Sentiment Analysis of Meme Images Using Deep Neural Network Based on Keypoint Representation

Social media platforms generate vast amounts of user-generated content, reflecting public opinions on various topics. Sentiment analysis, a subfield of natural language processing (NLP), aims to classify this content into positive, negative, or neutral sentiments. This study compares traditional machine learning techniques (Naïve Bayes, Support Vector Machine) with deep learning models (LSTM, CNN, BERT) for sentiment classification. We preprocess social media text using tokenization, word embeddings, and transformer-based feature extraction. Experimental results on Twitter, Facebook, and Reddit datasets demonstrate that BERT outperforms conventional models in capturing contextual sentiment, achieving a classification accuracy of 92.7%. Challenges such as sarcasm detection, multilingual processing, and noisy data are discussed. The findings highlight the effectiveness of deep learning in real-time sentiment analysis, paving the way for advanced applications in business intelligence, political analysis, and customer feedback systems.This study employs multiple NLP techniques, including tokenization, stop-word removal, lemmatization, and word embeddings such as Word2Vec and GloVe, to preprocess and enhance text data quality. We use publicly available datasets, including Twitter, Facebook, and Reddit, to evaluate the performance of different sentiment analysis models. Experimental results indicate that transformer-based models outperform traditional ML algorithms in accuracy, precision, recall, and F1-score, demonstrating their ability to handle challenges like sarcasm, slang, and multilingual sentiment analysis.Additionally, this paper discusses the practical applications of sentiment analysis in various domains, such as business intelligence, political forecasting, healthcare monitoring, and crisis management. Challenges such as noisy data, ethical concerns, bias in sentiment classification, and the need for real-time processing are also examined. The findings of this study highlight the growing significance of deep learning in analyzing user sentiments on social media and provide insights into future advancements in sentiment analysis research

Sentiment analysis for Indonesian social media text is very important because the text content in social media is very diverse and requires an accurate method that can produce an analysis describing the state of the actual data. The main problem in sentiment analysis for Indonesian text on social media is unstructured text data and the use of non-standard languages such that sentiment analysis often produces errors. This paper focuses on sentiment analysis using a hybrid model that combines lexicon based and maximum entropy methods to classify the sentiments of Indonesian public opinion on government. The method consists of extracting datasets, preprocessing, lexicon-based classification, machine learning training, machine learning classification, and result interpretation. The results of the study produce 91 classifications of neutral sentiment, 51 document negative sentiments, 39 document positive sentiments and 152 document of mix sentiments. Based on the evaluation results, the hybrid sentiment model for Indonesian Language sentiment analysis on social media produced a pretty good accuracy score of 84.31% compared to previous studies. The implication of this study is to produce a sentiment analysis system with a hybrid method for Indonesian text on social media.

The rise of social media platforms has revolutionized the way consumers interact with retailers and express their opinions on products and services. Online retailers particularly need to keep a close eye on customer sentiment in real-time to make informed decisions about their offerings and improve customer satisfaction. However, efficiently analysing large volumes of unstructured text data from social media in real-time poses a significant challenge. This research aimed to develop a scalable, real-time sentiment analysis system tailored for online retailers using Reddit as the data source. The system comprises three main components: a data extraction and streaming pipeline, a sentiment analysis model, and a web application with real-time analytics. To address the data extraction challenge, a job queue-based system was implemented using Node.js, ‘BullMQ’, and Redis to create and manage campaigns for data streaming from Reddit. The data was streamed using Kafka, a distributed streaming platform, to enable efficient real-time processing. The sentiment analysis model was developed using a Naive Bayes classifier after experimenting with other machine learning and deep learning techniques. In the conducted study, the sentiment analysis model's performance was evaluated using standard metrics tailored to the context of online retail sentiment analysis. An accuracy of 0.6737 was achieved, reflecting the model's ability to correctly classify approximately 67.37 per cent of the sentiments in the test data. Concurrently, an F1 score of 0.7894 was recorded and the Area Under the Curve (AUC) value on the test data was measured at 0.5468, a metric that, while acceptable, suggests room for further refinement in the model's discriminatory ability between classes. The integration of the Data Version Control (DVC) system provided a mechanism for fine-tuning the model according to specific data requirements of various tenants. These results, taken together, not only validate the feasibility of employing a Naive Bayes classifier for real-time sentiment analysis in the retail context, but also provide a baseline for future research aimed at enhancing both the accuracy and efficiency of sentiment classification. The project’s evaluation focused on the performance of the sentiment analysis model, the efficiency of the Kafka streaming and real-time Spark pre-processing pipeline, and the backend infrastructure, including the job queuing system and WebSocket implementation. Various evaluation techniques, such as graphs and literature comparisons, were used to assess the system’s performance. In conclusion, this project successfully demonstrated the feasibility of a scalable, real-time sentiment analysis system for online retailers using Reddit data. The system has the potential to help retailers better understand customer opinions and make data-driven decisions for their businesses. Future work could include exploring alternative data sources, experimenting with more advanced sentiment analysis techniques, and enhancing the web application’s user interface and analytics capabilities.

