Field Of Sentiment Analysis Research Articles

Sentiment Analysis by Double Classification of Takeaway Platform Reviews Based on Deep Learning LSTM Models

Sentiment analysis has a wide range of applications in the fields of opinion analysis, sentiment dialog, and product reviews. However, the sentiment information expressed in texts under different topics varies greatly; for example, a model that performs well on a movie review set has poor model classification on a social platform review set due to inconsistent recognition of antiphonal phrases, different expression of emoji sentiment, and missing contextual information. In this paper, the authors focus on tens of thousands of latest reviews of Chinese takeout platforms Meituan and Elema, and use the LSTM model in deep learning to double classify the data (positive and negative). This paper analyzes the performance of LSTM models in the field of sentiment analysis of takeout reviews and concludes that domain-specific text sentiment analysis requires specific analysis.

Text emotional analysis in Natural Language Processing

Natural Language Processing (NLP) is an important research direction in the field of artificial intelligence, aimed at enabling computers to understand and process human natural language. Emotional analysis refers to the use of computer technology to automatically recognize and classify the emotional tendencies expressed in text, such as positive, negative, or neutral. This technology can be applied to fields such as social media analysis, public opinion monitoring, market research, etc. The goal is to enable computers to understand and interpret emotional information in text like humans. Text sentiment analysis, as a key task of NLP, aims to identify and classify emotional tendencies expressed in text. This article will delve into the field of text sentiment analysis in natural language processing, focusing on the role and application of standard sentiment dictionaries and corpora in sentiment analysis. Reviewing relevant research conclusions, providing reference and inspiration for achieving more accurate and comprehensive text sentiment analysis through research and exploration.

Deep learning-based sentiment analysis for social media: A focus on multimodal and aspect-based approaches

Commonly referred to as opinion mining, sentiment analysis harnesses the power of deep learning systems to discern human emotions and subjective sentiments towards a wide array of subjects. As such, it has become an integral tool in identifying and distinguishing sentences that harbor emotional biases or trends. By systematically examining sentiment-tinged data, researchers can unearth pivotal insights that not only reflect current perspectives but also predict future behaviors and trends. This process involves intricate computational models that analyze and interpret the emotional undertones embedded within a body of text. Whether these undertones are positive, negative, or neutral, sentiment analysis allows us to delve into the subtle nuances of human communication. This ability to "understand" and quantify sentiment is particularly vital in our modern digital age, where opinions and reviews shared through social media and online platforms can greatly influence public sentiment and consumer behavior. By extending beyond the literal meanings of words and phrases, sentiment analysis can provide a more comprehensive understanding of how people truly feel. It is instrumental in fields as diverse as marketing, politics, social science, and even artificial intelligence development, given its potential to gauge public opinion and predict societal trends. This paper aims to consolidate relevant research within the field of sentiment analysis conducted in recent years. Furthermore, it seeks to prognosticate the future trajectories and impacts of this rapidly evolving domain. Emphasis is placed on the role of deep learning and its transformational effects on the approach and capabilities of sentiment analysis, anticipating how its further advancement will continue to refine this intricate process of emotion recognition and interpretation.

Multimodal Sentiment Analysis Based on Composite Hierarchical Fusion

Abstract In the field of multimodal sentiment analysis, it is an important research task to fully extract modal features and perform efficient fusion. In response to the problems of insufficient semantic information and poor cross-modal fusion effect of traditional sentiment classification models, this paper proposes a composite hierarchical feature fusion method combined with prior knowledge. Firstly, the ALBERT (A Lite BERT) model and the improved ResNet model are constructed for feature extraction of text and image, respectively, and high-dimensional feature vectors are obtained. Secondly, to solve the problem of insufficient semantic information expression in cross-scene, a prior knowledge enhancement model is proposed to enrich the data characteristics of each modality. Finally, to solve the problem of poor cross-modal fusion effect, a composite hierarchical fusion model is proposed, which combines the temporal convolutional network and the attention mechanism to fuse the sequence features of each modality information and realizes the information interaction between different modalities. Experiments on MVSA-Single and MVSA-Multi datasets show that the proposed model is superior to a series of comparison models and has good adaptability in new scenarios.

