Accuracy Of Sentiment Analysis Research Articles

Hierarchical Attention and Contextual Embedding for Robust Sarcasm Detection

Objective: The study aims to develop a technique for sarcasm detection in social media text to enrich the sentiment analysis. Methods: The methodology used in the present research is as follows: emotion-embedded vectors for capturing the emotional content of the text, dynamic contextual modulation for the adaption of the model to the given context, and Hierarchical attention mechanism for segmentation of the text at the different level of abstraction. Findings: Evaluation with the test set proved that the proposed model achieved accuracy rates 89% higher than the benchmark models. Including these several complicated methods helped achieve greater accuracy and F1 measures, contributing to sarcasm detection efficiency. Novelty: This approach ensures that vital sarcastic alerts are detected, providing a better text analysis and increasing sentiment analysis's accuracy and robustness. Keywords: Sarcasm detection, Sentiment analysis, Natural language processing, Deep learning, Opinion mining

A text sentiment analysis method based on BiGRU and capsule network

Most of the existing sentiment analysis methods based on product review texts rarely consider the aspect features of the review texts, and the relevant analysis models do not consider the long-term contextual dependency features and local text features at the same time, thus affecting the accuracy of sentiment analysis. A text sentiment analysis method based on a bidirectional gated recurrent network (BiGRU) and a capsule network is proposed. This method first uses a word frequency statistics-based method to extract the aspect features of the review text and integrates them into the word vector representation, thereby effectively improving the expressive power of the word vector. Then, BiGRU is used to extract the long-term contextual dependency features of the text, and the capsule network is used to extract the local features of the text, thereby achieving high-precision text sentiment analysis based on aspects. Experimental results on real datasets show that the proposed method is superior to existing sentiment analysis models such as bidirectional long short-term memory network (BiLSTM), CNN-LSTM, and TextCNN in terms of evaluation indicators such as accuracy, precision, recall, and score.

Applications of BERT in sentimental analysis

This research study emphasizes sentiment analysis and examines Natural Language Processing (NLP) by Bidirectional Encoder Representations from Transformers (BERT). BERT's bidirectional Transformer architecture pre-trained utilizes Next Sentence Prediction (NSP) and Masked Language Modeling (MLM) and has achieved a lot in terms of AI transformation. This paper provides a description of the BERT design, pre-training methods, and fine-tuning for sentiment analysis tasks. The study goes ahead and compares BERT's performance with other deep learning models, machine learning algorithms, and traditional rule-based techniques, highlighting the latter's limited ability to handle linguistic nuances and context. Additionally, studies proving the consistency and accuracy of BERT's sentiment analysis are examined, along with the challenges of handling irony, sarcasm, and domain-specific data. Ethical and privacy concerns that sentiment analysis inherently raises and makes recommendations for further research are also examined in the study, which also shows how integrating sentiment analysis with other domains can lead to multidisciplinary breakthroughs that can offer more comprehensive insights and applications.

Public Sentiment Analysis of the Israel-Palestine Conflict on Social Media Using BERT

General background Israel-Palestine conflict has drawn significant global attention, particularly in how it is perceived and discussed on social media platforms. Specific background understanding public sentiment surrounding such geopolitical issues is crucial for media monitoring, diplomatic efforts, and reputation management. Knowledge gap previous sentiment analysis studies often lack the ability to accurately handle multilingual and context-rich datasets, especially in analyzing neutral sentiments, which are commonly overlooked. This study aims to apply the Bidirectional Encoder Representations from Transformers (BERT) model to analyze public sentiment towards the Israel-Palestine conflict on the X platform, focusing on Indonesian users. Results Using BERT, the model achieved 93% accuracy, with a precision of 0.95, recall of 0.93, and F1-score of 0.94. The model performed well in predicting positive and negative sentiments but showed room for improvement in handling neutral sentiment. Novelty this study introduces the implementation of the BERT Transformer model for the multilingual and context-sensitive sentiment analysis of tweets, specifically addressing a high-stakes geopolitical conflict. Implications the findings demonstrate the potential for using advanced natural language processing techniques like BERT for monitoring public opinion, brand management, and detecting societal tensions on social media, offering valuable insights for stakeholders involved in conflict resolution and diplomatic strategies. Highlights: Achieved 93% accuracy in sentiment analysis using BERT on X platform. Identified strengths in predicting positive/negative sentiments, with challenges in neutral sentiment. Demonstrated BERT’s effectiveness in handling complex geopolitical social media data. Keywords: Sentiment Analysis, BERT Transformer, Israel-Palestine Conflict, Social Media, NLP

