Sentiment Analysis Approach Research Articles

Optimization of Marketing Strategy for State-Owned Energy Products through Sentiment Analysis with VADER and LSTM on Social Media

Sentiment analysis is also known as opinion mining. It has an important role in natural language processing and data mining. It involves extracting and analyzing subjective information from textual data to determine the sentiment. With the advancement of technology, it is increasingly important to understand users' opinions and sentiments regarding a particular product, service or issue. This research aims to optimize the marketing strategy of energy products in SOE subsidiaries through sentiment analysis using the VADER and LSTM methods on social media.The researcher analyzed data from twitter to identify consumer sentiment towards energy products. The results of the analysis show that the combination of VADER and LSTM is effective in identifying the nuances of sentiment, with the accuracy of sentiment classification reaching 85%. Based on the analysis, the researchers developed recommendations for marketing strategies that are more focused and responsive to public perception. This study also highlights the importance of two-way interaction between companies and consumers as part of an effective marketing strategy. The findings of this study are expected to be used as a reference by state-owned companies in the energy sector to improve the effectiveness of their marketing strategies through a sentiment analysis approach on social media.

Evaluating Tourist Dissatisfaction with Aspect-Based Sentiment Analysis Using Social Media Data

Tourism satisfaction is essential for encouraging tourists to stay longer, spend more and return. However, visitor dissatisfaction can also prove useful for understanding any shortcomings of a tourist destination, and Twitter, Instagram and TripAdvisor reviews might be able to provide an insight into tourist perceptions and experiences. This study examines the major causes of tourist dissatisfaction with a tourism destination using an aspect-based sentiment analysis approach to understand the key points of negative tweets, posts or reviews. We examined 19,340 tweets, 7,712 Instagram posts and 25,483 reviews about Granada in Spain in order to evaluate the negative user's perceptions, discover management-related problems and provide feedback to destination management organizations to enable them to improve their services and operations. Our work contributes to computational methods to address tourism (dis)satisfaction with a process to identify the most important entities (places), an algorithm to identify aspects and opinions, and the use of word-trees to show the most important aspect-opinion tuples. In practical terms, we provide to tourism industry professionals and managers, as well as travelers, with methods to identify the reasons for tourist dissatisfaction from available social media data, in such a way that managerial strategies or travel plans can be improved.

Insights of learning approach towards determination of potentially objectional communication in social networking

<p><span lang="EN-US">Over the last decade, sentiment analysis has evolved significantly towards extracting the contextual knowledge associated with the communication exchanged in social networks. Irrespective of various approaches to natural language processing and constantly evolving machine learning, sentiment analysis has inherent shortcomings, which further act as an obstacle to determining hateful and offensive speech exchanged in social networks. Therefore, this paper offers a compact yet granular insight into the effectiveness of existing sentiment analysis approaches used distinctly for determining hateful and offensive speech with particular emphasis on machine learning-based methodologies. The paper further contributes towards research trend analysis followed by distinct highlights of the research gap. The paper offers a learning outcome that significantly benefits future researchers investigating the same field.</span></p>

A Comparative Study of Online Consumer Reviews Among Mainstream and Niche Products

The purpose of the paper is to understand the variations in the online consumer reviews among product categories across geographies and cultures. We consider the cases of mainstream products and their lesser-known niche counterparts providing similar utilities across emerging and developed markets. We find reviews vary in terms of characteristics and sentiments across the two categories and markets. The reviews for the niches contain in-depth discussions on some topics. We apply mixed method approach of quantitative analysis, topic modeling, sentiment analysis, and network analysis to arrive at the conclusion. The quantitative analyses confirm the variations, while the qualitative approach enables us to create a typology/ontology for the reviews. Based on the observations, we could successfully map various motives and cultural dimensions related to online reviews to our hierarchical typology. Managers may find our study interesting to boost their online review strategy according to the product category in a specific geography.