Statistical Analysis and Accuracy Assessment of Improved Machine Learning Based Opinion Mining Framework

Sentiment analysis, also known as opinion mining, plays a crucial role in understanding and extracting valuable insights from textual data in various domains, including social media, customer feedback, and product reviews. This research presents an in-depth examination of an improved machine learning-based sentiment analysis framework, focusing on its statistical analysis and accuracy assessment. The research begins by introducing the framework's architecture, which incorporates advanced machine learning algorithms and natural language processing techniques. These enhancements aim to provide a more nuanced and context-aware sentiment analysis, addressing the limitations of traditional approaches. To evaluate the performance of the proposed framework, a comprehensive statistical analysis is conducted. Various statistical metrics, such as precision, recall, F1-score, and accuracy, are employed to assess its effectiveness in classifying text sentiments accurately. Additionally, the study explores the impact of different feature engineering and pre-processing techniques on model performance. The results of this study demonstrate the significant improvements achieved by the enhanced sentiment analysis framework in terms of accuracy and reliability. The statistical analysis confirms its superior performance in capturing subtle sentiment nuances, making it a valuable tool for applications requiring precise sentiment understanding. In conclusion, this research contributes to the field of sentiment analysis by presenting an improved machine learning-based framework and conducting a rigorous statistical assessment of its accuracy. The findings provide valuable insights for researchers and practitioners seeking to enhance sentiment analysis techniques and apply them effectively in various domains..

Multimodal Sentiment Analysis Method Based on Hierarchical Adaptive Feature Fusion Network

The traditional multi-modal sentiment analysis (MSA) method usually considers the multi-modal characteristics to be equally important and ignores the contribution of different modes to the final MSA result. Therefore, an MSA method based on hierarchical adaptive feature fusion network is proposed. Firstly, RoBERTa, ResViT, and LibROSA are used to extract different modal features and construct a layered adaptive multi-modal fusion network. Then, the multi-modal feature extraction module and cross-modal feature interaction module are combined to realize the interactive fusion of information between modes. Finally, an adaptive gating mechanism is introduced to design a global multi-modal feature interaction module to learn the unique features of different modes. The experimental results on three public data sets show that the proposed method can make full use of multi-modal information, outperform other advanced comparison methods, improve the accuracy and robustness of sentiment analysis, and is expected to achieve better results in the field of sentiment analysis.

SENTIMENT ANALYSIS OF WEB 3.0 ENABLED TWITTER DATASET

In this paper, the focus is on sentiment analysis of web 3.0 enabled twitter dataset. The objective of the project is to explore various methods for performing sentiment analysis on Twitter datasets and implementing these methods on the web3.0 Twitter platform. The project involves collecting Twitter data through blockchain-based applications, preprocessing the data to remove noise, and applying machine learning models for sentiment analysis. Sentiment analysis is simply the extraction of thoughts, ideas, opinions, and emotions from sources such as text, speech, tweets, and databases using natural language processing (NLP) This process involves text segmentation mentally makes it "good," "bad," and "neutral" groups. In addition, it is known by other terms such as objective evaluation, mindfulness mining, and rating extraction. Web 3.0, also known as Web3, represents the third contemplated iteration of the World Wide Web, which aspires to establish a connected, transparent and intelligent online environment Based on the concept of decentralization, blockchain technology and the implementation of token-based economies. The main outcome of the project is to gain insights into the sentiment of users by analyzing WEB3.0 enabled Twitter data. By implementing sentiment analysis techniques on a WEB3.0 enabled Twitter dataset, the project aims to contribute to the field of sentiment analysis and showcase the effectiveness of using WEB3.0 enabled for data collection and analysis. The project aims to provide valuable insights for various methods of sentiment analysis for researchers. Keywords – Sentiment Analysis, Blockchain- Enabled, Twitter data, WEB 3.0, Machine Learning Models, User Sentiment, Research Contribution

Multi-Task Momentum Distillation for Multimodal Sentiment Analysis

In the field of Multimodal Sentiment Analysis (MSA), the prevailing methods are devoted to developing intricate network architectures to capture the intra- and inter-modal dynamics, which necessitates numerous parameters and poses more difficulties in terms of interpretability in multimodal modeling. Besides, the heterogeneous nature of multiple modalities (text, audio, and vision) introduces significant modality gaps, thereby making multimodal representation learning an ongoing challenge. To address the aforementioned issues, by considering the learning process of modalities as multiple subtasks, we propose a novel approach named Multi-Task Momentum Distillation (MTMD) which succeeds in reducing the gap among different modalities. Specifically, according to the abundance of semantic information, we treat the subtasks of textual and multimodal representations as the teacher networks while the subtasks of acoustic and visual representations as the student ones to present knowledge distillation, which transfers the sentiment-related knowledge guided by the regression and classification subtasks. Additionally, we adopt unimodal momentum models to explore modality-specific knowledge deeply and employ adaptive momentum fusion factors to learn a robust multimodal representation. Furthermore, we provide a theoretical perspective of mutual information maximization by interpreting MTMD as generating sentiment-related views in various ways. Extensive experiments illustrate the superiority of our approach compared with the state-of-the-art methods in MSA.