On the Utilization of Emoji Encoding and Data Preprocessing with a Combined CNN-LSTM Framework for Arabic Sentiment Analysis

Social media users often express their emotions through text in posts and tweets, and these can be used for sentiment analysis, identifying text as positive or negative. Sentiment analysis is critical for different fields such as politics, tourism, e-commerce, education, and health. However, sentiment analysis approaches that perform well on English text encounter challenges with Arabic text due to its morphological complexity. Effective data preprocessing and machine learning techniques are essential to overcome these challenges and provide insightful sentiment predictions for Arabic text. This paper evaluates a combined CNN-LSTM framework with emoji encoding for Arabic Sentiment Analysis, using the Arabic Sentiment Twitter Corpus (ASTC) dataset. Three experiments were conducted with eight-parameter fusion approaches to evaluate the effect of data preprocessing, namely the effect of emoji encoding on their real and emotional meaning. Emoji meanings were collected from four websites specialized in finding the meaning of emojis in social media. Furthermore, the Keras tuner optimized the CNN-LSTM parameters during the 5-fold cross-validation process. The highest accuracy rate (91.85%) was achieved by keeping non-Arabic words and removing punctuation, using the Snowball stemmer after encoding emojis into Arabic text, and applying Keras embedding. This approach is competitive with other state-of-the-art approaches, showing that emoji encoding enriches text by accurately reflecting emotions, and enabling investigation of the effect of data preprocessing, allowing the hybrid model to achieve comparable results to the study using the same ASTC dataset, thereby improving sentiment analysis accuracy.

An Optimal Multi-Modal Approach for Stock Market Price Forecasting with Fused Sentiment Analysis for Real Time Data

Abstract: This study presents an innovative fusion-based methodology that integrates real-time stock market technical indicators with news sentiment analysis from financial news feeds to enhance stock selection decisions. The proposed framework employs a Bidirectional Long Short-Term Memory (Bi-LSTM) model for forecasting stock prices and a Deep Neural Network (DNN) used in conjunction with transformer-based model for sentiment classification, both optimized through the incorporation of real-time datasets. To further refine feature selection, Artificial Bee Colony (ABC) and Firefly algorithms are utilized, significantly improving the accuracy of price trend predictions and sentiment analysis. Technical indicators from stock market data are processed using the Bi-LSTM model to predict future stock prices. Concurrently, sentiment data from the Economic Times is pre-processed through Term Frequency-Inverse Document Frequency (TF-IDF) vectorization and pre-trained transformer models to extract key sentiment scores. The ABC algorithm enhances textual feature selection, while the Firefly algorithm reduces skewness in the error distribution of technical indicators, thereby improving the alignment between forecasted and actual stock prices. The fusion mechanism integrates predicted price movements with sentiment analysis to provide actionable insights for stock selection. The decision-making framework classifies stocks based on the consistency between price movements and sentiment: stocks predicted to rise with positive sentiment are labelled as "Sure Selection"; rising prices with negative sentiment are categorized as "Dicey Selection"; declining prices with positive sentiment result in a "Risky Selection"; and negative sentiment with falling prices trigger an "Avoid Selection". The proposed approach achieved a prediction accuracy of 67.00% for the Bi-LSTM model, with a precision of 0.714, recall of 0.695, and an F1 score of 0.677, demonstrating the model's robustness. The mean absolute error (MAE) for the Bi-LSTM model was 4.30, indicating strong predictive performance. The combined use of ABC and Firefly algorithms optimizes both technical and sentiment features, significantly contributing to the overall performance of the prediction model. By fusing multiple data sources and employing advanced optimization techniques, this methodology offers a unique and effective solution for stock market prediction and decision-making

Analysis Of The Use Of Nazief-Adriani Stemming And Porter Stemming In Covid-19 Twitter Sentiment Analysis With Term Frequency-Inverse Document Frequency Weighting Based On K-Nearest Neighbor Algorithm