This study explores sentiment analysis on Indonesian text using a hybrid deep learning approach that combines Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM). Due to the complex linguistic structure of the Indonesian language, sentiment classification remains challenging, necessitating advanced methods to capture both local patterns and sequential dependencies. The primary objective of this research is to improve sentiment classification accuracy by leveraging a hybrid model that integrates CNN for feature extraction and Bi-LSTM for contextual understanding. The dataset consists of 800 manually labeled samples collected from social media platforms, preprocessed using case folding, stop word removal, and lemmatization. Word embeddings are generated using the Word2Vec CBOW model, and the classification model is trained using a hybrid architecture. The best performance was achieved with 32 Bi-LSTM units, a dropout rate 0.5, and L2 regularization, which was evaluated using Stratified K-Fold cross-validation. Experimental results demonstrate that the hybrid model outperforms conventional deep learning approaches, achieving 95.24% accuracy, 95.09% precision, 95.15% recall, and 95.99% F1 score. These findings highlight the effectiveness of hybrid architectures in sentiment analysis for low-resource languages. Future work may explore larger datasets or transfer learning to enhance generalizability.

Sentiment analysis is a pivotal tool in understanding public opinion, consumer behavior, and social trends, underpinning applications ranging from market research to political analysis. However, existing sentiment analysis models frequently encounter challenges related to linguistic diversity, model generalizability, explainability, and limited availability of labeled datasets. To address these shortcomings, we propose the Transformer and Attention-based Bidirectional LSTM for Sentiment Analysis (TRABSA) model, a novel hybrid sentiment analysis framework that integrates transformer-based architecture, attention mechanism, and recurrent neural networks like BiLSTM. The TRABSA model leverages the powerful RoBERTa-based transformer model for initial feature extraction, capturing complex linguistic nuances from a vast corpus of tweets. This is followed by an attention mechanism that highlights the most informative parts of the text, enhancing the model’s focus on critical sentiment-bearing elements. Finally, the BiLSTM networks process these refined features, capturing temporal dependencies and improving the overall sentiment classification into positive, neutral, and negative classes. Leveraging the latest RoBERTa-based transformer model trained on a vast corpus of 124M tweets, our research bridges existing gaps in sentiment analysis benchmarks, ensuring state-of-the-art accuracy and relevance. Furthermore, we contribute to data diversity by augmenting existing datasets with 411,885 tweets from 32 English-speaking countries and 7,500 tweets from various US states. This study also compares six word-embedding techniques, identifying the most robust preprocessing and embedding methodologies crucial for accurate sentiment analysis and model performance. We meticulously label tweets into positive, neutral, and negative classes using three distinct lexicon-based approaches and select the best one, ensuring optimal sentiment analysis outcomes and model efficacy. Here, we demonstrate that the TRABSA model outperforms the current seven traditional machine learning models, four stacking models, and four hybrid deep learning models, yielding notable gain in accuracy (94%) and effectiveness with a macro average precision of 94%, recall of 93%, and F1-score of 94%. Our further evaluation involves two extended and four external datasets, demonstrating the model’s consistent superiority, robustness, and generalizability across diverse contexts and datasets. Finally, by conducting a thorough study with SHAP and LIME explainable visualization approaches, we offer insights into the interpretability of the TRABSA model, improving comprehension and confidence in the model’s predictions. Our study results make it easier to analyze how citizens respond to resources and events during pandemics since they are integrated into a decision-support system. Applications of this system provide essential assistance for efficient pandemic management, such as resource planning, crowd control, policy formation, vaccination tactics, and quick reaction programs.