PUBLIC STIGMA ABOUT POLYGAMY BASED ON ISLAMIC-MUHAMMADIYAH VIEWS USING SENTIMENT ANALYSIS APPROACH

Social media is very important to control the development of issues that occur today. With social shifts and changing societal values, polygamy has become a complex issue and attracts the attention of many people around the world discussed through social media platforms. This research contributes to the field by applying a sentiment analysis approach to automatically detect and analyze public sentiment regarding polygamiy content on Twitter, particularly in the context of Islamic-Muhammadiyah views. This study used decision tree classification methods, support vector machines, and random forests with the best analysis accuracy obtained at SVM 77.4%. Furthermore, the results of the sentiment class obtained were analyzed according to the views of Muhammadiyah. The results obtained in the analysis 77% commented negatively and 23% commented positively. In addition, this research can be used as a reference for future research on sentiment analysis cases to training and testing classroom models.

Exploring electric vehicle adoption in Indonesia using zero-shot aspect-based sentiment analysis

Transportation emissions significantly contribute to greenhouse gases (GHG) and climate change. Electric vehicles (EVs) offer a promising solution to this problem. Despite the noteworthy adoption of EVs in emerging economies like China and Europe, the pace of EV rollout in Indonesia remains sluggish. Currently, the country's rate of EV adoption is below 0.3 %, leading to stagnation in the Indonesian electric vehicle market. Several factors have impeded the adoption of EVs in Indonesia. Previous studies have investigated consumer acceptance of EV adoption in specific countries using sentiment analysis. Various data analytics and machine learning techniques have been implemented in those studies. However, those studies predominantly rely on the traditional sentiment analysis method, which assigns a single sentiment classification to each document or sentence. On the other hand, this study aims to investigate consumer acceptance of electric vehicle adoption in Indonesia through Twitter conversations. It utilises the Zero-Shot Aspect-Based Sentiment Analysis (ABSA) approach, which can provide a more fine-grained analysis of the aspects discussed and their corresponding sentiments. The findings demonstrate the effectiveness of this approach in identifying discussed aspects within tweets, though its sentiment classification performance is limited. The research also uncovers crucial aspects of electric vehicle adoption and public sentiments on Twitter. These insights could provide valuable guidance to the government and other stakeholders regarding the concerns associated with EV adoption in Indonesia.

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.

A Sentiment Analysis Approach for Exploring Customer Reviews of Online Food Delivery Services: A Greek Case

The unprecedented production and sharing of data, opinions, and comments among people on social media and the Internet in general has highlighted sentiment analysis (SA) as a key machine learning approach in scientific and market research. Sentiment analysis can extract sentiments and opinions from user-generated text, providing useful evidence for new product decision-making and effective customer relationship management. However, there are concerns about existing standard sentiment analysis tools regarding the generation of inaccurate sentiment classification results. The objective of this paper is to determine the efficiency of off-the-shelf sentiment analysis APIs in recognizing low-resource languages, such as Greek. Specifically, we examined whether sentiment analysis performed on 300 online ordering customer reviews using the Meaning Cloud web-based tool produced meaningful results with high accuracy. According to the results of this study, we found low agreement between the web-based and the actual raters in the food delivery services related data. However, the low accuracy of the results highlights the need for specialized sentiment analysis tools capable of recognizing only one low-resource language. Finally, the results highlight the necessity of developing specialized lexicons tailored not only to a specific language but also to a particular field, such as a specific type of restaurant or shop.

The microblog sentiment analysis based on latent dirichlet allocation and deep learning approaches

To study the application of convolutional neural networks (CNN) in microblog sentiment analysis, a microblog sentiment dictionary is established first. Then, latent Dirichlet allocation (LDA) is proposed for user forwarding sentiment analysis. The sentiment analysis models of CNN and long short-term memory network (LSTM) are established. Experiments are conducted to verify the application effect. The main contributions of this work encompass the establishment of a sentiment lexicon for Weibo, the optimization of two sentiment analysis models, namely CNN and LSTM, as well as the comparison and analysis of the performance of three sentiment analysis approaches: CNN, LSTM, and LDA. The research findings indicate that the CNN model achieves a prediction accuracy of 78.6% and an actual output precision of 79.3%, while the LSTM model attains a prediction accuracy of 83.9% and an actual output precision of 84.9%. The three analysis models all have high sentiment analysis accuracy. Among them, LDA analysis model has the advantages of universality and irreplaceable in text classification, while LSTM analysis model has relatively higher accuracy in sentiment analysis of users forwarding microblog. In short, each sentiment analysis model has its own strengths, and reasonable allocation and use can better classify microblog sentiment.

