Sentiment Analysis Methods Research Articles

Dose My Opinion Count? A CNN-LSTM Approach for Sentiment Analysis of Indian General Elections

Sentiment analysis on social media platforms is a critical area of research for understanding public opinion, particularly during significant events like elections. This paper presents a sentiment analysis study using datasets from Reddit. Traditional sentiment analysis methods, while useful, often struggle with the informal and diverse nature of social media language, including sarcasm and contextual nuances. To address these challenges, we explore the use of a hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model. This CNN-LSTM approach leverages the strengths of both architectures, capturing local features and contextual dependencies in the text. Our study aims to enhance sentiment classification accuracy and demonstrate the efficacy of the CNN-LSTM model in processing large-scale social media datasets.

Comprehensive Exploration of Machine and Deep Learning Classification Methods for Aspect-Based Sentiment Analysis with Latent Dirichlet Allocation Topic Modeling

This research explores the effectiveness of machine learning (ML) and deep learning (DL) classification methods in Aspect-Based Sentiment Analysis (ABSA) on product reviews, incorporating Latent Dirichlet Allocation (LDA) for topic modeling. Using the Amazon reviews dataset, this research tests models such as Naive Bayes (NB), Support Vector Machines (SVM), Random Forest (RF), Long Short-Term Memory (LSTM), and Gated Recurrent Units(GRU). Important aspects such as the product's quality, practicality, and reliability are discussed. The results show that the RF and DL models provide competitive performance, with the RF achieving an accuracy of up to 94.50% and an F1 score of 95.45% for the reliability aspect. The study's conclusions emphasize the importance of selecting an appropriate model based on specifications and data requirements for ABSA, as well as recognizing the need to strike a balance between accuracy and computational efficiency.

Analisis Opini Publik Terhadap Pemilu 2024 Pada Media Sosial X

The 2024 general election in Indonesia has become a global concern, with social media acting as the main platform for political interaction and political communication. This research aims to analyze public opinion regarding the 2024 elections in Indonesia expressed through Social Media X. Using content analysis and sentiment analysis methods, this research explores people's views, concerns and hopes regarding the upcoming elections. The research results show that there is significant polarization among Social Media X users in the election. These findings highlight the important role of social media in shaping public opinion and provide valuable insights for candidates and political parties in developing effective campaign strategies.

COMPARATIVE EFFECTIVENESS OF RULE-BASED AND MACHINE LEARNING METHODS IN SENTIMENT ANALYSIS OF KAZAKH LANGUAGE TEXTS

Sentiment analysis is increasingly pivotal in natural language processing (NLP), crucial for deciphering public opinions across diverse sectors. This research conducts a comparative examination of rule-based and machine learning (ML) methods in sentiment analysis, specifically targeting the Kazakh language. Given the Kazakh language's limited exposure in computational linguistics, the study meticulously evaluates datasets from news articles, literature, and Amazon product reviews, aiming to compare the efficiency, adaptability, and overall performance of these distinct approaches. Employing a detailed set of evaluation metrics such as accuracy, precision, recall, and computational efficiency, the study provides a comprehensive analysis of the strengths and limitations of rule-based techniques versus ML models like Logistic Regression, Multinomial Naive Bayes, Decision Trees, Random Forest, and XGBoost. The findings suggest rule-based methods excel in identifying nuanced emotional expressions within literary texts, while ML models demonstrate superior adaptability and robustness, particularly effective in handling the linguistic variations found in news and reviews. Despite the strengths identified, the study also reveals significant limitations of the rule-based approach, especially in broader contexts beyond literary analysis. This highlights an imperative for future research to integrate sentiment dictionaries or domain-specific lexicons that cater to a wider array of linguistic styles, potentially enhancing sentiment analysis tools' applicability in Kazakh and similar less-studied languages. This investigation contributes significantly to the sentiment analysis discourse, offering invaluable insights for both researchers and practitioners by elucidating the complexities of applying NLP technologies across diverse linguistic landscapes, thus advancing the understanding and methodologies of sentiment analysis in the Kazakh language context.