Abstract. This system was developed to determine the accuracy of sentiment analysis on Twitter regarding the COVID-19 issue using the Nazief-Adriani and Porter stemmers with TF-IDF weighting, along with a classification process using K-Nearest Neighbor (KNN) that resulted in a comparison of 48.24% for Nazief-Adriani and 48.24% for Porter. Purpose: This research aims to determine the accuracy of the Nazief-Adriani and Porter stemmer algorithms in performing text preprocessing using a dataset from Indonesian-language Twitter. This research involves word weighting using TF-IDF and classification using the K-Nearest Neighbor (KNN) algorithm. Methods/Study design/approach: The experimentation was conducted by applying the Nazief-Adriani and Porter stemmer algorithm methods, utilizing data sourced from Twitter related to COVID-19. Subsequently, the data underwent text preprocessing, stemming, TF-IDF weighting, accuracy testing of training and testing data using K-Nearest Neighbor (KNN) algorithm, and the accuracy of both stemmers was calculated employing a confusion matrix table. Result/Findings: This study obtained reasonably accurate results in testing the Nazief-Adriani stemmer with an accuracy of 50.98%, applied to sentiment analysis of COVID-19-related Twitter data using the Indonesian language. As for the accuracy of the Porter stemmer, it achieved an accuracy rate of 48.24%. Novelty/Originality/Value: Feature selection is crucial in stemmer accuracy testing. Therefore, in this study, feature selection is carried out using the Nazief-Adriani and Porter stemmers for testing purposes, and the accuracy data classification is conducted using the K-Nearest Neighbor (KNN) algorithm

Feature Extraction Optimization to Improve Naïve Bayes Accuracy in Sentiment Analysis of Bulukumba Tourism Objects

Feature Extraction Optimization to Improve Naïve Bayes Accuracy in Sentiment Analysis of Bulukumba Tourism Objects

BERT Model Adoption for Sarcasm Detection on Twitter Data

Sarcasm is a term used to criticize someone's feelings. Sometimes, humans are not able to identify sarcastic comments, and they typically express the reverse of what they mean when they make snarky remarks. Therefore, the detection of sarcasm within a text automatically is a difficult task. Its significance in enhancing sentiment analysis has also made it an important study field. In previous studies, different approaches to deep learning (DL) and machine learning (ML) have been explored. However, previous approaches mainly depend on the lexical and linguistic aspects. Therefore, these techniques could not perform well in the context of sentiment accuracy. In this research, an efficient approach for detecting sarcasm is proposed. A Bidirectional Encoder Representation from a Transformer (BERT) is proposed to improve the sentiment accuracy in this research. This research also aims to compare the two models of deep learning, the BERT and LSTM (Long Short-Term Memory) models. This comparative analysis aims to provide a detailed overview of the pros and cons of each approach for the detection of sarcasm. The primary aim of this study is to examine the different existing ML and DL approaches for the identification of sarcasm. Apart from this, the comparison of BERT and LSTM contributes to the ongoing debate about whether models work best for sarcasm detection in social media. In this study, sentiment analysis's accuracy is improved by making better decisions, especially when it concerns Twitter interactions.

Sentiment analysis for airline twitter based of machine learning and deep learning

The study of public opinion can be beneficial for obtaining certain knowledge. Thus, the sentiment analysis of the social networks, for example, the Twitter or Facebook has grown into an effective way of understanding users’ opinion and has many uses. Nevertheless, the efficiency and accuracy of sentiment analysis seem to be hampered by the problems rising from natural language processing processes that are inherent with the text. Currently the airline sector is considered the significant field of the market. To sustain that sector and constantly update it, mind mining becomes inevitable. This paper, proposed a model for sentiment analysis based on extracting two different features. Term frequency-inverse document frequency and Word2vec. These feature introduced separately to different classifiers to classify the sentences as positive, negative, or neutral. Twitter US Airline dataset used to evaluate the performance of the proposed model. Bi-directional Long short Term memory outperformed others methods with recall, Precision, and F-Score reached to 0.97, 0.98,and 0.97 respectively when using Word2vec feature.

