged as one of the most critical challenges for multimodal sentiment analysis. However, existing approaches tend to overly focus on a specific type of imperfection, leading to performance degradation in real-world scenarios where multiple types of noise exist simultaneously. In this work, we formulate the imperfection with the modality feature missing at the training period and propose the noise intimation based adversarial training framework to improve the robustness against various potential imperfections at the inference period. Specifically, the proposed method first uses temporal feature erasing as the augmentation for noisy instances construction and exploits the modality interactions through the self-attention mechanism to learn multimodal representation for original-noisy instance pairs. Then, based on paired intermediate representation, a novel adversarial training strategy with semantic reconstruction supervision is proposed to learn unified joint representation between noisy and perfect data. For experiments, the proposed method is first verified with the modality feature missing, the same type of imperfection as the training period, and shows impressive performance. Moreover, we show that our approach is capable of achieving outstanding results for other types of imperfection, including modality missing, automation speech recognition error and attacks on text, highlighting the generalizability of our model. Finally, we conduct case studies on general additive distribution, which introduce background noise and blur into raw video clips, further revealing the capability of our proposed method for real-world applications.

Application of Deep Learning-Based Natural Language Processing in Multilingual Sentiment Analysis

This study explores the application of deep learning-based natural language processing technologies in multilingual sentiment analysis. By examining the performance of deep learning models such as BERT and LSTM in multilingual settings, the research demonstrates the effectiveness of these models in cross-linguistic sentiment classification tasks. Despite progress, major challenges in multilingual sentiment analysis include language and cultural differences, insufficient complex context processing, and data imbalance. Future research directions include optimizing the models' contextual understanding abilities, leveraging multilingual data resources, exploring novel neural network architectures, and improving assessment metrics. With these measures, the accuracy and efficiency of multilingual sentiment analysis are expected to be significantly enhanced, further advancing the global application of natural language processing technologies.

A multi-message passing framework based on heterogeneous graphs in conversational emotion recognition

As an important development direction of natural language processing, emotion recognition in conversation (ERC) remains a challenge in sentiment analysis. Given the large-scale dialogue datasets and their wide application in the fields of recommendation systems and human–machine dialogue systems, researchers have begun to pay more attention to the issue of ERC. In recent research, the task of ERC has been largely based on the graph structure to model the speaker level. However, most existing studies simply splice multimodal features, and the heterogeneity of multimodal features tends to be overlooked. Hence, this paper proposes a multivariate messaging framework to embed heterogeneous information into multimodal relational graphs. In the process of aggregating graph node information, we take into account the homogeneity of nodes and assign different weights to different nodes so as to better aggregate semantic information. In order to improve the robustness of the model, we utilize the mechanism of sharing weights among neighbors to reduce the number of network parameters and improve the generalization ability of the model. In so doing, the node information is aggregated through the constructed graph network, and the final semantic vector representation is obtained. Experiments over two benchmark datasets for ERC show that our proposed model achieves improved performance in accuracy and F1 value.

A GCN- and Deep Biaffine Attention-Based Classification Model for Course Review Sentiment

In recent years, the increasing use of online surveys for course evaluation in schools has led to an outpouring of evaluation texts. These texts, with their emotional polarity, can give schools the most direct feedback. Emotional analysis on course evaluation, therefore, has great implications. However, the not-so-rigid text grammar and rich text content pose a challenge for sentiment analysis in Chinese course evaluation. To solve this problem, this paper proposes a sentiment classification model BiLSTM-GCN-Att (BGAN). Here, BiLSTM is used to extract the features of the text and output the hidden state vector. Then, the deep biaffine attention mechanism is used to analyze the dependence of the text and generate a dependency matrix. Next, input the hidden state vector to the GCN. Finally, the softmax function is used as the output layer of the model to perform sentiment classification. The model proves effective and experimental results, showing that the BGAN achieved a maximum improvement of 11.02% and 14.47% in precision and F1-score respectively compared with the classical models.

Social Network Sentiment Analysis Using Hybrid Deep Learning Models

The exponential growth in information on the Internet, particularly within social networks, highlights the importance of sentiment and opinion analysis. The intrinsic characteristics of the Spanish language coupled with the short length and lack of context of messages on social media pose a challenge for sentiment analysis in social networks. In this study, we present a hybrid deep learning model combining convolutional and long short-term memory layers to detect polarity levels in Twitter for the Spanish language. Our model significantly improved the accuracy of existing approaches by up to 20%, achieving accuracies of around 76% for three polarities (positive, negative, neutral) and 91% for two polarities (positive, negative).