Тональність художньої прози як вияв особливостей перекладу

The paper is devoted to the peculiarities of sentiment analysis of texts. The necessity to take into account the tone as an important component of the text’s content to achieve equivalence in literary translation and ensure the preservation of the emotional and expressive background and expressiveness of the original is substantiated. The importance of automating the processes of detecting the text’s tone to obtain better and more accurate results is determined. The paper outlines the main methods of preprocessing textual information and identifies a set of the most popular machine learning approaches that can be used for tone classification. In particular, the attention is focused on the Bag-of-Words, TF-IDF Vectorizer, and Word2Vec models used to convert textual data into numerical data. Classifier algorithms are analysed as a further step in data processing (in particular, Bayesian, maximum entropy, support vectors, etc.). The advantages of the LIWC-22 software, which has proven itself and has been successfully tested with the involvement of experts in psychology, sociology and linguistics, are described separately. Using the above-mentioned lexicon, a sentiment analysis of three versions of Y. Vynnychuk's short story («The Embroidered World») translations into English (author's translation by M. Naidan and automated translations by Google and DeepL) is carried out. The total number of words is established, and the indicators of authenticity and tone (in the binary coordinate system «positive/negative») of each translation option are determined. Prospects for further research in the field of sentiment analysis for the interpretation of original and translated texts are formulated.

Sentiment polarity analysis of love letters: Evaluation of TextBlob, Vader, Flair, and Hugging Face transformer

Sentiment analysis is the task of computationally identifying and quantifying the emotions and opinions expressed in text. However, existing sentiment analysis tools, while increasingly sophisticated, face challenges when applied to complex and personal domains such as love letters. This study investigates the performance and accuracy of four popular Python libraries for sentiment analysis (TextBlob, Vader, Flair, and Hugging Face Transformer) in determining the polarity and intensity of sentiments in love letters. A corpus of 300 love letters was collected and randomly sampled to provide 500 sentences for analysis. Due to the lack of labelled data, human experts participated in evaluating the quality and accuracy of the sentiment annotations. Inter-rater agreements were computed among four judges across randomly sampled sentence lots in two distinct blind rounds. The results reveal varying degrees of effectiveness and agreement among sentiment analysis tools (TextBlob, Vader, Flair, and Hugging Face) and human judges, with Cohen?s Kappa values showing low to moderate agreement (ranging from 0.09 to 0.77), and each tool demonstrating unique strengths?Vader excelling in sentiment intensity and Flair with Hugging Face better at contextual nuances?in handling the emotional complexity of the texts. The study also highlights limitations and proposes some custom metrics for evaluating sentiment analysis tools in the context of love letters, such as tenderness index, passion quotient, nostalgia score, and others. The findings contribute to the emerging field of sentiment analysis and provide insights for developing natural language models better suited for personal and emotionally charged domains.

Sentiment Analysis Using Bert Model

Abstract The topic of this presentation entails a comprehensive investigation of our sentiment analysis algorithm. The document provides a thorough examination of its theoretical underpinnings, meticulous assessment criteria, consequential findings, and an enlightening comparative analysis. Our system makes a substantial contribution to the field of sentiment analysis by using advanced techniques based on deep learning and state-of-the-art architectures.

A knowledge-augmented heterogeneous graph convolutional network for aspect-level multimodal sentiment analysis

Aspect-level multimodal sentiment analysis has also become a new challenge in the field of sentiment analysis. Although there has been significant progress in the task based on image–text data, existing works do not fully deal with the implicit sentiment expression in data. In addition, they do not fully exploit the important information from external knowledge and image tags. To address these problems, we propose a knowledge-augmented heterogeneous graph convolutional network (KAHGCN). First, we propose a dynamic knowledge selection algorithm to select the most relevant external knowledge, thereby enhancing KAHGCN’s ability of understanding the implicit sentiment expression in review texts. Second, we propose a graph construction strategy to construct a heterogeneous graph that aggregates review text, image tags and external knowledge. Third, we propose a multilayer heterogeneous graph convolutional network to strengthen the interaction between information from external knowledge, review texts and image tags. Experimental results on two datasets demonstrate the effectiveness of the KAHGCN.