Microblog sentiment analysis method using BTCBMA model in Spark big data environment

Abstract Microblogs are currently one of the most well-liked social platforms in China, and sentiment analysis of microblog texts can help further analyze the realization of their media value; however, the current task of sentiment analysis based on microblog information suffers from low accuracy due to the large size and high redundancy of microblog data, a microblog sentiment analysis method using Bidirectional Encoder Representation from Transformers (BERT)–Text Convolutional Neural Network (TextCNN)–Bidirectional Gate Recurrent Unit (BiGRU)–Multihead-Attention model in Spark big data environment is proposed. First, the Chinese pre-trained language model BERT is used to convert the input data into dynamic character-level word vectors; then, TextCNN is used to effectively obtain local features such as keywords and pool the filtered features; then, BiGRU is introduced to quickly capture more comprehensive semantic information; finally, a multi-headed attention mechanism is implemented to emphasize the most significant features in order to accomplish the sentiment classification of microblog information task precisely. By comparing the existing advanced models, the proposed model demonstrates an improvement of at least 4.99% and 0.05 in accuracy and F1-score evaluation indexes, respectively. This enhancement significantly enhances the accuracy of microblog sentiment analysis tasks and aids pertinent authorities in comprehending the inclination of individual’s attitude toward hot topics. Furthermore, it facilitates a prompt prediction of topic trends, enabling them to guide public opinion accordingly.

A Multimodal Sentiment Analysis Approach Based on a Joint Chained Interactive Attention Mechanism

The objective of multimodal sentiment analysis is to extract and integrate feature information from text, image, and audio data accurately, in order to identify the emotional state of the speaker. While multimodal fusion schemes have made some progress in this research field, previous studies still lack adequate approaches for handling inter-modal information consistency and the fusion of different categorical features within a single modality. This study aims to effectively extract sentiment coherence information among video, audio, and text and consequently proposes a multimodal sentiment analysis method named joint chain interactive attention (VAE-JCIA, Video Audio Essay–Joint Chain Interactive Attention). In this approach, a 3D CNN is employed for extracting facial features from video, a Conformer is employed for extracting audio features, and a Funnel-Transformer is employed for extracting text features. Furthermore, the joint attention mechanism is utilized to identify key regions where sentiment information remains consistent across video, audio, and text. This process acquires reinforcing features that encapsulate information regarding consistency among the other two modalities. Inter-modal feature interactions are addressed through chained interactive attention, and multimodal feature fusion is employed to efficiently perform emotion classification. The method is experimentally validated on the CMU-MOSEI dataset and the IEMOCAP dataset. The experimental results demonstrate that the proposed method significantly enhances the performance of the multimodal sentiment analysis model.

A Sentiment Analysis Method for Big Social Online Multimodal Comments Based on Pre-trained Models

A Sentiment Analysis Method for Big Social Online Multimodal Comments Based on Pre-trained Models

Comparison of Sentiment Analysis Methods Used to Investigate the Quality of Teaching Aids Based on Virtual Simulators of Embedded Systems

Virtual simulators of embedded systems and analyses of student surveys regarding their use at the early stage of the process of learning embedded systems, are presented in this article. The questionnaires were prepared in the Polish language, and the answers were automatically translated into English using two publicly available translators. The results of users’ experiences and feelings related to the use of virtual simulators are shown on the basis of detected sentiment using three chosen analysis methods: the Flair NLP library, the Pattern library, and the BERT NLP model. The results of the selected sentiment detection methods were compared and related to users reference answers, which gives information about the methods quality of the methods and their possible use in the automated review analysis process. This paper comprises detailed sentiment analysis results with a broader statistical approach for each question. Based on the students feedback and sentiment analysis, a new version of the TMSLAB v.2 virtual simulator was created.

