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The emergence of multimodal data on social media platforms presents new opportunities to better understand user sentiments toward a given aspect. However, existing multimodal datasets for aspect-category sentiment analysis (ACSA) often focus on textual annotations, neglecting fine-grained information in images. Consequently, these datasets fail to fully exploit the richness inherent in multimodal. To address this, we introduce a new Vietnamese multimodal dataset, named ViMACSA, which consists of 4876 text-image pairs with 14,618 fine-grained annotations for both text and image in the hotel domain. Additionally, we propose a fine-grained cross-modal fusion framework (FCMF) that effectively learns both intra- and inter-modality interactions and then fuses these information to produce a unified multimodal representation. Experimental results show that our framework outperforms SOTA models on the ViMACSA dataset, achieving the highest F1 score of 79.73%. We also explore characteristics and challenges in Vietnamese multimodal sentiment analysis, including misspellings, abbreviations, and the complexities of the Vietnamese language. This work contributes both a benchmark dataset and a new framework that leverages fine-grained multimodal information to improve multimodal aspect-category sentiment analysis. Our dataset is available for research purposes. [ABSTRACT FROM AUTHOR]

On June 27, 2015, the Greek coalition government, led by the left-wing SYRIZA party, announced the July 5th referendum, asking citizens to decide on the adoption of the EU-proposed economic plan. Referendums in Greece are infrequent, and this decision sparked various interpretations of the motives behind it. In events like the 2016 US presidential elections and the Brexit referendum of the same year, public opinion, especially as expressed on platforms like Twitter, often diverged from the narrative set by the news media agenda. The outcome of the Greek referendum reflected Twitter users' preferences more closely, surprising many and challenging the traditional role of news media in shaping public opinion. This paper revisits the 2015 Greek referendum, comparing topics discussed in news media with those on Twitter to understand whether the disparity between the two platforms resulted from the dominance of news media agenda-setting or other factors, such as Twitter's inclination toward alternative voices. The study employs content analysis and topic modeling on a dataset comprising news articles and tweets. Results indicate that the news media agenda predominantly influenced topics discussed by "YES" supporters on Twitter, while it played a less significant role in shaping the topics discussed by "NO" supporters during the 2015 Greek referendum. [ABSTRACT FROM AUTHOR]

Sentiment analysis has been widely used in various fields of social media, education, and business. Specifically, in the education domain, the usage of sentiment analysis is difficult due to the huge amount of information, the nature of language, and processing the diverse perceptions of students. Deep learning emerges as an advanced concept in the realm of machine learning that learns features automatically from raw text data, making them well-suited for sentiment analysis tasks. In recent years, deep learning has been used in analyzing the sentiments. Deep learning architectures have surpassed other machine learning paradigms for performing sentiment analysis. The ability to analyze automatically the students’ sentiments enables HEI to process huge amounts of unstructured data quickly, efficiently, and cost-effectively. The paper aims to predict the sentiments of students’ reviews posted in VLE regarding online learning that enables the educators to optimize their teaching methods for the best results. This study paper explores the usage of CNN, LSTM, and hybrid CNN-LSTM for the prediction of sentiments. The proposed hybrid CNN-LSTM architecture achieves superior performance compared to other baseline algorithms with respect to accuracy, precision, recall, and F1 score. According to outcomes, the recommended technique achieves remarkable accuracy of 97%. The findings facilitate the progress of a more efficient deep learning sentiment prediction system that gives valuable insights from a huge volume of students’ textual data. [ABSTRACT FROM AUTHOR]