BIDRN: A Method of Bidirectional Recurrent Neural Network for Sentiment Analysis

Text mining research has grown in importance in recent years due to the tremendous increase in the volume of unstructured textual data. This has resulted in immense potential as well as obstacles in the sector, which may be efficiently addressed with adequate analytical and study methods. Deep Bidirectional Recurrent Neural Networks are used in this study to analyze sentiment. The method is categorized as sentiment polarity analysis because it may generate a dataset with sentiment labels. This dataset can be used to train and evaluate sentiment analysis models capable of extracting impartial opinions. This paper describes the Sentiment Analysis-Deep Bidirectional Recurrent Neural Networks (SA-BDRNN) Scheme, which seeks to overcome the challenges and maximize the potential of text mining in the context of Big Data. The current study proposes a SA-DBRNN Scheme that attempts to give a systematic framework for sentiment analysis in the context of student input on institution choice. The purpose of this study is to compare the effectiveness of the proposed SA- DBRNN Scheme to existing frameworks to establish a robust deep neural network that might serve as an adequate classification model in the field of sentiment analysis.

Research on sentiment analysis methods based on aspect word embedding graph convolutional networks

Aspect-based sentiment analysis is a fine-grained task in the field of sentiment analysis. Various GCN approaches have recently emerged to work on this, but many approaches ignored the critical role of aspectual word information and the effect of noise. In view of this situation, we propose an aspect-based word embedding graph convolutional network (AWEGCN) model. In order to make good use of the aspect information and distinguish the contextual information that is more important for a particular aspect, the aspect information is embedded in the output of the hidden layer. To reduce the noise effect when multiple aspect words appear in a sentence, after going through the bidirectional graph convolutional network, the aspect information is embedded. A specific contextual representation is computed through an attention mechanism, which is used as the final classification feature. Experiments show that our model achieves impressive performance on five public datasets, and we also apply BERT and XLNet pre-trained models to this task and obtain advanced results that validate the effectiveness of our model.

Sentiment Analysis on Algerian Dialect with Transformers

The task of extracting sentiment from text has been widely studied in the field of natural language processing. However, little work has been conducted specifically on the Arabic language with the Algerian dialect. In this research, we aim to make a significant contribution to the field of sentiment analysis on the Algerian dialect by creating a custom and relatively large dataset with a tailored deep learning model. The dataset was extracted from Algerian YouTube channels and manually annotated by the research team. We then utilize this dataset to train a state-of-the-art deep learning model for natural language processing called BERT, which is a type of Transformer model. Using this model, we were able to achieve an F1-score of 78.38% and an accuracy of 81.74% on the testing set. This demonstrates the effectiveness of our approach and the potential of using BERT for sentiment analysis on the Algerian dialect. Our model can be used to infer sentiment from any Algerian text, thus providing a valuable tool for understanding the opinions and emotions of the population. This research highlights the importance of studying the Algerian dialect and the potential of using state-of-the-art deep learning models for natural language processing in this area.

Enhancing aspect-based financial sentiment analysis through contrastive learning

The subject of research in the article explores the specialized application of Aspect-Based Financial Sentiment Analysis (ABFSA), focusing on the intricate and multifaceted emotional landscape of financial textual data. The study extends the current understanding of sentiment analysis by addressing its limitations and opportunities within a financial context. The purpose of the work is to advance the field of Aspect-Based Financial Sentiment Analysis by developing a more nuanced and effective methodology for analyzing sentiments in financial news. Additionally, the study aims to assess the efficacy of recent advancements in Natural Language Processing (NLP) and machine learning for enhancing ABFSA models. The article deals with the following tasks: Firstly, the study focuses on the rigorous pre-processing of the SEntFiN dataset to make it more amenable to advanced machine learning techniques, specifically contrastive learning methodologies. Secondly, it aims to architect a unified model that integrates state-of-the-art machine learning techniques, including DeBERTa v3, contrast learning, and LoRa fine-tuning. Lastly, the research critically evaluates the proposed model's performance metrics across the test dataset and compares them with existing methodologies. The following methods are used: Firstly, the study employs pre-processing techniques tailored for the SEntFiN dataset, which is explicitly designed for entity-sensitive sentiment analysis in financial news. Secondly, it utilizes advanced machine learning techniques such as DeBERTa v3 for language model pre-training, contrast learning for focusing on causal relationships, and LoRa for fine-tuning large language models. Lastly, performance evaluation methods are used to assess the efficacy of the proposed model, including comparisons with existing methodologies in the field.The following results were obtained: The study reveals that the proposed pre-processing framework successfully accommodates the variable number of entities present in financial news, thereby improving the granularity of sentiment classification. Furthermore, the integration of advanced NLP and machine learning techniques significantly enhances the accuracy and efficiency of ABFSA models. Conclusions: The paper concludes that specialized ABFSA methodologies, when augmented with advanced NLP techniques and a robust pre-processing framework, can offer a more nuanced and accurate representation of sentiment in financial narratives. The study lays the groundwork for future research in this nascent yet crucial interdisciplinary field, providing actionable insights for stakeholders ranging from investors to financial analysts.

