Sentiment Analysis Approach Research Articles

Sentiment analysis of Algerian Arabic dialect on social media Using Bi-LSTM recurrent neural networks

This paper presents a sentiment analysis approach using Bidirectional Long Short-Term Memory (Bi-LSTM) Recurrent Neural Networks to train predictive models for sentiment analysis on social media, particularly focusing on Algerian Arabic Dialect. The method leverages word-to-vector embedding for word representation and incorporates natural language understanding of emojis to improve semantic interpretation. The model achieves a high accuracy of 94%, demonstrating its effectiveness in analyzing sentiments in online discussions. The originality lies in applying Bi-LSTM to handle multilingual challenges on social platforms. The findings have practical implications for business, policymaking, and public sentiment evaluation, while also contributing positively to fostering informed online discourse.

DRSS: a multimodal sentiment analysis approach based on dual representation and self-supervised learning strategy

DRSS: a multimodal sentiment analysis approach based on dual representation and self-supervised learning strategy

A Machine Learning Approach for Sentiment Analysis Using Social Media Posts

Sentiment analysis on Twitter provides organizations and persons with quick and effective instrument to observe the public's perceptions of them and their competition. A modest number of assessment datasets have been produced in recent years to check the efficiency of sentiment analysis algorithms on Twitter. Researchers offer a review of eight publicly accessible as well as manually annotated assessment datasets for analyzing Twitter sentiment in this research. As a result of this evaluation, we demonstrate that is a widespread weakness of many when using these datasets performing at sentiment analysis the objective (entity) level is indeed the absence of different sentiment classifications across tweets as well as the objects contained in them.[1], As an example all of that "I love my iPhone but I despise my iPad." Could be marked with a made-by-mixing classify however the object iPhone contained within this Twitter post should be annotated with just a label with an optimism. To get around this restriction and enhance existing assessment We have datasets that provide STS-Gold a novel assessment of datasets in which tweets or objects (entities) remain tagged separately hence might show alternative opinion labels. Though research furthermore compares the various datasets on multiple characteristics such as an entire quantity of posts as well as vocabulary size and sparsity.[2] In addition, look at pair by pair relationships between these variables and how they relate to sentiment classifier performance on various data. In this study we used five different classifiers and compared them and, in our experiment, we found that the bagging ensemble classifier performed best among them and have an accuracy level of 94.2% for the GASP dataset and 91.3% for the STS-Gold dataset.

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.

Service‐dominant logic and customer engagement based value proposition framework in peer‐to‐peer accommodation: A two‐study approach

AbstractIn the competitive travel accommodation market, peer‐to‐peer accommodation (P2PA) providers are in desperate need of a value proposition framework that will assist them in guiding the development of an ‘engaged’ customer base. However, currently, the P2PA literature does not provide such a framework that includes a comprehensive list of tangible and intangible values driving customer engagement (CE). Underpinned by the service‐dominant (S‐D) logic theory, we adopted a big data‐based, two study approach to identify these values (and their composites) that drive CE in P2PA. Study 1 was conducted to identify important tangible and intangible values driving customers to engage with P2PA. This qualitative study adopted a text‐mining and sentiment analysis approach which was performed on 499,160 customer reviews. The subsequent quantitative study (Study 2) used a multi‐factor ANOVA analysis (i.e., Automatic Linear Modelling) to examine 19,060 listed houses using their star ratings and other available quantitative data (e.g., accommodation cost, response rate and number of rooms). Our findings present an S‐D logic‐based value proposition framework for enhancing CE in P2PA. This framework shows that customers consider seven tangible‐intangible values (i.e., accommodation, perceived enjoyment, perceived friendliness, perceived convenience, surrounding, perceived response and trustworthiness) in creating three values (i.e., re‐adopt, recommend, and refer P2PA) for P2PA‐platform providers and listed‐houses. Overall, by developing an S‐D logic‐based framework and identifying salient values in P2PA value co‐creation, this research extends both the P2PA literature and S‐D logic theory. We also provide insightful recommendations for P2PA providers.