Aspect-oriented extraction involves the identification and extraction of specific aspects, features, or entities within a piece of text. Traditional methods often struggled with the complexity and variability of language, leading to the exploration of advanced deep learning approaches. In the realm of sentiment analysis, the conventional approaches often fall short when it comes to providing a nuanced understanding of sentiments expressed in textual data. Traditional sentiment analysis models often overlook the specific aspects or entities within the text that contribute to the overall sentiment. This limitation poses a significant challenge for businesses and organizations aiming to gain detailed insights into customer opinions, product reviews, and other forms of user-generated content.In this research, we propose an innovative approach for aspect-oriented extraction and sentiment analysis leveraging optimized hybrid deep learning techniques. Our methodology integrates the powerful capabilities of deep learning models with the efficiency of Reptile Search Optimization. Furthermore, we introduce an advanced sentiment analysis framework employing the state-of-the-art Extreme Gradient Boosting Algorithm. The fusion of these techniques aims to enhance the precision and interpretability of aspect-oriented sentiment analysis. The proposed approach first utilizes deep learning architectures to extract and comprehend diverse aspects within textual data. Through the incorporation of Reptile Search Optimization, we optimize the learning process, ensuring adaptability and improved model generalization across various datasets. Subsequently, the sentiment analysis phase employs the robust Extreme Gradient Boosting Algorithm, known for its effectiveness in handling complex relationships and patterns within data. Our experiments, conducted on diverse datasets, demonstrate the superior performance of the proposed methodology in comparison to traditional approaches. The optimized hybrid deep learning approach, coupled with the Reptile Search Optimization and Extreme Gradient Boosting Algorithm, showcases promising results in accurately capturing nuanced sentiments associated with different aspects. This research contributes to the advancement of aspect-oriented sentiment analysis techniques, offering a comprehensive and efficient solution for understanding sentiment nuances in textual data across various domains. The ResNet 50 and EfficientNet B7 architecture of the modified pre-trained model is proposed for the aspect extraction function. The Reptile Search Optimization based Extreme Gradient Boosting Algorithm (RSO-EGBA) is proposed to analyze and predict customer sentiments. The execution of this study is carried out using python software. It has been observed that the overall accuracy of our proposed method is 99.8%, while that of the other state-of-the-art. The overall accuracy of our proposed method shows an increment of 9–16% from that of the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The main objective of recommender systems is to assist users in overcoming the issue of information overload by providing them with a carefully selected list of items that they are likely to find useful or relevant. Recommender systems may face many limitations and challenges, such as the cold start problem, which occurs when there is insufficient or no information about a new user or item. This leads to a decline in the performance of the recommender system. In this paper, we propose a recommendation system based on textual reviews and the deep learning method (RS-TRDL) to alleviate the user cold-start problem. Our RS-TRDL model can extract the important aspects and underlying sentiment polarity classification from the review text using NLP techniques and a deep learning method. These are then fused into collaborative filtering techniques to improve the RS and alleviate the user cold-start problem. The proposed method consists of two components: (i) An aspectbased sentiment analysis module that aims to extract aspects from the review text with its polarity; (ii) A recommendation generation component that uses the aspects as additional information with the numeric ratings. It also employs an important feature in the dataset, namely, the helpfulness to finally infer the overall rating prediction. Extensive experiments were conducted by the proposed system on two Amazon datasets. The experimental results show that the proposed RS-TRDL model exceeded all literature-reviewed comparison methods in the cold-start problem alleviation task. [ABSTRACT FROM AUTHOR]

Customer satisfaction is not just a significant factor but a cornerstone for smart cities and their organizations that offer services to people. It enhances the organization's reputation and profitability and drastically raises the chances of returning customers. Unfortunately, customer support service through online chat is often not rated by customers to help improve the service. This study employs artificial intelligence and data augmentation to predict customer satisfaction ratings from conversations by analyzing the responses of customers and service providers. For the study, the authors obtained actual conversations between customers and real agents from the call center database of Jeddah Municipality that were rated by customers on a scale of 1–5. They trained and tested five prediction models with approaches based on logistic regression, random forest, and ensemble-based deep learning, and fine-tuned two pre-trained recent models: ArabicT5 and SaudiBERT. Then, they repeated training and testing models after applying a data augmentation technique using the generative artificial intelligence, GPT-4, to improve the unbalance in customer conversation data. The study found that the ensemble-based deep learning approach best predicts the five-, three-, and two-class classifications. Moreover, data augmentation improved accuracy using the ensemble-based deep learning model with a 1.69% increase and the logistic regression model with a 3.84% increase. This study contributes to the advancement of Arabic opinion mining, as it is the first to report the performance of determining customer satisfaction levels using Arabic conversation data. The implications of this study are significant, as the findings can be applied to improve customer service in various organizations. [ABSTRACT FROM AUTHOR]