This study explores the role of sentiment analysis as a predictive tool for understanding and forecasting product launch success in the digital market. Sentiment analysis involves the classification of consumer sentiment expressed on social media platforms such as Twitter and Instagram, and it can significantly impact businesses by predicting consumer behavior and product performance. The research highlights the relationship between social media sentiment and product success, demonstrating that positive sentiment is strongly correlated with higher sales and consumer engagement, while negative sentiment can lead to declines. Machine learning models, including Support Vector Machines (SVM) and Random Forest, were employed to classify sentiment from large volumes of social media data and correlate it with product performance indicators such as sales volume and consumer interaction. The study found that sentiment analysis models were highly effective in predicting product success, with positive sentiment generally driving product profitability and negative sentiment posing a potential threat to brand reputation. Moreover, the analysis showed that social media sentiment provides real-time insights into consumer perceptions, enabling businesses to quickly adjust marketing strategies and product development plans. These findings underscore the importance of integrating sentiment analysis into product launch evaluations and strategic decision-making. Future research should explore the integration of sentiment analysis with other predictive market models and investigate the effects of fake reviews and post-purchase consumer behaviors on product success.

Sentiment analysis is crucial in understanding and analyzing public opinions, feedback, and social media data. In this study, we propose a modified Bayesian Boosting algorithm with weight-guided optimal feature selection for sentiment analysis. The goal is to improve the accuracy and efficiency of sentiment classification by selecting the most informative features and utilizing an optimized boosting algorithm. The proposed algorithm leverages the strength of the Bayesian framework and boosting techniques to handle the challenges of sentiment analysis effectively. It incorporates a feature selection mechanism that assigns weights to individual features based on their importance in sentiment classification. This weight-guided feature selection approach helps identify the most relevant and discriminative features, enhancing classification performance. The algorithm employs a modified Bayesian boosting algorithm to optimize the boosting process. It dynamically adjusts the weights assigned to weak classifiers during the boosting iterations, emphasizing the misclassified instances and providing more focused learning for accurate sentiment prediction. The proposed algorithm is evaluated on a benchmark sentiment analysis dataset, comparing it with other state-of-the-art algorithms. The results demonstrate that the modified Bayesian Boosting algorithm with weight-guided optimal feature selection achieves superior accuracy, precision, recall, and F1 score. The algorithm shows its effectiveness in capturing the sentiment patterns and improving the overall sentiment classification performance. The results demonstrated that the proposed model had gained an accuracy level of 98.49%, even the Random Forest algorithm gained an accuracy level of 78.6%, and the C4.5 model achieved an accuracy level of 68.58% individually. The proposed modified Bayesian Boosting algorithm with weight-guided optimal feature selection presents a promising approach for sentiment analysis. It effectively addresses the challenges of feature selection and boosting, leading to improved sentiment classification accuracy. The algorithm’s ability to adjust weights and select informative features adaptively enhances its sentiment analysis tasks performance. This research opens up new avenues for advancing sentiment analysis techniques and contributes to developing more accurate and efficient sentiment analysis models.

Sentiment Analysis (SA) is the process of extracting the information from the given text in which the text consists of various emotions such as happy, sad, proud, fear and so on about various events, people, system, and facts. Since the social media is burgeoned with huge amount of data, it is necessary to derive some useful information from the available data. This extracted useful information can be used in product purchase, politics, sports, and more. To be precise, it helps in decision making. For implementing the sentiment analysis, a lot of research work has been carried out which ranges from lexicon based approach to machine learning based approach. In recent days deep learning and neural networks plays a major role in sentiment analysis and it is considered as a state-of-the art method for analysing various languages. Tamil is one of the Indian languages which still requires state-of-art model for sentiment analysis. The language specific features, grammar structure, agglutinative nature of Tamil language poses more challenges. In this paper, we proposed a combined character based Deep Bidirectional long short-term memory neural networks (DBLS TM) to analyse the Tamil tweets.