Research on Emotional Analysis of Online Book Reviews Based on Word2Vec Method

At present, emotional analysis in the field of natural language processing is gradually becoming a popular research direction on social media. With the rise of artificial intelligence technology and deep learning algorithms, word vector representation methods based on neural networks have emerged and emerged in the field of sentiment analysis. According to the current research results of experts, the Word2Vec model has become the mainstream word vector representation method. The algorithm model is based on continuous optimization and iteration of random initialization word vectors, ultimately obtaining a stable performance word vector representation. If the research goal is to obtain sentence vectors, weighting processing is also required. Therefore, after systematically summarizing and analyzing the representation techniques of initialized word vectors and solving sentence vectors through word vector weighting, this article proposes corresponding improvement methods. This article improves the initialization algorithm of its word vector based on the Word2Vec model. On the basis of the original randomly generated word vector, the correlation information of words is added to change the distribution characteristics of the initial word vector, thereby enhancing the performance of the word vector. In the comparative experiment of the user comment dataset, the improved model showed a 1% to 3% improvement in sentiment polarity classification accuracy compared to the other model.

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

Sentiment Analysis Using PSEUDO Nearest Neighbor and TF-IDF TEXT Vectorizer

Twitter is one of the social media that is often used by researchers as an object of research to conduct sentiment analysis. Twitter is also a good indicator in influencing research, problems that often arise in research in the field of sentiment analysis are the many factors such as the use of colloquial or informal language and other factors that can affect sentiment results. To improve the results of sentiment classification, it is necessary to carry out a good information extraction process. One of the word weighting methods resulting from the information extraction process is the TF-IDF Vectorizer. This study examines the effect of the TF-IDF Vectorizer weighting results in sentiment analysis using the Pseudo Nearest Neighbor method. The results of the f-measure classification of sentiment using the TF-IDF Vectorizer at parameters k-2 = 89%, k-3 = 89%, k-4 = 71% and k-5 = 75% while without using the TF-IDF Vectorizer on the parameters k-2 = 90%, k-3 = 92%, k-4 = 84% and k-5 = 89%. From the results of the classification of sentiment analysis that does not use the TF-IDF Vectorizer, the f-measure value is slightly better than using it.

Sentiment analysis in multilingual context: Comparative analysis of machine learning and hybrid deep learning models

This research paper investigates the efficacy of various machine learning models, including deep learning and hybrid models, for text classification in the English and Bangla languages. The study focuses on sentiment analysis of comments from a popular Bengali e-commerce site, "DARAZ," which comprises both Bangla and translated English reviews. The primary objective of this study is to conduct a comparative analysis of various models, evaluating their efficacy in the domain of sentiment analysis. The research methodology includes implementing seven machine learning models and deep learning models, such as Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), Convolutional 1D (Conv1D), and a combined Conv1D-LSTM. Preprocessing techniques are applied to a modified text set to enhance model accuracy. The major conclusion of the study is that Support Vector Machine (SVM) models exhibit superior performance compared to other models, achieving an accuracy of 82.56% for English text sentiment analysis and 86.43% for Bangla text sentiment analysis using the porter stemming algorithm. Additionally, the Bi-LSTM Based Model demonstrates the best performance among the deep learning models, achieving an accuracy of 78.10% for English text and 83.72% for Bangla text using porter stemming. This study signifies significant progress in natural language processing research, particularly for Bangla, by enhancing improved text classification models and methodologies. The results of this research make a significant contribution to the field of sentiment analysis and offer valuable insights for future research and practical applications.