Improve Sentiment Analysis Research Articles

Improving Sentiment Analysis Accuracy Using CRNN on Imbalanced Data: a Case Study of Indonesian National Football Coach

Conducting sentiment research on the perception of the Indonesian people towards Shin Tae Yong's (STY) role as coach of the Indonesian National Football Team (PSSI) is crucial as it can assist PSSI in determining whether to extend STY's contract. Prior studies have demonstrated that Deep Learning achieves a high level of accuracy when applied to sentiment analysis in many domains. Nevertheless, no investigation has been conducted thus far utilizing deep learning techniques to examine emotion towards STY. This study employs modified Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Convolutional Recurrent Neural Networks (CRNN), and CRNN models with and without data oversampling. The research findings indicate that the CRNN model, when combined with data oversampling and a redesigned architecture, achieves the highest level of accuracy (1.00) and consistently performs well. This research provides significant contributions in three areas: firstly, it utilizes Deep Learning techniques for sentiment analysis on STY; secondly, it modifies the CRNN architecture; and thirdly, it applies data oversampling to address the issue of imbalanced data.

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.

Machine Learning-based Voting Classifier for Improving Sentiment Analysis on Twitter Data

Machine Learning-based Voting Classifier for Improving Sentiment Analysis on Twitter Data

Natural Language Processing (NLP) for Sentiment Analysis in Social Media

Purpose: This study sought to analyze Natural Language Processing (NLP) for sentiment analysis in social media. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to Natural Language Processing (NLP) for sentiment analysis in social media. Preliminary empirical review revealed that advanced computational techniques significantly advanced the understanding of sentiments across diverse social media platforms. Methodologies such as machine learning algorithms and deep learning models like CNNs and RNNs demonstrated robust capabilities in categorizing sentiments accurately and capturing contextual nuances such as sarcasm and slang. The research highlighted the interdisciplinary nature of NLP applications, integrating linguistics with computer science and social sciences to develop effective frameworks for analyzing large-scale social media data. These findings contributed to enhancing decision-making in marketing, politics, and public opinion research, pointing towards future directions in hybrid NLP models for improved sentiment analysis across different languages and cultural contexts. Unique Contribution to Theory, Practice and Policy: The Social Constructionism, Cognitive Linguistics and Discourse Analysis Theory may be used to anchor future studies on Natural Language Processing (NLP). The recommendations aimed to advance theoretical foundations by exploring deep learning models and nuanced sentiment lexicons. Practical applications were enhanced through the development of scalable NLP tools for real-time data processing and integration into social media platforms. Policy implications focused on establishing ethical guidelines for data privacy and bias mitigation in sentiment analysis algorithms. Cross-disciplinary collaboration fostered innovation by integrating insights from computer science, linguistics, psychology, and social sciences. Education initiatives and international collaborations were prioritized to build capacity and standardize methodologies globally, ensuring advancements in both research and practical deployment of sentiment analysis technologies.

Improving Sentiment Analysis and Topic Extraction in Indonesian Travel App Reviews Through BERT Fine-Tuning

Improving Sentiment Analysis and Topic Extraction in Indonesian Travel App Reviews Through BERT Fine-Tuning

Improving Sentiment Analysis in Digital Marketplaces through SVM Kernel Fine-Tuning

Improving Sentiment Analysis in Digital Marketplaces through SVM Kernel Fine-Tuning

A robust approach for aspect-based sentiment analysis using deep learning and domain ontologies

PurposeThe purpose of this research study is to improve sentiment analysis (SA) at the aspect level, which is accomplished through two independent goals of aspect term and opinion extraction and subsequent sentiment classification.Design/methodology/approachThe proposed architecture uses neighborhood and dependency tree-based relations for target opinion extraction, a domain–ontology-based knowledge management system for aspect term extraction, and deep learning techniques for classification.FindingsThe authors use different deep learning architectures to test the proposed approach of both review and aspect levels. It is reported that Vanilla recurrent neural network has an accuracy of 83.22%, long short-term memory (LSTM) is 89.87% accurate, Bi-LSTM is 91.57% accurate, gated recurrent unit is 65.57% accurate and convolutional neural network is 82.33% accurate. For the aspect level analysis, ρaspect comes out to be 0.712 and Δ2aspect is 0.384, indicating a marked improvement over previously reported results.Originality/valueThis study suggests a novel method for aspect-based SA that makes use of deep learning and domain ontologies. The use of domain ontologies allows for enhanced aspect identification, and the use of deep learning algorithms enhances the accuracy of the SA task.