Post-Election Sentiment Analysis 2024 via Twitter (X) Using the Naive Bayes Classifier Algorithm

This study examines sentiment related to the topic of Twitter after the 2024 election, where the topic focused on the 2024 presidential election. Where there are a lot of public opinions and comments after the 2024 presidential election. One of them is the phenomenon when Anies-Muhaimin and Ganjar-Mahfud filed a lawsuit with the Constitutional Court (MK) to appeal over suspicions of fraud over the victory of the elected pair Prabowo-Gibran. By applying the Naïve Bayes Classifier algorithm to analyze public sentiment. Through data crawling, preprocessing, feature extraction, and sentiment classification, the study identified the dominant sentiment and its intensity among social media users. This methodology utilizes quantitative data analysis, using Twitter data linked to specific election-related hashtags. The findings reveal a mix of negative and positive sentiments, reflecting diverse public opinion about election results and related political developments. The accuracy of Naïve Bayes Classifier is highlighted, demonstrating its effectiveness in sentiment classification in the context of social media. This research contributes to understanding public sentiment in the political realm and improving methodological approaches in sentiment analysis using machine learning.

Sentiment analysis of movie review classifications using deep learning approaches

Movie reviews reflect how the public feels about a movie they have watched. However, because many reviews are posted on various websites, it is practically impossible to read each one. Summarizing all movie reviews can help people make informed decisions without reading through all of them. Previous studies have used different machine learning and deep learning techniques for sentiment analysis (SA), but few have combined comprehensive hyperparameter tuning and novel datasets for better performance. This paper presents an SA approach using deep learning models with optimized hyperparameters and a novel Rotten Tomatoes (RT) dataset to help viewers make better movie choices. SA, or opinion mining, is a computational technique to extract and analyze opinions and emotions expressed in text. We explore deep learning models such as Long Short-Term Memory (LSTM), XLNet, Convolutional Neural Networks-LSTM (CNN-LSTM), and Bidirectional Encoder Representations from Transformers (BERT). These models are known for capturing complex language patterns and context from raw text data. XLNet, a pre-trained model, effectively understands context by considering all possible permutations of the input sequence, BERT excels at using bidirectional context to understand text, LSTM retains information about long-term patterns in sequential data, and CNN-LSTM combines local and global context for reliable feature extraction. The RT dataset was pre-processed with data cleaning, spelling correction, lemmatization, and handling of informal words to improve the results. Our experiments show that XLNet performed better than other models on the Rotten Tomatoes dataset. The study demonstrates that SA of movie reviews provides insights into emotions and attitudes, allowing us to estimate a movie’s performance based on its overall sentiment.

Sentiment analysis with machine learning and deep learning: A survey of techniques and applications

Sentiment analysis is the task of automatically identifying the sentiment expressed in text. It has become increasingly important in many applications such as social media monitoring, product reviews analysis, and customer feedback evaluation. With the advent of deep learning techniques, sentiment analysis has seen significant improvements in performance and accuracy. This paper presents a comprehensive survey of machine learning and deep learning methods for sentiment analysis at the document, sentence, and aspect levels. We first provide an overview of traditional machine learning approaches to sentiment analysis and their limitations. We then look into various machine learning and deep learning architectures that have been successfully applied to this task. Additionally, we discuss the challenges of dealing with different data modalities, such as visual and multimodal data, and how both techniques have been adapted to address these challenges. Furthermore, we explore the applications of sentiment analysis in diverse domains, including social media, product reviews, and healthcare. Finally, we highlight the current limitations of deep learning approaches for sentiment analysis and outline potential future research directions. This survey aims to provide researchers and practitioners with a comprehensive understanding of the state-of-the-art deep learning techniques for sentiment analysis and their practical applications.