Multimodal Sentiment Analysis Based on Attentional Temporal Convolutional Network and Multi-Layer Feature Fusion

Multimodal sentiment analysis aims to extract and integrate information from different modalities to accurately identify the sentiment expressed in multimodal data. How to effectively capture the relevant information within a specific modality and how to fully exploit the complementary information among multiple modalities are two major challenges in multimodal sentiment analysis. Traditional approaches fail to obtain the global contextual information of long time-series data when extracting unimodal temporal features, and they usually fuse the features from multiple modalities with the same method and ignore the correlation between different modalities when modeling inter-modal interactions. In this paper, we first propose an Attentional Temporal Convolutional Network (ATCN) to extract unimodal temporal features for enhancing the feature representation ability, then introduce a Multi-layer Feature Fusion (MFF) model to improve the effectiveness of multimodal fusion, which fuses the different-level features by different methods according to the correlation coefficient between the features, and cross-modal multi-head attention is used to fully explore the potential relationship between the low-level features. The experimental results on SIMS and CMU-MOSI datasets show that the proposed model achieves superior performance on sentiment analysis tasks compared to state-of-the-art baselines.

Sentiment Analysis in Online Product Reviews: Mining Customer Opinions for Sentiment Classification

Online product reviews have become a valuable resource for consumers seeking detailed information and making informed choices. The process of automatically extracting sentiment or opinions from these reviews heavily relies on sentiment analysis, a branch of Natural Language Processing (NLP). This research article focuses on sentiment categorization in online product evaluations, utilizing innovative techniques for mining consumer opinions. The project aims to establish a robust framework for sentiment analysis that accurately classifies emotions expressed in these reviews. The proposed system incorporates advanced deep learning and machine learning methods to enhance data classification and extract fine-grained sentiment information. The study addresses the unique challenges of sentiment analysis in the context of online product evaluations, including polarity changes, sarcasm, and domain-specific sentiment expressions, which often pose significant obstacles to precise sentiment classification. The approach combines feature engineering and deep learning techniques, extracting lexical, syntactic, and semantic features such as part-of-speech tags, n-grams, sentiment lexicons, and word embeddings from the review texts. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are employed as sophisticated neural network architectures to leverage these features, creating robust representations and capturing contextual information. The suggested architecture is extensively evaluated on a large dataset of online product reviews, demonstrating superior performance in sentiment categorization compared to existing approaches. The evaluation encompasses various sentiment classes, measuring metrics like accuracy, recall, and F1-score, and assessing the framework's adaptability to different product domains. The study showcases the effectiveness of advanced machine learning and deep learning algorithms in sentiment categorization, advancing the field of sentiment analysis for online product evaluations. Businesses can gain valuable insights into customer sentiment and make well-informed decisions regarding product enhancements and marketing strategies by leveraging the proposed framework

Using deep learning to analyze the psychological effects of COVID-19.

Sentiment Analysis (SA) automates the classification of the sentiment of people's attitudes, feelings or reviews employing natural language processing (NLP) and computational approaches. Deep learning has recently demonstrated remarkable success in the field of SA in many languages including Arabic. Arabic sentiment analysis, however, still has to be improved, due to the complexity of the Arabic language's structure, the variety of dialects, and the lack of lexicons. Moreover, in Arabic, anxiety as a psychological sentiment has not been the target of much research. This paper aims to provide solutions to one of the challenges of Arabic Sentiment Analysis (ASA) using a deep learning model focused on predicting the anxiety level during COVID-19 in Saudi Arabia. A psychological scale to determine the level of anxiety was built and validated. It was then used to create the Arabic Psychological Lexicon (AraPh) containing 138 different dialectical Arabic words that express anxiety, which was used to annotate our corpus (Aranxiety). Aranxiety comprises 955 Arabic tweets representing the level of user anxiety during COVID-19. Bi-GRU model with word embedding was then applied to analyze the sentiment of the tweets and to determine the anxiety level. For SA, the applied model achieved 88% on accuracy, 89% on precision, 88% on recall, and 87% for F1. A majority of 77% of tweets presented no anxiety, whereas 17% represented mild anxiety and a mere 6% represented high anxiety. The proposed model can be used by the Saudi Ministry of Health and members of the research community to formulate solutions to increase psychological resiliency among the Saudi population.