Sentiment analysis is a cornerstone in many contextual data analyses, from opinion mining to public discussion analysis. Gender bias is one of the well-known issues in sentiment analysis models, which can produce different results for the same text depending on the gender it refers to. This gender bias leads to further bias in other text analyses that use such sentiment analysis models. This study reviews existing solutions to reduce gender bias in sentiment analysis and proposes a new method to address this issue. The proposed method offers more practical flexibility as it focuses on sentiment estimation rather than model training. Furthermore, it provides a quantitative measure to investigate the gender bias in sentiment analysis results. The performance of the proposed method across five sentiment analysis models is presented using texts containing gender-specific words. The proposed method is applied to a set of social media posts related to Morocco’s 2023 earthquake to estimate the gender-unbiased sentiment of the posts and evaluate the gender-unbiasedness of five different sentiment analysis models in this context. The result shows that, although the sentiments estimated with different models are very different, the gender bias in none of the models is drastically large.

Sentiment Analysis (SA) is one of the subfields in Natural Language Processing (NLP) which focuses on identification and extraction of opinions that exist in the text provided across reviews, social media, blogs, news, and so on. SA has the ability to handle the drastically-increasing unstructured text by transforming them into structured data with the help of NLP and open source tools. The current research work designs a novel Modified Red Deer Algorithm (MRDA) Extreme Learning Machine Sparse Autoencoder (ELMSAE) model for SA and classification. The proposed MRDA-ELMSAE technique initially performs preprocessing to transform the data into a compatible format. Moreover, TF-IDF vectorizer is employed in the extraction of features while ELMSAE model is applied in the classification of sentiments. Furthermore, optimal parameter tuning is done for ELMSAE model using MRDA technique. A wide range of simulation analyses was carried out and results from comparative analysis establish the enhanced efficiency of MRDA-ELMSAE technique against other recent techniques.

Sentiments are feelings, emotions likes and dislikes or opinions which can be articulate through text, images or videos. Sentiment Analysis on web data is now becoming a budding research area of social analytics. Users express their sentiments on the web by exchanging texts and uploading images through a variety of social media like Instagram, Facebook, Twitter, WhatsApp etc. A lot of research work has been done for sentiment analysis of textual data; there has been limited work that focuses on analyzing the sentiment of image data. Image sentiment concepts are ANPs i.e. Adjective Noun Pairs automatically discovered tags of web images which are useful for detecting the emotions or sentiments conveyed by the image. The major challenge is to predict or identify the sentiments of unlabelled images. To overcome this challenge deep learning techniques are used for sentiment analysis, as deep learning models have the capability for effectively learning the image behavior or polarity. Image recognition, image prediction, image sentiment analysis, and image classification are some of the fields where Neural Network (NN) has performed well implying significant performance of deep learning in image sentiment analysis. This paper focuses on some of the noteworthy models of deep learning as Deep Neural Network (DNN), Convolutional Neural Network (CNN), Region-based CNN (R-CNN) and Fast R-CNN along with the suitability of their applications in image sentiment analysis and their limitations. The study also discusses the challenges and perspectives of this rising field.

