Challenges Of Sentiment Analysis Research Articles

A Multi-Level Embedding Framework for Decoding Sarcasm Using Context, Emotion, and Sentiment Feature

Sarcasm detection in text poses significant challenges for traditional sentiment analysis, as it often requires an understanding of context, word meanings, and emotional undertones. For example, in the sentence “I totally love working on Christmas holiday”, detecting sarcasm depends on capturing the contrast between affective words and their context. Existing methods often focus on single-embedding levels, such as word-level or affective-level, neglecting the importance of multi-level context. In this paper, we propose SAWE (Sentence, Affect, and Word Embeddings), a framework that combines sentence-level, affect-level, and context-dependent word embeddings to improve sarcasm detection. We use pre-trained transformer models SBERT and RoBERTa, enhanced with a bidirectional GRU and self-attention, alongside SenticNet to extract affective words. The combined embeddings are processed through a CNN and classified using a multilayer perceptron (MLP). SAWE is evaluated on two benchmark datasets, Sarcasm Corpus V2 (SV2) and Self-Annotated Reddit Corpus 2.0 (SARC 2.0), outperforming previous methods, particularly on long texts, with a 4.2% improvement on F1-Score for SV2. Our results emphasize the importance of multi-level embeddings and contextual information in detecting sarcasm, demonstrating a new direction for future research.

Automatic Negation Detection for Semantic Analysis in Arabic Hotel Reviews Through Lexical and Structural Features: A Supervised Classification

One significant challenge in sentiment analysis is the presence of negation, which reverses the meanings of sentences, transformingpositive statements into negative ones and impacting the sentiment conveyed in the text. This issue is particularly pronounced in Arabic, a language known for its complex morphology. Detecting negation is crucial for enhancing sentiment analysis performance and various natural language processing applications. This paper presents an approach for automatically detecting negation in user-generated Arabic hotel reviews through lexical and structural features. It comprises several stages: data collection, text pre-processing, feature extraction, supervised learning classification, and evaluation. The study employed multiple supervised classification techniques, including naïve Bayes, random forest, logistic regression, support vector machines, and deep learning, to analyse lexical and structural features extracted from the dataset. The results of the experiments yielded promising outcomes, demonstrating the feasibility of the approach for practical applications. The classifiers exhibited highly comparable performance in identifying negation, with only marginal deviations in their performance metrics. Notably, the deep learning classifier consistently emerged as the top performer, achieving an exceptionally high overall accuracy rate of 99.24 percent, surpassing established benchmarks in Arabic text processing and underscoring its potential for practical applications. These findings hold significant implications for advancing Arabic text processing, particularly in sentiment analysis and related NLP tasks. The high accuracy of 99.24 percent achieved by the deep learning classifier highlights its robustness in accurately detecting negation, a critical challenge in sentiment analysis. This classifier performance demonstrates the potential to be integrated into real-world applications, such as automated review systems and opinion mining tools, where accurate sentiment interpretation is essential.

Reconstructing representations using diffusion models for multimodal sentiment analysis through reading comprehension

The primary challenge in multimodal sentiment analysis (MSA), which utilizes textual, audio, and visual information to analyze speakers' emotions, lies in constructing representation vectors that incorporate both unimodal semantic and multimodal interaction information. While existing research has extensively focused on multimodal fusion strategies, there remains insufficient exploration of the intrinsic potential within concurrently enhancing unimodal and multimodal representations. To address this gap, we introduce two additional steps to traditional three-step MSA: text modality enhancement through the machine reading comprehension (MRC) framework and multimodal representation reconstruction via proposing the diverse diffusion denoising autoencoder (D3AE) module. The MRC queries are integrated to locate sentiment-related prior knowledge, thereby deepening textual semantic understanding from a pretrained language model. Meanwhile, D3AE employs a single-step denoising strategy along with diffusion models across multiple time intervals, enabling efficient reconstruction and enhancement of multimodal representations. Extensive experiments conducted on two benchmark datasets, CMU-MOSI and CMU-MOSEI, validate that our model, MRC-D3AE, achieves state-of-the-art performance. The superiority of our model over existing baselines is primarily attributed to integrating MRC for enhancing text modality and D3AE for reconstructing multimodal representations.