Distance-reconstructed dependency enhanced aspect-based sentiment analysis with sentiment strength

Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task that requires the detection of sentiment polarity towards specific aspects. Dependency tree-based graph convolutional network models (GCN) have been widely applied in ABSA. These studies utilize syntactic analysis tools to derive syntactic dependency graphs, which are then inputted into GCN. It enables iterative learning of sentiment dependency information from contextual words to aspect terms. However, directly using syntactic dependency graphs can lead to two shortcomings. First, the long-distance sentiment information in long sentences will be weakened during GCN iterative computations. Second, the syntactic dependency graphs of short sentences lack sentiment strength information. To address these two shortcomings, we propose a Distance-Reconstructed Dependency Enhanced Sentiment analysis approach with Sentiment Strength based on GCN (DRD-SE-GCN) approach, in which the syntactic dependency graph of long sentences is reconstructed based on distance, thus narrowing the gap between sentiment words and target aspect words. It ensures that the sentiment dependency information carried by the long-distance sentiment words will not be weakened or lost during the GCN iteration. Additionally, the weights of the syntactic dependency graph for short sentences are reassigned so as to carry the strength information of sentiment words. Experimental results on four standard datasets show that our proposed model can effectively improve the accuracy of aspect-level sentiment analysis.

Sentiment Analysis of Arab Tweets: Unveiling Public Opinion Trends Using Machine Learning

Abstract: The rapid spread of social media platforms, especially Twitter, has provided a rich and valuable source of data for understanding public opinion. This thesis presents a comprehensive study on analyzing the sentiments of Arab tweeters, intending to reveal public opinion trends using advanced machine learning techniques. The study begins by collecting a large data set of tweets in Arabic. Preprocessing steps are carefully implemented to deal with the unique linguistic characteristics of Arabic, including encoding, normalization, and deletion. The BERT system was used for the application and the model was created using AJGT.csv Dataset. The evaluation was performed using different metrics. The test results were as follows: 0.9822 % accuracy, precision 0.9823, recall 0.9822, F1 0.982265 and FPR 0.0177. The findings of this research provide significant insights into the prevailing public opinion trends in the Arab world, revealing the potential of sentiment analysis as a powerful tool for policymakers, businesses, and researchers to gauge public sentiment and make informed decisions. The thesis concludes with a discussion of the limitations of the study and suggestions for future research directions, emphasizing the need for more sophisticated models and larger datasets to further enhance the accuracy and reliability of sentiment analysis in the Arabic language.

English Sentiment Analysis and its Application in Translation Based on Decision Tree Algorithm

Sentimental analysis belongs to the class of Natural Language Processing (NLP) based on the rule and machine model. The proposed model comprises of the pre-defined function for the estimation of the features in the English statements. This paper presents the Reflect Sentiment Translation Decision Tree (RSTDT), a novel model designed to integrate sentiment analysis and translation tasks for English text. The RSTDT model combines the strengths of decision tree algorithms with feature extraction techniques to accurately analyze sentiment and translate text across languages. The proposed RSTDT dataset comprises English sentences with annotated sentiment labels, the RSTDT model is trained to identify sentiment polarity and generate corresponding translations in Arabic. The proposed RSTDT model uses Traslation mapping for the estimation of the sentimental features. In order to estimate and classify the features in the neural network, the processes features are assessed using the decision tree model. The RSTDT model's efficacy in precisely capturing sentiment nuances and generating linguistically appropriate translations was shown through thorough testing and review. The model achieves high accuracy in sentiment analysis and exhibits proficiency in translating sentiment-rich content into Arabic while maintaining contextual relevance. Additionally, robust classification performance metrics underscore the model's efficacy in accurately classifying English words into sentiment categories. The RSTDT model offers a promising solution for multilingual sentiment analysis applications, with potential applications in social media monitoring, customer feedback analysis, and cross-cultural sentiment analysis.

ArabBert-LSTM: improving Arabic sentiment analysis based on transformer model and Long Short-Term Memory.