Sentiment analysis has become an important task in natural language processing because it is used in many different areas. This paper gives a detailed review of sentiment analysis, including its definition, challenges, and uses. Different approaches to sentiment analysis are discussed, focusing on how they have changed and their limitations. Special attention is given to recent improvements with transformer models and transfer learning. Detailed reviews of well-known transformer models like BERT, RoBERTa, XLNet, ELECTRA, DistilBERT, ALBERT, T5, and GPT are provided, looking at their structures and roles in sentiment analysis. In the experimental section, the performance of these eight transformer models is compared across 22 different datasets. The results show that the T5 model consistently performs the best on multiple datasets, demonstrating its flexibility and ability to generalize. XLNet performs very well in understanding irony and sentiments related to products, while ELECTRA and RoBERTa perform best on certain datasets, showing their strengths in specific areas. BERT and DistilBERT often perform the lowest, indicating that they may struggle with complex sentiment tasks despite being computationally efficient. [ABSTRACT FROM AUTHOR]

Sentiment analysis (SA) is a technique that employs natural language processing to determine the function of mining methodically, extract, analyse and comprehend people's thoughts, feelings, personal opinions and perceptions as well as their reactions and attitude regarding various subjects such as topics, commodities and various other products and services. However, it only reveals the overall sentiment. Unlike SA, the aspect-based sentiment analysis (ABSA) study categorizes a text into distinct components and determines the appropriate sentiment, which is more reliable in its predictions. Hence, ABSA is essential to study and break down texts into various service elements. It then assigns the appropriate sentiment polarity (positive, negative or neutral) for every aspect. In this paper, the main task is to critically review the research outcomes to look at the various techniques, methods and features used for ABSA. After giving brief introduction of SA in order to establish a clear relationship between SA and ABSA, we focussed on approaches, applications, challenges and trends in ABSA research. [ABSTRACT FROM AUTHOR]

Opinion mining for mobile application (app) reviews aims to analyze people’s comments on app stores to support software engineering activities, particularly software maintenance and evolution. One of the main challenges for software quality maintenance is promptly identifying emerging issues, e.g., bugs. However, due to a large amount of textual data, manually analyzing these comments is challenging, and machine learning-based methods have been used to automate opinion mining. This paper introduces the automatic generation of a risk matrix from user reviews to answer the following research question: how do we prioritize and treat reviews in time so that the app is competitive and guarantees the timely maintenance and evolution of the software? We present the MApp-IDEA (Monitoring App for Issue Detection and Prioritization) method to detect issues and classify the reviews in a risk matrix with prioritization levels. We present an approach that (i) automatically collects reviews, (ii) detects issues, (iii) classifies reviews in a risk matrix, and then (iv) models the temporal dynamics of issues and risks through time series to trigger alerts. We performed an empirical evaluation with 50 mobile apps and processed approximately 5 million reviews, where we detected 230,000 issues and classified them into priority levels using a risk matrix. We found that issues detected early with our approach are associated with later fix releases by developers. [ABSTRACT FROM AUTHOR]