Enhancing Social Media Insights: Leveraging Artificial Intelligence for Sentiment Analysis

Social media platforms have become indispensable channels for communication and interaction, offering a wealth of data that can provide valuable insights into public sentiments. However, analyzing sentiment on these platforms poses significant challenges due to the diverse and unstructured nature of user-generated content. Traditional Natural Language Processing (NLP) techniques struggle to accurately classify sentiments expressed through text, images, emoticons, and multimedia elements. Moreover, the informal and nuanced language used in Electronic Word of Mouth (eWOM) further complicates sentiment analysis. In response, this paper explores the role of Artificial Intelligence (AI) in improving sentiment analysis on social media. By leveraging Machine Learning (ML) algorithms trained on large datasets, AI can enhance the accuracy and efficiency of sentiment classification, providing decision-makers with actionable insights into the sentiment landscape of social media. Keywords: Social media, Sentiment analysis, Artificial Intelligence, Machine Learning, Natural Language Processing, Electronic Word of Mouth.

Improving Sentiment Analysis of Shopee Reviews with Informal Language and Slang

Improving Sentiment Analysis of Shopee Reviews with Informal Language and Slang

Improving sentiment analysis using text network features within different machine learning algorithms

Sentiment analysis poses a significant challenge due to the inherent subjectivity of natural language and the prevalence of unstandardized dialects in social networks. Regrettably, existing literature lacks a dedicated focus on network representation learning for sentiment classification. This paper addresses this gap by investigating ten machine learning algorithms, including support vector machine (SVM), random forest (RF), logistic regression (LR), and Naive Bayes (NB). Our approach integrates text network analysis and sentiment analysis to propose a comprehensive solution. We begin by applying text preprocessing techniques and converting a text corpus into a text network using word co-occurrence. Subsequently, we employ network analysis techniques to extract features based on network topology and node attributes. These network-derived features serve as inputs for sentiment prediction on Yelp reviews. Through the incorporation of diverse text network features and various machine learning algorithms, we achieve significant enhancements in sentiment classification performance. Our evaluation demonstrates an improved area under curve (AUC) of 83% on the Yelp reviews corpus, underscoring the efficacy of integrating network features to enhance sentiment classifiers. This research underscores the critical role of network representation and its potential impact on sentiment analysis, highlighting the prospect of harnessing network features for sentiment classification tasks.

Sentiment Analysis in Portuguese Restaurant Reviews: Application of Transformer Models in Edge Computing

This study focuses on improving sentiment analysis in restaurant reviews by leveraging transfer learning and transformer-based pre-trained models. This work evaluates the suitability of pre-trained deep learning models for analyzing Natural Language Processing tasks in Portuguese. It also explores the viability of utilizing edge devices for Natural Language Processing tasks, considering their computational limitations and resource constraints. Specifically, we employ bidirectional encoder representations from transformers and robustly optimized BERT approach, two state-of-the-art models, to build a sentiment review classifier. The classifier’s performance is evaluated using accuracy and area under the receiver operating characteristic curve as the primary metrics. Our results demonstrate that the classifier developed using ensemble techniques outperforms the baseline model (from 0.80 to 0.84) in accurately classifying restaurant review sentiments when three classes are considered (negative, neutral, and positive), reaching an accuracy and area under the receiver operating characteristic curve higher than 0.8 when examining a Zomato restaurant review dataset, provided for this work. This study seeks to create a model for the precise classification of Portuguese reviews into positive, negative, or neutral categories. The flexibility of deploying our model on affordable hardware platforms suggests its potential to enable real-time solutions. The deployment of the model on edge computing platforms improves accessibility in resource-constrained environments.

Cross-Domain Aspect-Based Sentiment Analysis for Enhancing Customer Experience in Electronic Commerce