Aim/Purpose: The need for this paper stems from the challenge of efficiently analyzing large volumes of customer reviews in the hotel industry, which is growing and complex due to the widespread use of digital platforms. With consumers increasingly sharing feedback across various social media applications, manual processing becomes impractical, necessitating machine learning algorithms for accurate sentiment analysis and prediction. Background: This paper addresses the problem by applying machine learning algorithms, specifically fine-tuned bidirectional GRU with FastText embedding, to perform location-based sentiment analysis and prediction of customer reviews in the hotel industry, providing an efficient solution to process large-scale unstructured data and extract valuable insights for improving customer experience. Methodology: This study employs machine learning techniques, including Random Forest (RF), Support Vector Machine (SVM), Bidirectional Long-Short Term Memory (BiLSTM), and Bidirectional Gated Recurrent Unit (GRU), to perform sentiment analysis on customer reviews in the hotel industry. The paper focuses on using a fine-tuned bidirectional GRU model with FastText embedding for location-based sentiment analysis and prediction. The research sample consists of a large dataset of customer reviews, which are processed and analyzed to predict sentiment and evaluate model performance, achieving an overall score of 84.31%. Contribution: This paper contributes to the body of knowledge by demonstrating the effective application of advanced machine learning algorithms, particularly a fine-tuned bidirectional GRU with FastText embedding, for sentiment analysis in the hotel industry. It offers a unique approach to location-based sentiment analysis, allowing for a deeper understanding of regional variations in customer perceptions. The study also provides insights into the comparative effectiveness of different machine learning models for sentiment classification and prediction by comparing multiple algorithms. This work enhances the existing methodologies for processing large-scale, unstructured data and highlights the potential of sentiment analysis to drive data-informed strategies in customer-centric industries. Findings: The study found that the fine-tuned bidirectional GRU model with FastText embedding achieved an accuracy of 84.31%, outperforming other algorithms in sentiment classification. It highlighted the effectiveness of location-based sentiment analysis for understanding regional variations in customer perceptions. The approach also demonstrated strong predictive capabilities, aiding data-driven decision-making in the hospitality sector. Recommendations for Practitioners: Practitioners in customer-focused sectors, particularly hospitality, should consider using advanced models like the fine-tuned bidirectional GRU with FastText embedding for precise sentiment analysis. This approach helps capture regional customer preferences, allowing for tailored services. Integrating sentiment insights with real-time data can further enhance responsiveness to customer needs, supporting data-driven improvements in customer satisfaction. Recommendation for Researchers: Researchers should investigate more advanced models and varied datasets to enhance sentiment analysis accuracy and applicability. Extending sentiment analysis to fields like healthcare and finance could offer broader insights into consumer behavior. Additionally, integrating sentiment analysis with real-time data sources, such as social media, may yield more dynamic predictive models and reveal key regional trends. Impact on Society: The findings of this paper have significant implications for industries, particularly in the hospitality sector, as they provide an effective method for analysing customer sentiment at a granular level. The ability to assess customer opinions through sentiment analysis can help businesses improve customer service, tailor their offerings to meet customer needs, and enhance the overall customer experience. Additionally, location-based sentiment analysis can enable businesses to understand regional differences, allowing for more personalized marketing strategies and better resource allocation. Future Research: Future research could focus on improving the accuracy and efficiency of sentiment analysis models by incorporating more advanced deep learning techniques and larger, more diverse datasets. Exploring the application of sentiment analysis in other sectors, such as healthcare, education, and finance, could provide broader insights into customer perception. Additionally, integrating sentiment analysis with other data sources, such as social media and customer feedback, may lead to more comprehensive and real-time prediction models. Further directions include multilingual sentiment analysis, the use of transformer-based models, and deployment in resource-constrained environments.