Abstract Now adays people express and share their opinions on various events on the internet thanks to social media. Opinion mining is the process of interpreting user-generated opinion data on social media. Aside from its lack of resources in opinion-mining tasks, Amharic presents numerous difficulties because of its complex structure and variety of dialects. Analyzing every comment written in Amharic is a challenging task. Significant advancements in opinion mining have been achieved using deep learning. An opinion-mining model was used in this study to classify user comments written in Amharic as positive or negative. The domains that we focus on in this study are YouTube and Facebook. From the Ethiopian broadcasts YouTube and Facebook official pages, we gathered 11,872 unstructured data for this study using www.exportcomment.com , and Facebook page tools. Text preprocessing and feature extraction techniques were used, in addition to manual annotation by linguistic specialists. The dataset was prepared for the experiment after annotation, preprocessing, and representation. LSTM, GRU, BiGRU, BiLSTM, and a hybrid of CNN with BiLSTM classifiers from the TensorFlow Keras deep learning library were used to train the model using the dataset, which was split using the 80/20 train-test method, which proved effective for classification problems. Finally, we achieved of 94.27%, 95.20%, 95.49%, 95.62%, and 96.08% using GRU, BiGRU, LSTM, BiLSTM, and CNN with BiLSTM, respectively, in word2vec embedding model.

This paper explores the diversity of people's opinions. We have chosen as the research material a corpus of customer reviews on robotic vacuum cleaners in the Russian language. The study aims to identify unique perspectives and recurring patterns in user feedback, examining how individuals express their satisfaction or dissatisfaction with the product. In particular, we estimate the growth rate of the number of different opinions with an increase of the corpus size. By employing both qualitative and quantitative methods, the research explores the linguistic strategies used by users to convey their experiences and emotions. We utilize a large language model (LLM) as a tool for extracting and analyzing opinions from product reviews.

The rapid growth of Internet-enabled applications, such as social media platforms, e-commerce sites, and blogs, has led to a surge in user-generated content. This vast amount of data has made sentiment analysis increasingly valuable. Modern Aspect-Based Sentiment Analysis (ABSA) offers a more detailed approach by identifying sentiment trends related to specific aspects within the text. However, the challenge lies in analyzing reviews that are often short, unstructured, and filled with slang and emotive language, making it difficult to gauge customer opinions accurately. To address these issues, we proposed an effective hybrid approach “RoBERTa-1D-CNN-BiLSTM” for ABSA. Initially, the pre-trained Robustly Optimized BERT approach (RoBERTa) and One Dimensional Convolutional Neural Network (1D-CNN) models are used to extract features at the aspect level from the context of the review, following which classification is performed using Bidirectional Long Short-Term Memory (BiLSTM). The approach is evaluated on three cross-domain standards datasets, yielding an accuracy of 92.33%. The results of the experiments show that it surpasses the current leading methods in sentiment analysis and product recommendation.

Now adays people express and share their opinions on various events on the internet thanks to social media. Opinion mining is the process of interpreting user-generated opinion data on social media. Aside from its lack of resources in opinion-mining tasks, Amharic presents numerous difficulties because of its complex structure and variety of dialects. Analyzing every comment written in Amharic is a challenging task. Significant advancements in opinion mining have been achieved using deep learning. An opinion-mining model was used in this study to classify user comments written in Amharic as positive or negative. The domains that we focus on in this study are YouTube and Facebook. From the Ethiopian broadcasts YouTube and Facebook official pages, we gathered 11,872 unstructured data for this study using www.exportcomment.com, and Facebook page tools. Text preprocessing and feature extraction techniques were used, in addition to manual annotation by linguistic specialists. The dataset was prepared for the experiment after annotation, preprocessing, and representation. LSTM, GRU, BiGRU, BiLSTM, and a hybrid of CNN with BiLSTM classifiers from the TensorFlow Keras deep learning library were used to train the model using the dataset, which was split using the 80/20 train-test method, which proved effective for classification problems. Finally, we achieved of 94.27%, 95.20%, 95.49%, 95.62%, and 96.08% using GRU, BiGRU, LSTM, BiLSTM, and CNN with BiLSTM, respectively, in word2vec embedding model. [ABSTRACT FROM AUTHOR]