Hybrid Framework Integrating Lexicon and Learning Methods for Enhancing Sentiment Analysis Based on Patients' Tweets on Medicines

- Sentiment analysis is the act of locating and categorizing the emotions conveyed in text data using text analysis tools. Previous studies have demonstrated the great potential for sentiment analysis of medicine reviews to provide useful data that will aid healthcare professionals and businesses in assessing the safety of pharmaceuticals after they have been sold. These details protect patients and strengthen their confidence in healthcare professionals. Existing frameworks for opinion investigation within the restorative field either take two types of methods one is lexicon methods and the other one is machine learning models. Learning-based approaches need annotated data, whereas lexicon-based approaches are domain-specific and have a smaller range of applications. To improve outcomes, this study employs a hybrid methodology that fusion machine and deep learning models with lexicon techniques. The reviews are annotated using all-purpose emotion lexicons like TextBlob and SenticNet. To extract meaningful features, we are using feature engineering methods TF and TF- IDF. Last but not least, classification tasks are carried out using learning models such as machine learning models and deep learning models for biomedical text. The execution of the prospective combined technique is appraised using performance metrics. According to experimental findings, combining lexicon- and learning-based techniques yields superior outcomes over using them alone. Additionally, TextBlob has demonstrated impressive results, providing an accuracy of 97% with LSTM-CNN and the Bio Bert model when used to a dataset of medication reviews, as well as 95% accuracy when used with TF and the logistic regression model. Additionally, TextBlob provides an accuracy of 94% when combined with TF and LSTM, and provides an accuracy of 97% when using with Bio Bert Model on a dataset including tweets.

Sentimental analysis and prediction of socioeconomic disasters tweets by ML and regular expression

Sentiment analysis is a method of analyzing emotions and using text analysis techniques with natural language processing methods. Sentiment analysis uses data from various sources to identify the user’s attitude through different aspects. It is widely used for extracting opinions and recognizing sentiments, which helps Business organizations understand the user’s needs. This paper proposes a simple but compelling sentiment analysis method, showing the combined scores based on positive and negative words. Then, the tweets are categorized as Neutral, Negative, or Positive according to the scores. Sentiment analysis and opinion mining have grown significantly in the last decade. Different studies in this domain try to determine people’s feelings, opinions, and emotions about something or someone. The main objective of this analysis is to determine the sentiment of the review using a machine learning model and then compare the result with the manual review of the data. This would allow researchers to represent and analyze opinions objectively across different domains. A hybrid method that combines a supervised machine learning algorithm with natural language processing techniques is suggested for review analysis. This project aims to find the best model to predict the sentiment of the tweets on airlines. During the research process and considering various methods and variables that should be considered, we found that methods like naïve Bayes and random forest were not fully explored. The proposed system improves an effective and more feasible method for sentimental analysis using machine learning, multinomialNB, linear regression, and regular expression.

Service quality improvement strategies of online car-hailing based on SPC-PCN method

Online car-hailing service is practicing the concept of sharing, bringing new energy and convenience to the transportation market. However, with its rapid development, service issues have become increasingly prominent, and improving service quality is the key to the sustainable growth of the online ride-hailing industry. Microblog, as a major online platform for gathering public views, have become one of the significant media for publishing and disseminating information nowadays, which also contains numerous views of online taxi users on online taxi services. Aiming at the deficiency of online car-hailing service under microblog public opinion, this paper integrates Service Profit Chain (SPC) and Process Chain Network (PCN) from the perspective of service management. Firstly, a crawler program is used to collect the Internet Word of Mouth data related to online taxi. Then, a BERT text classification model is constructed to roughly classify the data, and the accuracy of this model is tested to 94.54 %. Afterwards, layer segmentation is implemented based on SPC and CatBoost. Besides, a series of problems in the current online taxi service, such as poor attitude of drivers, unsafe driving, and inefficient communication with the customer service staff, have been identified by using sentiment analysis and frequent mining methods. Finally, PCN analysis is used to optimize the online taxi service, proposing strategies such as setting a reservation reminder function, building a dynamic feedback mechanism, and establishing a contact point for lost and found items. The new SPC-PCN online taxi service system constructed in this paper is vital for service quality optimization, and can also promote the sustainable growth of the online taxi market.