With the rapid development of the Internet, the number of social media and e-commerce platforms increased dramatically. Users from all over world share their comments and sentiments on the Internet become a new tradition. Applying natural language processing technology to analyze the text on the Internet for mining the emotional tendencies has become the main way in the social public opinion monitoring and the after-sale feedback of manufactory. Thus, the study on text sentiment analysis has shown important social significance and commercial value. Sentiment analysis is a hot research topic in the field of natural language processing and data mining in recent ten years. The paper starts with the topic of "Sentiment Analysis using a CNN-BiLSTM deep model based on attention mechanism classification". First, it conducts an in-depth investigation on the current research status and commonly used algorithms at home and abroad, and briefly introduces and analyzes the current mainstream sentiment analysis methods. As a direction of machine learning, deep learning has become a hot research topic in emotion classification in the field of natural language processing. This paper uses deep learning models to study the sentiment classification problem of short and long text sentiment classification tasks. The main research contents are as follows. Firstly, Traditional neural network based short text classification algorithms for sentiment classification is easy to find the errors. The feature dimension is too high, and the feature information of the pool layer is lost, which leads to the loss of the details of the emotion vocabulary. To solve this problem, the Word Vector Model (Word2vec), Bidirectional Long-term and Short-term Memory networks (BiLSTM) and convolutional neural network (CNN) are combined in Quora dataset. The experiment shows that the accuracy of CNN-BiLSTM model associated with Word2vec word embedding achieved 91.48%. This proves that the hybrid network model performs better than the single structure neural network in short text. Convolutional neural network (CNN) models use convolutional layers and maximum pooling or max-overtime pooling layers to extract higher-level features, while LSTM models can capture long- term dependencies between words hence are better used for text classification. However, even with the hybrid approach that leverages the powers of these two deep-learning models, the number of features to remember for classification remains huge, hence hindering the training process. Secondly, we propose an attention based CNN-BiLSTM hybrid model that capitalize on the advantages of LSTM and CNN with an additional attention mechanism in IMDB movie reviews dataset. In the experiment, under the control of single variable of Data volume and Epoch, the proposed hybrid model was compared with the results of various indicators including recall, precision, F1 score and accuracy of CNN, LSTM and CNN-LSTM in long text. When the data size was 13 k, the proposed model had the highest accuracy at 0.908, and the F1 score also showed the highest performance at 0.883. When the epoch value for obtaining the optimal accuracy of each model was 10 for CNN, 14 for LSTM, 5 for MLP and 15 epochs for CNN-LSTM, which took the longest learning time. The F1 score also showed the best performance of the proposed model at 0.906, and accuracy of the proposed model was the highest at 0.929. Finally, the experimental results show that the bidirectional long- and short-term memory convolutional neural network (BiLSTM-CNN) model based on attention mechanism can effectively improve the performance of sentiment classification of data sets when processing long-text sentiment classification tasks. Keywords: sentiment analysis, CNN, BiLSTM, attention mechanism, text classification

Deep learning is a new era of machine learning techniques that essentially imitate the structure and function of the human brain. It is a development of deeper Artificial Neural Network (ANN) that uses more than one hidden layer. Deep Learning Neural Network has a great ability on recognizing patterns from various data types such as picture, audio, text, and many more. In this paper, the authors tries to measure that algorithm’s ability by applying it into the text classification. The classification task herein is done by considering the content of sentiment in a text which is also called as sentiment analysis. By using several combinations of text preprocessing and feature extraction techniques, we aim to compare the precise modelling results of Deep Learning Neural Network with the other two commonly used algorithms, the Naϊve Bayes and Support Vector Machine (SVM). This algorithm comparison uses Indonesian text data with balanced and unbalanced sentiment composition. Based on the experimental simulation, Deep Learning Neural Network clearly outperforms the Naϊve Bayes and SVM and offers a better F-1 Score while for the best feature extraction technique which improves that modelling result is Bigram.

Social media platforms have seen a significant increase in the number of users and content in recent years. Owing to the increased usage of these platforms, incidents of teasing, provocation—both positive and negative—and harassment, and community attacks have increased tremendously. There is an urgent need to automatically identify such content or tweets that can hamper the well-being of an individual or society. Analyzing social media messages from Twitter and Facebook has become the focus of sentiment analysis in recent years, which formerly focused on online product evaluations. Sentiment analysis is used in a wide range of fields besides product reviews, including harassment, stock markets, elections, disasters, and software engineering. After the tweets have been preprocessed, the extracted features are categorized using classifiers like decision trees, logistic regression, multinomial nave Bayes, support vector machines, random forests, and Bernoulli nave Bayes, as well as deep learning techniques like recurrent neural network (RNN) models, long short-term memory (LSTM) models, bidirectional long short-term memory (BiLSTM) models, and convolutional neural network (CNN) model for sentiment analysis. In this paper, different techniques are compared to classify Twitter tweets into three categories: “positive,” “negative,” and “neutral.” We proposed a novel data-balancing technique for text classification. A text classification technique is proposed for analyzing textual data using the Generative Pretrained Transformer model owing to its contextual understanding and more realistic data generation capability. Comparative analysis of different Machine learning and Deep learning models are performed with and without data balancing. The experiments show that the accuracy and F1-measure of the Twitter sentiment classification classifier are improved. The proposed ensemble has outperformed and achieved an accuracy of 90%, precision of 88%, and 81% F1 score.