We perform a sentence-level sentiment analysis study of different literary texts in English language. Each text is converted into a series in which the data points are the sentiment value of each sentence obtained using the sentiment analysis tool (VADER). By applying the Detrended Fluctuation Analysis (DFA) and the Higuchi Fractal Dimension (HFD) methods to these sentiment series, we find that they are monofractal with long-term correlations, which can be explained by the fact that the writing process has memory by construction, with a sentiment evolution that is self-similar. Furthermore, we discretize these series by applying a classification approach which transforms the series into a one on which each data point has only three possible values, corresponding to positive, neutral or negative sentiments. We map these three-states series to a Markov chain and investigate the transitions of sentiment from one sentence to the next, obtaining a state transition matrix for each book that provides information on the probability of transitioning between sentiments from one sentence to the next. This approach shows that there are biases towards increasing the probability of switching to neutral or positive sentences. The two approaches supplement each other, since the long-term correlation approach allows a global assessment of the sentiment of the book, while the state transition matrix approach provides local information about the sentiment evolution along the text. [ABSTRACT FROM AUTHOR]

The higher levels of technology-driven consumption, especially the rapid replacement of mobile phones, have recently led to ecological imbalance due to the growth of electronic waste generation in both developed and developing societies. Refurbishing mobile phones can contribute significantly to reducing electronic waste and conserving natural resources by extending the lifespan of mobile phones. In this vein, further adoption of such devices can be considered a significant opportunity. Nonetheless, consumer purchasing behavior differs in various socio-cultural settings. Therefore, to better identify such differences in consumers' needs and the influencing factors on their actual behavior toward refurbished mobile phones in the USA and India (two diverse societies), this cross-cultural study applies a confirmatory aspect-level opinion mining method to analyze customer comments collected from Amazon.com and Amazon.in. According to the results obtained, American and Indian customers show various attention levels and emotional inclinations concerning the same pre-specified aspect category of this study. American consumers mention features related to product characteristics of refurbished phones (e.g., battery, camera, screen, etc.) more than Indian customers (26.7% difference on percent of total), while Indians mostly emphasize on unsatisfactory experiences of these features. On the other hand, in India, seller-related aspects, including seller's reputation, warranty, and packaging are of higher importance (37.2% of total) when compared to the USA (17.8% of total). Moreover, considering the customer-related category, it can be concluded that environmental incentives are stronger for American customers, whereas Indian customers are highly motivated by financial motivators. Eventually, remanufacturers and marketing management can apply the research findings to develop appropriate product development and marketing strategies in both developed and developing countries. [ABSTRACT FROM AUTHOR]