Analisis Sentimen Rakyat Brunei Terhadap Siaran Halaman ‘Brunei Fm’ Yang Bertajuk ‘Kes Pertama #Covid-19 Di Negara Brunei Darussalam'

The COVID-19 pandemic has significantly impacted global societies, prompting widespread discussions on social media platforms. This study examines the sentiments expressed in Facebook comments by Bruneians regarding the first COVID-19 case in Negara Brunei Darussalam. Despite COVID-19 no longer being a global concern, understanding public sentiment during such crises remains critical for example, for future public health responses. Using a manual sentiment analysis approach, 50 comments from the post’s on ‘Brunei FM’ Facebook page were analyzed to categorize them into positive, negative, and neutral sentiments. The analysis revealed that 54% of the comments were negative, expressing fear, criticism, and blame, while 26% were positive, reflecting hope, support for health measures, and sympathy. The remaining 20% were neutral, focusing on information sharing and advice. This research aims to contribute to the field of manual sentiment analysis by providing a detailed examination of public reactions during a health crisis. The findings underscore the importance of effective communication strategies, community engagement, and responsive policy-making to address public concerns and enhance resilience during future health emergencies.

Optimized Attention-Driven Bidirectional Convolutional Neural Network

This paper devises an optimization-based technique for sentiment analysis using the set of reviews. The major processes involved for the developed sentiment analysis approach are tokenization and sentiment classification. Initially, the input reviews are considered from the database and are subjected to the tokenization process. The tokenization process is performed using Bidirectional Encoder Representations from Transformer (BERT) where the input review data is partitioned into individual words, named as tokens. Finally, sentiment classification is carried out using Attention-based Bidirectional CNN-RNN Deep Model (ABCDM), which is trained by proposed Chimp Deer Hunting Optimization (CDHO) approach. Accordingly, the proposed CDHO algorithm is newly designed by incorporating Chimp Optimization Algorithm (ChOA) and Deer Hunting Optimization Algorithm (DHOA). The proposed CDHO-based ABCDM provided enhanced performance with highest precision of 93.5%, recall of 94.5% and F-measure of 94%.

A Review on Sentiment Analysis Techniques and Approaches

In current scenario persons share their emotions and views on social media in symbolically and text form. These views turn out to be beneficial to organizations, political institutions and the public. It can be difficult to record and understand consumer emotions because reviews on the Internet are available to millions for a product or service. Sentiment examination assumes a significant part in corporate life as they influence their dynamic cycle in different sorts of occasions they face. The essential goal of this survey is to give a total image of sentiment investigation techniques and approaches, its sorts and grouping. This research paper presents an insight of different approaches on sentiment analysis along with demerits.

Optimizing Tourist Satisfaction at Mount Bromo: A Sentiment Analysis Approach

In the era of ever-growing globalization, information technology is the main driver of transformation in various sectors of life. Information technology plays an important role in the progress of the tourism sector by offering easy access to information. The Mount Bromo tourist attraction is one of the popular destinations in Indonesia which attracts many visitors every year. The large number of comments from visitors to the Mount Bromo tourist attraction on the Trip Advisor site means that there is no filtering of information or reviews so that the Mount Bromo National Park management does not know whether there are many positive or negative reviews. The method used in the research is the Naive Bayes method it produces an accuracy value of 0.85%. This accuracy value can be used to classify visitor review data at the Mount Bromo tourist attraction. The conclusion is that there are many positive reviews.

Corpus-based Approaches for Sentiment Analysis: A Review

The investigation studied the state of art on corpus-based approaches for sentiment analysis. Thus, detailing its methodologies, evaluation metrics, limitations, and future directions. The importance of sentiment analysis in fields such as marketing, customer feedback analysis, social media monitoring, financial analysis, and political science is emphasized. The methodology for corpus-based approaches in sentiment analysis includes the following key steps: data collection, preprocessing, feature extraction, and sentiment classification. The lexicon-based approaches include the corpus-based or bag of words (BOW) and dictionary (also called opinion lexicon). Evaluation of the corpus-based sentiment analysis approach is addressed through performance metrics such as accuracy, precision, recall, F1-score, and comparative analysis with other approaches including hybrid and rule-based systems. Limitations of corpus-based sentiment analysis, such as data sparsity and domain adaptation, are acknowledged, alongside potential enhancements and research directions including ensemble learning, deep learning architectures, and multimodal data integration. The conclusion emphasizes the versatility and scalability of corpus-based sentiment analysis, while ongoing research efforts aim to address its limitations and further enhance its applicability in diverse domains.