Study of the Problems of Determining Public Opinion of the Israeli-Palestinian War in Social Networks

This paper explores the use of neural networks for determining public opinion on the Israeli-Palestinian conflict. In today's world, where numerous user opinions on various topics are posted daily on social media platforms, it is important to be able to analyze this public sentiment. The method of sentiment analysis and weighting of votes in comments is proposed, allowing for the identification of overall audience moods and assessment of their reactions to different events or content. The research results indicate the potential usefulness of these methods for marketing strategies, reputation management, and decision-making in various fields. However, there are challenges associated with data verification and consideration of manipulative interventions in public opinion. This work aims to review the current state of research in this area and identify prospects for further development. In today's digital world, analyzing public opinion on social media is becoming increasingly important. Understanding how users react to various topics and events is crucial for various fields, from marketing to politics. This paper discusses methods of sentiment analysis and vote weighting in comments using the example of analyzing public opinion on the Israeli-Palestinian conflict on the Reddit social network. The use of natural language processing tools, machine learning, and data visualization allows for valuable insights into users' emotional attitudes toward discussed topics and trends in public opinion. However, to achieve the best results, it is necessary to consider not only textual content but also social interaction and user status. The development of effective social media analysis tools opens the way to a deeper understanding of public opinion and making informed decisions in various areas of social life.

Feelflow: Delving into Emotional Depths with Generative AI

Abstract: Significant progress has been made in sentiment analysis in recent decades, with a focus on textual data. Nonetheless, the scientific community has not done a great deal to investigate sentiment analysis in the context of audio. By applying a novel method of sentiment analysis to voice transcripts and concentrating on the subtle interpretation of emotions sent by distinct speakers during conversations, this study seeks to close this gap in knowledge. The main goal of this suggested research paper is to create an advanced sentiment analysis system that can communicate with several users in a seamless manner while identifying and assessing the emotional content that each user is conveying through their audio inputs. Advanced approaches like Recurrent Neural Networks (RNN), Long Short Term Memory(LSTM), Teacher Forcing , Encoder- Decoder Model, Tokenization, Gated Recurrent Units (GRU),(Bidirectional Encoder Representations from Transformers),Gradient Boosting Machine.

A CNN-BiLSTM algorithm for Weibo emotion classification with attention mechanism

Weibo short text information contains a large amount of network language, emoticons, etc., and due to the long-time span of the content, the emotions of the posts posted by people often change due to time or the occurrence of certain special events. Therefore, traditional sentiment analysis methods are not suitable for this task. This article proposes a CNNs-Bi LSTM sentiment analysis method that integrates attention mechanism. It combines convolutional neural networks and bidirectional short-term memory networks to obtain keyword information in text through attention mechanism, efficiently and accurately realizing data temporal and semantic information mining. Through experimental verification using Weibo public opinion data, the results show that this method achieves higher accuracy compared to other benchmark models and can fully utilize multidimensional matrices to capture rich text features, with certain advantages.

Deep learning-based method for sentiment analysis for patients' drug reviews.

This article explores the application of deep learning techniques for sentiment analysis of patients' drug reviews. The main focus is to evaluate the effectiveness of bidirectional long-short-term memory (LSTM) and a hybrid model (bidirectional LSTM-CNN) for sentiment classification based on the entire review text, medical conditions, and rating scores. This study also investigates the impact of using GloVe word embeddings on the model's performance. Two different drug review datasets were used to train and test the models. The proposed methodology involves the implementation and evaluation of both deep learning models with the GloVe word embeddings for sentiment analysis of drug reviews. The experimental results indicate that Model A (Bi-LSTM-CNN) achieved an accuracy of 96% and Model B (Bi-LSTM-CNN) performs consistently at 87% for accuracy. Notably, the incorporation of GloVe word representations improves the overall performance of the models, as supported by Cohen's Kappa coefficient, indicating a high level of agreement. These findings showed the efficacy of deep learning-based approaches, particularly bidirectional LSTM and bidirectional LSTM-CNN, for sentiment analysis of patients' drug reviews.