Although graphene has created a strong wave of enthusiasm after its discovery in 2004 and the subsequent Nobel prize awarded to the two detectors in 2010, there is no clear information how people with special interests in it feel toward the possibility of its commercial value over time. The objective of this study is to identify graphene sentiment shift and major discussion topics among the interested public on Reddit by presenting an effective tool for extracting public opinions in social media. To achieve this, we processed 11,287 comments collected from the Reddit from 2010 to 2021, and applied text mining techniques, including sentiment analysis. This study found that over half of the comments were positive toward graphene, and specific sentiment fluctuated commensurate with major events, for example, Nobel Prize, commercialisation delay and wide applications to the industry. Our research proved that public sentiments toward graphene technology follow a similar trend to the hype cycle by deriving a sentiment hype cycle for graphene. This study provides a convincing case for understanding shifts in the sentiments of the interested public regarding new technology, such as graphene, which will interest graphene technology R&D experts and policy-makers. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to develop a conceptual model of positive employee experience using sentiment analysis within algorithm-based human resource (HR) strategies. Its goal is to enhance HR professionals' understanding of employee experiences and enable data-driven decision-making to create a positive work environment, thereby contributing to the originality of HR research. Design/methodology/approach: The study conducts sentiment analysis – a text mining technique – to assess employee reviews and extract distinct positive experience factors. The employed data-driven methodology serves to fortify the reliability and objectivity of the analysis, ultimately resulting in a more refined depiction of the conveyed sentiment. Findings: Utilizing sentiment analysis, the authors identified 135 keywords that signify positive employee experiences. These keywords were then categorized into four clusters aligned with factors influencing employee experience: work, relationships, organizational system and organizational culture, employing an inductive approach. The framework outlines the process of nurturing positive employee experiences throughout the employee life cycle, incorporating insights from the affective events theory and cognitive appraisal theory. Practical implications: Data-driven insights empower HR professionals to enhance employee satisfaction, engagement and productivity. HR managers implementing AI-assisted HR ecosystems need digital and data science skills. Additionally, these insights can offer practical support in accentuating diversity and ethical considerations within the organizational culture. Candid employee data can enhance leadership and support diversity in organizational culture. Managers play a crucial communication role, ensuring flexible access to personalized HR solutions. Originality/value: Applying sentiment analysis through opinion mining allows for the collection of unstructured data, reflecting authentic employee perceptions. This innovative approach expedites issue identification and targeted actions, enhancing employee satisfaction. Textual reviews, integral to employee feedback, offer comprehensive insights. Additionally, considering subjectivity and review length in online employee reviews adds value to understanding experiences (Zhao et al., 2019). This study surpasses prior research by directly identifying key factors of employee experience through the analysis of actual employee review texts, addressing a gap in understanding beyond previous attempts. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Hospitality & Tourism Technology is the property of Emerald Publishing Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Polarity shifting can be considered one of the most challenging problems in the context of sentiment analysis. Polarity shifters are treated as linguistic contextual items that can increment, reduce or neutralise the polarity of a word called 'focus' included in an opinion. The automatic detection of such items enhances performance and accuracy of computational systems for opinion mining. From a symbolic approach, we aim to advance in the automatic processing of the polarity shifters that affect the opinions expressed on tweets in English. To this end, we describe a novel knowledge-based model to deal with quantification in English, which increments or reduces the polarity of opinions. In particular, we explain the linguistic rules of each category of quantification shifter, including information about the scope and direction with respect to the focus. Furthermore, we present the mathematical formulae that calculate the strength of the effect on the prior polarity. Finally, we describe the matrices associated to the linguistic rules, which serve to model the knowledge in text-mining systems. [ABSTRACT FROM AUTHOR]

The notion of discerning political affiliations from users' social media behavior instills a sense of unease in many. Democracy necessitates that individuals' political affiliations remain private, and social media challenges this foundational principle of democracy. This study uses BERT, a pre-trained language model to analyze X's (formally Twitter) users and their political affiliations to understand that how much it is easy now to find the political affiliation of people. We collect posts in both English and Urdu languages from different political leaders and their followers, which are used to fine-tune the BERT model. The model classifies the users' profiles into Pro, Neutral, or Anti-government classes. To assess the performance of the proposed method, experiments are conducted to evaluate its accuracy, efficiency, and effectiveness. The findings of this study confirm the hypothesis that it is easy to detect the political affiliation of individuals using social media with high accuracy (69% for English and 94% for Urdu language) and it can undermine democracy. [ABSTRACT FROM AUTHOR]

On June 27, 2015, the Greek coalition government, led by the left-wing SYRIZA party, announced the July 5th referendum, asking citizens to decide on the adoption of the EU-proposed economic plan. Referendums in Greece are infrequent, and this decision sparked various interpretations of the motives behind it. In events like the 2016 US presidential elections and the Brexit referendum of the same year, public opinion, especially as expressed on platforms like Twitter, often diverged from the narrative set by the news media agenda. The outcome of the Greek referendum reflected Twitter users’ preferences more closely, surprising many and challenging the traditional role of news media in shaping public opinion. This paper revisits the 2015 Greek referendum, comparing topics discussed in news media with those on Twitter to understand whether the disparity between the two platforms resulted from the dominance of news media agenda-setting or other factors, such as Twitter’s inclination toward alternative voices. The study employs content analysis and topic modeling on a dataset comprising news articles and tweets. Results indicate that the news media agenda predominantly influenced topics discussed by “YES” supporters on Twitter, while it played a less significant role in shaping the topics discussed by “NO” supporters during the 2015 Greek referendum.