Advancing Sentiment Analysis Through Emotionally-Agnostic Text Mining in Large Language Models (LLMS)

The conventional methodology for sentiment analysis within large language models (LLMs) has predominantly drawn upon human emotional frameworks, incorporating physiological cues that are inherently absent in text-only communication. This research proposes a paradigm shift towards an emotionallyagnostic approach to sentiment analysis in LLMs, which concentrates on purely textual expressions of sentiment, circumventing the confounding effects of human physiological responses. The aim is to refine sentiment analysis algorithms to discern and generate emotionally congruent responses strictly from text-based cues. This study presents a comprehensive framework for an emotionally-agnostic sentiment analysis model that systematically excludes physiological indicators whilst maintaining the analytical depth required for accurate emotion detection. A novel suite of metrics tailored to this approach is developed, facilitating a nuanced interpretation of sentiment within text data, which is paramount for enhancing user interaction across a spectrum of text mining applications, including recommendation systems and interactive AI characters. The research undertakes a critical comparative analysis, juxtaposing the newly proposed model with traditional sentiment analysis techniques, to evaluate efficacy enhancements and to substantiate its application potential. Further, the investigation delves into the short-term memory capabilities of LLMs, exploring the implications for character AI roleplaying interfaces and their ability to recall and respond to user input within a text-driven emotional framework. Findings indicate that this emotionally-agnostic sentiment analysis approach not only simplifies the sentiment assessment process within LLMs but also opens avenues for a more precise and contextually appropriate emotional response generation. This abstracted model, devoid of human physiological constraints, represents a significant advancement in text mining, fostering improved interactions and contributing to the evolution of LLMs as adept analytical tools in various domains where emotional intelligence is crucial.

How should Bank Syariah Indonesia respond to cyber-attacks? Churn, sentiments, and emotions analysis with machine learning

IntroductionThis study aims to identify the most popular topics and words in Twitter conversations regarding cyber-attacks on Bank Syariah Indonesia that occurred in May 2023. It also seeks to analyze the sentiments, emotions, and potential customer churn of netizens following cyber-attacks.ObjectivesThe objective of this study is to investigate the public's response to cyber-attacks on Bank Syariah Indonesia, focusing on identifying key topics, analyzing sentiments and emotions, and estimating potential customer churn.MethodThis study uses a qualitative method with a sentiment analysis approach utilizing Orange Data Mining software. The data comprises tweets collected from May 10, 2023, to May 24, 2023, using keywords such as “BSI” and “Bank Syariah Indonesia,” resulting in 30,014 tweets. Sentiment and emotion analyses were conducted to categorize tweets and identify the prevalent sentiments and emotions.ResultsThe analysis reveals that the words “BSI,” “Data,” and “Lockbit” are most frequently mentioned, indicating the relevance of the cyber-attackers who targeted Bank Syariah Indonesia. The sentiment analysis showed that 56% of the tweets were neutral and dominated by emotions of joy. The study also identifies a short-term potential churn rate of 1.60% for Bank Syariah Indonesia's total customer base, indicating the risk of customers switching to other banks.ImplicationsThe results highlight the importance of robust cybersecurity measures and quick response strategies for maintaining customer trust and satisfaction. Financial institutions, particularly banks, must prioritize information and technology security to prevent customer churn and ensure the continuity of their services.Originality/NoveltyThis study provides insights into public reactions to cyber-attacks on Islamic banks, emphasizing the role of sentiment and emotion analysis in understanding customer behavior. This offers practical implications for improving risk management and customer retention strategies in the banking sector.

Public Perceptions of EV Charging Infrastructure: A Combined Sentiment Analysis and Topic Modeling Approach

Public Perceptions of EV Charging Infrastructure: A Combined Sentiment Analysis and Topic Modeling Approach