Sentiment analysis also referred to as opinion mining, plays a significant role in automating the identification of negative, positive, or neutral sentiments expressed in textual data. The proliferation of social networks, review sites, and blogs has rendered these platforms valuable resources for mining opinions. Sentiment analysis finds applications in various domains and languages, including English and Arabic. However, Arabic presents unique challenges due to its complex morphology characterized by inflectional and derivation patterns. To effectively analyze sentiment in Arabic text, sentiment analysis techniques must account for this intricacy. This paper proposes a model designed using the transformer model and deep learning (DL) techniques. The word embedding is represented by Transformer-based Model for Arabic Language Understanding (ArabBert), and then passed to the AraBERT model. The output of AraBERT is subsequently fed into a Long Short-Term Memory (LSTM) model, followed by feedforward neural networks and an output layer. AraBERT is used to capture rich contextual information and LSTM to enhance sequence modeling and retain long-term dependencies within the text data. We compared the proposed model with machine learning (ML) algorithms and DL algorithms, as well as different vectorization techniques: term frequency-inverse document frequency (TF-IDF), ArabBert, Continuous Bag-of-Words (CBOW), and skipGrams using four Arabic benchmark datasets. Through extensive experimentation and evaluation of Arabic sentiment analysis datasets, we showcase the effectiveness of our approach. The results underscore significant improvements in sentiment analysis accuracy, highlighting the potential of leveraging transformer models for Arabic Sentiment Analysis. The outcomes of this research contribute to advancing Arabic sentiment analysis, enabling more accurate and reliable sentiment analysis in Arabic text. The findings reveal that the proposed framework exhibits exceptional performance in sentiment classification, achieving an impressive accuracy rate of over 97%.

Financial Report Sentiment Analysis Using Loughran-mcdonald Dictionary and BERT

In the ever-changing world of financial markets, understanding investor behavior and making informed decisions relies heavily on sentiment analysis. This study delves into the integration of traditional techniques, such as the Loughran- McDonald dictionary, with advanced natural language processing (NLP) methods utilizing BERT (Bidirectional Encoder Representations from Transformers). The goal is to enhance the accuracy and depth of sentiment analysis in financial reports.To begin, we employ the specialized Loughran-McDonald dictionary designed for financial sentiment analysis. This lexicon includes domainspecific word lists for positive and negative sentiments, forming a solid foundation for sentiment scoring. Expanding on this foundation, we incorporate BERT, an advanced transformerbased NLP model. BERT’s contextual understanding of language and ability to capture intricate semantic relationships within financial texts aim to overcome the limitations of rule-based sentiment analysis. The methodology involves preprocessing financial reports, integrating Loughran-McDonald sentiment scores, and fine-tuning BERT for financial sentiment classification. This hybrid approach leverages both the domain expertise encoded in the dictionary and BERT’s contextual comprehension of financial jargon and nuances. We validate and evaluate our implementation using a diverse dataset comprising quarterly earnings releases, annual reports, and other relevant disclosures. Performance metrics such as precision, recall, and F1 score are analyzed to assess the effectiveness of our hybrid approach compared to individual methods. The findings have significant implications for financial analysts, investors, and policymakers by providing a more nuanced understanding of sentiment in financial reports. Our hybrid approach aims to offer improved accuracy in capturing sentiment polarity while facilitating more informed decision-making in today’s complex and dynamic realm of financial markets.

Deciphering Negative Feedback through Handwritten Text

In the contemporary realm of digital communication, this study delves into a novel approach to analyzing sentiments, with a specific focus on handwritten negative reviews. The research aims to improve the precision of sentiment analysis by exploring the inclusion of handwritten text, providing distinctive insights into consumer feedback, and refining business strategies. Taking a historical perspective, the investigation traces the evolution of sentiment analysis within the domain of natural language processing, leading to the central question: "How can the accuracy of sentiment analysis for negative reviews be enhanced through the incorporation of handwritten text?" This question guides an exploration of the challenges and potentials associated with merging handwriting analysis with conventional sentiment analysis methods. The study puts forth hypotheses that address the expected advantages of this integration, seeking to develop an innovative framework capable of not only accurately detecting negative sentiments but also considering the individuality inherent in handwritten expressions. The research methodology encompasses a review of existing literature and empirical analysis, resulting in the creation of a unique hybrid sentiment analysis algorithm that assesses both textual and handwriting features. This work contributes to the advancement of sentiment analysis and has implications for businesses aiming to better understand and respond to negative consumer sentiments.