Enhanced UrduAspectNet: Leveraging Biaffine Attention for superior Aspect-Based Sentiment Analysis

Urdu, with its rich linguistic complexity, poses significant challenges for computational sentiment analysis. This study presents an enhanced version of UrduAspectNet, specifically designed for Aspect-Based Sentiment Analysis (ABSA) in Urdu. We introduce key innovations including the incorporation of Biaffine Attention into the model architecture, which synergizes XLM-R embeddings, a bidirectional LSTM (BiLSTM), and dual Graph Convolutional Networks (GCNs). Additionally, we utilize dependency parsing to create the adjacency matrix for the GCNs, capturing syntactic dependencies to enhance relational representation. The improved model, termed Enhanced UrduAspectNet, integrates POS and lemma embeddings, processed through BiLSTM and GCN layers, with Biaffine Attention enhancing the extraction of intricate aspect and sentiment relationships. We also introduce the use of BIO tags for aspect term identification, improving the granularity of aspect extraction. Experimental results demonstrate significant improvements in both aspect extraction and sentiment classification accuracy. This research advances Urdu sentiment analysis and sets a precedent for leveraging sophisticated NLP techniques in underrepresented languages.

A deep spatiotemporal interaction network for multimodal sentimental analysis and emotion recognition

One of the challenges of sentiment analysis and emotion recognition is how to effectively fuse the multimodal inputs. The transformer-based models have achieved great success in applications of multimodal sentiment analysis and emotion recognition recently. However, the transformer-based model often neglects the coherence of human emotion due to its parallel structure. Additionally, a low-rank bottleneck created by multi- attention-head causes an inadequate fitting ability of models. To tackle these issues, a Deep Spatiotemporal Interaction Network (DSIN) is proposed in this study. It consists of two main components, i.e., a cross-modal transformer with a cross-talking attention module and a hierarchically temporal fusion module, where the cross-modal transformer is used to model the spatial interactions between different modalities and the hierarchically temporal fusion network is utilized to model the temporal coherence of emotion. Therefore, the DSIN can model the spatiotemporal interactions of multimodal inputs by incorporating the time-dependency into the parallel structure of transformer and decrease the redundancy of embedded features by implanting their spatiotemporal interactions into a hybrid memory network in a hierarchical manner. The experimental results on two benchmark datasets indicate that DSIN achieves superior performance compared with the state-of-the-art models, and some useful insights are derived from the results.

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.

Unveiling Sarcasm in Hindi: Cutting-Edge Deep Learning Framework for Sarcasm Detection

Opinion mining, also known as sentiment analysis (SA), is a pivotal computer-based approach for discerning and categorizing sentiments within textual data. With the burgeoning popularity of social media platforms and the proliferation of user-generated content in Hindi, there is an escalating need for proficient sentiment analysis techniques tailored specifically for this language. The internet's accessibility in regional languages has become imperative to accommodate a diverse user base irrespective of age or linguistic inclination. While a significant portion of SA research has been conducted in English, there exists a dearth of comprehensive studies focusing on Indian languages, notably Hindi. This paper addresses this gap by investigating sentiment analysis within the context of Hindi, delving into various sentiment categories including positive, negative, neutral, and the notably complex sentiment of sarcasm. Sarcasm, characterized by its subtle irony and ridicule, poses a formidable challenge in sentiment analysis, particularly in languages like Hindi, which boast intricate grammatical structures and nuanced contextual cues. This study aims to enhance sentiment analysis methodologies by unraveling the intricacies of sarcasm detection in Hindi textual data.

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.