Cross-Domain ABSA has proved effective in extracting more descriptive sentiment information from the reviews or feedback as an important step in improving the experience of the customers in electronic commerce. This work examines the performance of ABSA in the cross-domain scenario where models trained on the review data of one domain for example electronics domain are applied to another domain such as fashion domain. Here, we put forward a risk mitigation strategy that builds upon transfer learning and domain adversarial training methodologies to enhance the overall resilience and reliability of sentiment estimations across multiple product domains. The proposed model was tested using data obtained from different e-commerce retailers, such as Amazon, eBay, and Alibaba concerning various categories of products including electronics, fashion, and home appliances. The outcome of experiments show better performance over most of the compared methods of single-domain ABSA and cross-domain approaches. The model offered greater accuracy, recall rate, F1- score, and cross-domain efficiency, which demonstrated the model’s effectiveness and versatility. The consequences for e-business companies are significant. Improved sentiment analysis allows businesses to obtain more specific data about customers’ opinions, correct mistakes when developing products, and adjust their advertising approaches. Also, the use of advanced text analytics to measure and monitor such aspects in different product areas offers a competitive edge and improves product innovation decisions. However, there are several limitations to the study itself, such as the variations that may arise among the domain, and the limited availability of the data. Further work is to be done on more complex and sophisticated methods of domain adaptation, using external resources; the main model itself should also be much faster and more efficient in scalability. This work thus indicates an opportunity for cross-domain ABSA to generate practical insights for enhancing customers’ experience within e-commerce

Using Large Language Models to Improve Sentiment Analysis in Latvian Language

Using Large Language Models to Improve Sentiment Analysis in Latvian Language

An efficient Cucconi based feature extraction with random decision forest classification for improved sentiment analysis

Sentiment analysis is a form of opinion mining technique that identifies the polarity of extracted opinions. Nowadays, opinion mining has become an important research area in recent decades to identify the polarity of the statements. Various research works have been carried out on sentiment analysis. However, the existing sentimental analysis techniques, such as time and space complexity, still have considerable limitations. To deal with these issues, this paper proposed the Cucconi Feature Extracted Random Decision Forest Classification (CFDFC) Approach. The main objective of the CFDFC approach is to provide effective sentiment analysis with improved accuracy and reduced time complexity. The proposed CFDFC framework comprises pre-processing, feature extraction, and classification. The pre-processing step eliminates stop words and stem words from user reviews. After the pre-processing step, the feature extraction process is carried out to minimize the dimensionality and time consumption for opinion classification. Cucconi's projective feature extraction process is used in this work to reduce dimensionality. Finally, the classification process is formulated using a random decision forest classifier. The random decision forest classifier uses the ID3 DT (decision tree) as a weak learner to classify the review statements. The performance evaluation of the proposed approach is carried out using performance metrics such as accuracy, error rates, recall values, and time and space complexities concerning the number of review statements gathered from the dataset. The results show that the proposed CFDFC model achieves remarkable accuracy, recall, and minimal time complexity compared to existing methods.

Improving sentiment analysis of financial news headlines using hybrid Word2Vec-TFIDF feature extraction technique

With the evolution of big data and information technology, sentiment analysis has become a research hotspot in the financial market. Researchers are increasingly focused on improving the efficiency of sentiment analysis using different machine learning and deep learning architectures. Feature extraction is a fundamental process in sentiment analysis that enhances text representation and classification. Though various feature extraction techniques are present in the field, limited attention has been given to hybrid feature extraction techniques. The primary objective of this study is to improve the sentiment analysis of financial news headlines using a hybrid Word2Vec-TFIDF feature extraction technique. The study evaluates the performance of Word2Vec, Doc2Vec, TFIDF, Word2Vec-TFIDF, and Doc2Vec-TFIDF with six machine learning classifiers. The results find that the hybrid feature extraction technique, Word2vec-TFIDF with SVM classifier, outperforms all other sentiment analysis models with an accuracy of 82 percent.

Enhanced negation handling for sentiment analysis on Twitter using deep neural networks

<span>Sentiment analysis is a tool to identify and measure the emotion in a piece of text. Negation handling is an important aspect of natural language processing (NLP) for Twitter data. It is a process of correctly interpreting the sentences containing negation words, such as, "never", "no", "neither" and so on. Negation words are used in machine learning to express negative sentiment or indicate the absence of something. In this article, a negation handling technique using deep learning models. Artificial neural networks (ANNs) and convolutional neural networks (CNNs) for classification is proposed. The system is evaluated on SemEval-2017 dataset. The classification performance is improved by using ANN and CNN on the negative tweets. The study aims to improve the classification accuracy by considering negation words in the text. The paper compares the performance of ANNs and CNNs in handling negation words and evaluates them on the tweets data. This study provides insights into the effectiveness of using deep learning techniques for negation handling in sentiment analysis and highlights the importance of considering negation words in the text for improved sentiment analysis performance. The proposed negation strategy attains a superior performance accuracy over machine learning models by preventing misclassified tweets.</span>

Improving sentiment analysis on PeduliLindungi comments: a comparative study with CNN-Word2Vec and integrated negation handling