Sentiment Analysis Based on Transformer

This article aims to explore the sentiment analysis of Transformer comments on Weibo to understand the attitudes and emotional tendencies of social media users regarding the movie The boy and the heron. This paper uses natural language processing technology and sentiment analysis model Transformer to crawl the Weibo comment data set, analyzes the anti-crawling mechanism of Weibo, adds HEADERS and REFERER directly in the code to bypass the inspection, and preprocesses it. Sentiment analysis was conducted on a large number of Weibo comments related to the boy and the heron. The results of the study showed that the vast majority of the comments expressed positive emotional attitudes and held positive and appreciative attitudes towards the boy and the heron. This study reveals that social media users have positive attitudes and emotional identification with the boy and the heron, and the audience really likes the film, which is of great help for the filmmakers to further understand the advantages and disadvantages of the film. Performing Weibo sentiment analysis based on advanced natural language processing technologies such as transformers can not only improve the accuracy and efficiency of sentiment analysis, but also help promote the progress and application of artificial intelligence technology. This is important for the development of more intelligent social media analysis tools and systems.

A shared-private sentiment analysis approach based on cross-modal information interaction

To explore the heterogeneous sentiment information in each modal feature and improve the accuracy of sentiment analysis, this paper proposes a Multimodal Sentiment Analysis based on Text-Centric Sharing-Private Affective Semantics (TCSP). First, the Deep Canonical Time Wrapping (DCTW) algorithm is employed to effectively align the timing deviations of Audio and Picture modalities. Then, a cross-modal shared mask matrix is designed, and a mutual attention mechanism is introduced to compute the shared affective semantic features of Audio-picture-to-text. Following this, the private affective semantic features within Audio and Picture modalities are derived via the self-attention mechanism with LSTM. Finally, the Transformer Encoder structure is improved, achieving deep interaction and feature fusion of cross-modal emotional information, and conducting emotional analysis. Experiments are conducted on the IEMOCAP and MELD datasets. By comparing with current state-of-the-art models, the accuracy of the TCSP model reaches 82.02%, fully validating the effectiveness. In addition, the rationality of the design of each structure within the model is verified through ablation experiments.

Sentiment Analysis of YouTube Comments

This study explores sentiment analysis of YouTube comments using machine learning algorithms including CNN, LSTM, SVM, Naive Bayes, and Random Forest. Implementing ensemble learning techniques, we evaluate their accuracies to understand public sentiment. The backend is built with Django, frontend with Vue.js, facilitating user-friendly visualization of results. Our findings highlight ensemble learning's effectiveness in enhancing sentiment analysis accuracy, offering insights into public sentiment on online platforms

A hybrid machine learning model for sentiment analysis and satisfaction assessment with Turkish universities using Twitter data

Social media platforms, such as X (Twitter), contain enormous amounts of data, and collecting valuable information from that data assists in making more informed decisions. In recent years, governments and institutions have begun to explore the possibilities of utilizing social networks as a platform to supply and enhance the quality of their services. Consequently, there is an increased demand to estimate people’s satisfaction with Turkish Universities written in Turkish wherever it is challenging and requires more effort to cope with its rich morphological structure to perform a Sentiment Analysis task. This study proposes a Turkish text sentiment analysis methodology for estimating people’s satisfaction with Turkish Universities by employing nine conventional machine learning models, deep learning techniques, and BERT-based transformers upon an original manually annotated dataset consisting of 17,793 tweets in Turkish. An innovative hybrid architecture, named BERT-BiLSTM-CNN, integrates Bidirectional Encoder Representations from Transformers (BERT), Bidirectional Long Short-Term Memory (BiLSTM), and a triple parallel Convolutional Neural Network (CNN) branch, achieving exceptional accuracy in sentiment analysis of Turkish tweets. During testing, the proposed architecture revealed an impressive accuracy rate of over 0.9101, an F1 Score of 0.8801, and a Receiver Operating Characteristic (ROC) of 0.9632 for analyzing sentiment, demonstrating that the model outperformed the state-of-the-art models, and it is capable of coping with the linguistic complexities of Turkish sentiment analysis. This work provides new insights into sentiment analysis by proposing a hybrid model that combines several computational methodologies to improve the understanding of attitudes.