Application of the Naive Bayes Algorithm in Twitter Sentiment Analysis of 2024 Vice Presidential Candidate Gibran Rakabuming Raka using Rapidminer

In the current era of digital democracy, social media sentiment analysis has become a relevant method for understanding public views of political figures. As one of the leading social media platforms, Twitter provides a public space for sharing opinions and expressions regarding political issues. This research aims to classify and measure the accuracy of people's responses to the positive and negative sides. Sentiment analysis was carried out using the Naïve Bayes method using a dataset of 3223 tweets. The final results of this research show that implementing the Naïve Bayes Method in sentiment analysis regarding political dynasty polemics, especially regarding the 2024 Cawapres Gibran Rakabuming Raka, provides an accuracy value of 82.19%. Of the 1696 negative and 112 positive sentiments predicted, there were 462 harmful and 953 positive predicted data. These results indicate that most public responses tend to be detrimental to the Constitutional Court's (MK) decision, which grants political legitimacy to Gibran Rakabuming Raka as the 2024 vice-presidential candidate.

Sentiment Analysis Method of Epidemic-related Microblog Based on Hesitation Theory

The COVID-19 pandemic in 2020 brought an unprecedented global crisis. After two years of control efforts, life gradually returned to the pre-pandemic state, but localized outbreaks continued to occur. Toward the end of 2022, COVID-19 resurged in China, leading to another disruption of people’s lives and work. Many pieces of information on social media reflected people’s views and emotions toward the second outbreak, which showed distinct differences compared to the first outbreak in 2020. To explore people’s emotional attitudes toward the pandemic at different stages and the underlying reasons, this study collected microblog data from November 2022 to January 2023 and from January to June 2020, encompassing Chinese reactions to the COVID-19 pandemic. Based on hesitancy and the Fuzzy Intuition theory, we proposed a hypothesis: hesitancy can be integrated into machine learning models to select suitable corpora for training, which not only improves accuracy but also enhances model efficiency. Based on this hypothesis, we designed a hesitancy-integrated model. The experimental results demonstrated the model’s positive performance on a self-constructed database. By applying this model to analyze people’s attitudes toward the pandemic, we obtained their sentiments in different months. We found that the most negative emotions appeared at the beginning of the pandemic, followed by emotional fluctuations influenced by social events, ultimately showing an overall positive trend. Combining word cloud techniques and the Latent Dirichlet Allocation (LDA) model effectively helped explore the reasons behind the changes in pandemic attitude.

An Enhanced Ensemble Learning Method for Sentiment Analysis based on Q-learning

An Enhanced Ensemble Learning Method for Sentiment Analysis based on Q-learning

Unendorsed Machine Approaches of Learning in the field of Analysis of Sentiment over Unstructured Bigdata

Terabytes of data are created every day by modern information systems and new technology. It takes a lot of work across many levels to get useful information out of these massive datasets for decision-making. Social media, as well as other Internet-based applications, have been a major source of big data in recent years. When it comes to social media, Twitter is a household name across the globe. Unstructured data can be found in abundance on Twitter and other social media platforms. An innovative way to examine the emotions expressed on social media is through the use of sentiment analysis clustering techniques. Unsupervised machine learning methods for sentiment analysis of unstructured bigdata is discussed here. The Meanshift clustering method and the Bisecting K means algorithm is then compared using the metrics of precision, recall, and accuracy, among other things the f1 score. Python programming and Pyspark are employed for data analysis.

Sentiment Analysis on The Place of Interest in Malaysia

This study focuses on utilizing machine learning methods for sentiment analysis to identify positive and negative comments regarding Malaysian Places of Interest (PoI). The data was collected from Twitter using social media monitoring software and organized into tables. Pre-processing techniques and Natural Language Processing (NLP) methods were applied to handle missing values and prepare the text data for analysis. The dataset was then split into training and testing sets, and three supervised learning algorithms which are Support Vector Machine, Random Forest, and Naive Bayes were employed to evaluate the sentiment analysis models. The performance of each model was compared, and it was found that Support Vector Machine achieved the highest accuracy, recall score, F1 score, and precision score. This study demonstrates the potential to extend sentiment analysis to analyze sentiments expressed in texts written in the Malay language by utilizing the Malaya corpus. Additionally, visual dashboards can be created to present the findings and provide recommendations based on the insights gathered from the sentiment analysis of PoI feedback.