This study investigates sentiment analysis in Google Play reviews of the PeduliLindungi application, focusing on the integration of negation handling into text preprocessing and comparing the effectiveness of two prominent methods: CNN-Word2Vec CBOW and CNN-Word2Vec SkipGram. Through a meticulous methodology, negation handling is incorporated into the preprocessing phase to enhance sentiment analysis. The results demonstrate a noteworthy improvement in accuracy for both methods with the inclusion of negation handling, with CNN-Word2Vec SkipGram emerging as the superior performer, achieving an impressive 76.2% accuracy rate. Leveraging a dataset comprising 13,567 comments, this research introduces a novel approach by emphasizing the significance of negation handling in sentiment analysis. The study not only contributes valuable insights into the optimization of sentiment analysis processes but also provides practical considerations for refining methodologies, particularly in the context of mobile application reviews.

A Feature Extraction based Improved Sentiment Analysis on Apache Spark for Real-time Twitter Data

This paper aims to improve the accuracy of sentiment analysis on Apache Spark for a real-time general twitter data. A lot of works exist on sentiment analysis on offline or stored twitter data that uses several classification algorithms on relevant features extracted using well-known feature extraction methodologies on pre-processed text data. However, not much works exist for sentiment analysis of real-time twitter data and especially for the generic data on big data processing platforms such as Apache Spark. This paper proposes a real-time sentiment analysis for generic twitter data through Apache Spark using six classification algorithms on N-gram and Term Frequency – Inverse Document Frequency (TF-IDF) feature extraction methodologies on the pre-processed data. An exhaustive comparison is done using Logistic Regression (LR), Multinomial Naive Bayes (MNB), Random Forest Classfier (RFC), Support Vector Machine (SVM), K-Nearest Neighbour (K-NN), and Decision Tree (DT) classification algorithms. It is observed that the trigram feature extraction method performs the best on LR and SVM and the RFC results are also comparable on the considered general tweets data.

Multimodal Sentiment Analysis: A Survey of Methods, Trends, and Challenges

Sentiment analysis has come long way since it was introduced as a natural language processing task nearly 20 years ago. Sentiment analysis aims to extract the underlying attitudes and opinions toward an entity. It has become a powerful tool used by governments, businesses, medicine, marketing, and others. The traditional sentiment analysis model focuses mainly on text content. However, technological advances have allowed people to express their opinions and feelings through audio, image and video channels. As a result, sentiment analysis is shifting from unimodality to multimodality. Multimodal sentiment analysis brings new opportunities with the rapid increase of sentiment analysis as complementary data streams enable improved and deeper sentiment detection which goes beyond text-based analysis. Audio and video channels are included in multimodal sentiment analysis in terms of broadness. People have been working on different approaches to improve sentiment analysis system performance by employing complex deep neural architectures. Recently, sentiment analysis has achieved significant success using the transformer-based model. This paper presents a comprehensive study of different sentiment analysis approaches, applications, challenges, and resources then concludes that it holds tremendous potential. The primary motivation of this survey is to highlight changing trends in the unimodality to multimodality for solving sentiment analysis tasks.

MTR-SAM: Visual Multimodal Text Recognition and Sentiment Analysis in Public Opinion Analysis on the Internet

Existing methods for monitoring internet public opinion rely primarily on regular crawling of textual information on web pages but cannot quickly and accurately acquire and identify textual information in images and videos and discriminate sentiment. The problems make this a challenging research point for multimodal information detection in an internet public opinion scenario. In this paper, we look at how to dynamically monitor the internet opinion information (mostly images and videos) that different websites post. Based on the most recent advancements in text recognition, this paper proposes a new method of visual multimodal text recognition and sentiment analysis (MTR-SAM) for internet public opinion analysis scenarios. In the detection module, a LK-PAN network with large sensory fields is proposed to enhance the CML distillation strategy, and an RSE-FPN with a residual attention mechanism is used to improve feature map representation. Second, it proposes that the original CTC decoder be replaced with a GTC method to solve earlier problems with text detection at arbitrary rotation angles. Additionally, the performance of scene text detection for arbitrary rotation angles is improved using a sinusoidal loss function for rotation recognition. Finally, the improved sentiment analysis model is used to predict the sentiment polarity of the text recognition results. The experimental results show that the new method proposed in this paper improves recognition speed by 31.77%, recognition accuracy by 10.78% on the video dataset, and the F1 score of the multimodal sentiment analysis model by 4.42% on the self-built internet public opinion dataset (lab dataset). The method proposed provides significant technical support for internet public opinion analysis in multimodal domains.