Sentiment Analysis of Political Parties on social media: A Machine Learning and Lexicon-Based Approach

Social media platforms like Facebook, Twitter, Instagram, and YouTube have become central to communication and entertainment, with users sharing opinions on various topics. These opinions, often categorized as positive, negative, or neutral sentiments, provide valuable data for sentiment analysis. Our research analyzed political YouTube comments related to India’s Bhartiya Janata Party (BJP) and Indian National Congress (INC) using a combination of the AFINN lexicon and machine learning techniques. We applied feature representation methods such as Bag of Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF), alongside five machine learning algorithms: Multinomial Naïve Bayes, Logistic Regression, Random Forest, Support Vector Machine (SVM), and K-nearest neighbor (K-NN). We aimed to determine the most efficient sentiment analysis approach by comparing the performance of these models using standard evaluation metrics. For the BJP dataset, Logistic Regression performed best with BoW, while SVM was most effective with TF-IDF. Similarly, for the INC dataset, Random Forest excelled with BoW, and SVM outperformed others with TF-IDF. The AFINN lexicon showed poor performance across both datasets, and K-NN consistently achieved lower accuracy. Our findings suggest that SVM and Random Forest are more suitable for political sentiment analysis.

Exploring gender stereotypes in financial reporting: An aspect-level sentiment analysis using big data and deep learning

This study delves into the intricate interplay between gender stereotypes and financial reporting through an aspect-level sentiment analysis approach. Leveraging Big Data comprising 129,251 human face images extracted from 2085 financial reports in Chile, and employing Deep Learning techniques, we uncover the underlying factors influencing the representation of women in financial reports. Our findings reveal that gender stereotypes, combined with external economic factors, significantly shape the portrayal of women in financial reports, overshadowing intentional efforts by companies to influence stakeholder perceptions of financial performance. Notably, economic expansion periods correlate with a decline in women's representation, while economic instability amplifies their portrayal. Furthermore, the financial inclusion of women positively correlates with their presence in financial report images. Our results underscore a bias in image selection within financial reports, diverging from the neutrality principles advocated by the International Accounting Standards Board (IASB). This pioneering study, combining Big Data and Deep Learning, contributes to gender stereotype literature, financial report soft information research, and business impression management research.

Hybrid HAN‐CNN with aspect term extraction for sentiment analysis using product review

AbstractIn this article, an intensive sentiment analysis approach termed Hierarchical attention‐convolutional neural network (HAN‐CNN) has been proposed using product reviews. Firstly, the input product review is subjected to Bidirectional Encoder Representation from Transformers (BERT) tokenization, where the input data of each sentence are partitioned into little bits of words. Thereafter, Aspect Term Extraction (ATE) is carried out and feature extraction is completed utilizing some features. Finally, sentiment analysis is accomplished by the developed HAN‐CNN, which is formed by combining a Hierarchical Attention Network (HAN) and a Convolutional Neural Network (CNN). Moreover, the proposed HAN‐CNN achieved a greater performance with maximum accuracy, recall and F1‐Score of 91.70%, 90.60% and 91.20%, respectively.

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.

Aspect-based sentiment analysis via bidirectional variant spiking neural P systems

In recent years, aspect-based sentiment classification has predominantly relied on artificial neural networks, achieving notable success. However, effectively capturing the contextual semantic relationships between aspects and content has remained challenging. To address this issue, we propose an innovative approach for aspect-based sentiment analysis. Our method uses a Bidirectional Variant Spiking Neural P System (Bi-VSNP) as a context encoding module, integrated with a two-channel mechanism to extract context and aspect features. Furthermore, Our dual attention mechanism leverages the relationship between aspects and context, allowing the model to focus on key sentiment information. We rigorously evaluated our method on publicly available datasets, demonstrating its superior performance over most models based on artificial neural networks as context extraction. Compared to other models utilizing SNP methods, our approach achieves performance improvements of 1.72%, 1.27%, and 1.05% on the Laptop, Restaurant, and Twitter datasets, respectively. These results underscore the efficacy of our approach in aspect-based sentiment classification.

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.

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.