Sentiment analysis is becoming rapidly important for exploring social media Bangla text. The lack of sufficient resources is considered to be an important challenge for Aspect Based Sentiment Analysis (ABSA) of the Bangla language. The ABSA is a technique that divides the text and defines its sentiment based on its aspects. In this paper, we developed a high-quality Bangla ABSA annotated dataset namely BANGLA_ABSA. The datasets are labelled with aspects category and their respective sentiment polarity to do the ABSA task in Bangla. Four open domains namely Restaurant, Movie, Mobile phone, and Car are considered to make the dataset. The datasets are called Restaurant_ABSA, Movie_ABSA, Mobile_phone_ABSA, and Car_ABSA respectively that contain 801, 800, 975, and 1149 comments. All the comments are either complex or compound sentences. We created the dataset manually and annotated the same by exerting opinions. We organized the dataset as three tuples in Excel format namely 〈Id, Comment, {Aspect category, Sentiment Polarity}〉. These data are very important that facilitate the efficient handling of sentiment for any machine learning and deep learning research, especially for Bangla text.

With the progressive enhancement of social media, several people prefer posting their opinions on various social media instead of posting on radios, television or newspapers. The postings differ in dimensions and include various titles and comments. Nowadays, the formation of plagiarism is increasing tremendously which occurs by rewriting or repeating one’s work. There are many ways to detect plagiarism by browsing through the internet. The significant intention of this paper involves the detection of plagiarism in social media using four different phases, namely the data pre-processing phase, n-gram evaluation, similarity/distance computation analysis and the plagiarism detection phase. The pre-processing includes data cleaning processes, such as the removal of redundant data, upper case letters, noise, irrelevant punctuations and characterizing into a vector form. After pre-processing the data are fed for n-gram evaluation to develop a posting attribution system. Then finally, an ensemble support vector machine-based African vulture optimization (ESVM-AVO) approach is employed to detect plagiarism which signifies that the performance based on detection is enhanced and the execution time in obtaining a high rate of detection accuracy is very low. Finally, the performance evaluation and the comparative analysis are carried out to determine the performance of the proposed system.

Sentimental analysis, is the study of sentiments which resolves the judgement of customer's opinions emotions, sentiments and evaluations in terms of the entities like, services, topics, products, and events. In this research work, an innovative opinion mining system called Dual Interactive Wasserstein Namib Beetle Generative Adversarial Network (DIWNBGAN) is introduced. Initially, the data is gathered and pre-processed. Secondly, the necessary features are extracted by optimizing the reviews with the aid of lexicon hybrid N-gram2vector method. Since navigating through a vast array of reviews can be challenging, feature selection serves to diminish the dimensionality of the data. Thirdly, the entropy-kurtosis based feature selection technique is proposed for the dimensionality reduction purpose. The selection of an appropriate feature is crucial in sentiment analysis as it enables the identification of product attributes that consumers either dislike or extensively discuss. Finally, the DIWNBGAN is employed to classify the selected reviews into different states, like extremely positive, positive, neutral, extremely negative, and negative. The introduced approach is assessed by applying data collected from amazon's smartphone review dataset. A total of 150,000 reviews/samples were extracted from web pages and the dataset size is 3200 × 5. The implementation of the introduced work is carried out using PYTHON software. From the experimental outcomes, the achieved accuracy, precision, recall, and F1 Score of the introduced system are: 99.98%, 97.88%, 99.97%, and 98.97% respectively. The proposed model (DIWNBGAN) outperforms all the implemented models based on accuracy, with an improvement of 25.34%. [ABSTRACT FROM AUTHOR]

With the progressive enhancement of social media, several people prefer posting their opinions on various social media instead of posting on radios, television or newspapers. The postings differ in dimensions and include various titles and comments. Nowadays, the formation of plagiarism is increasing tremendously which occurs by rewriting or repeating one’s work. There are many ways to detect plagiarism by browsing through the internet. The significant intention of this paper involves the detection of plagiarism in social media using four different phases, namely the data pre-processing phase, n-gram evaluation, similarity/distance computation analysis and the plagiarism detection phase. The pre-processing includes data cleaning processes, such as the removal of redundant data, upper case letters, noise, irrelevant punctuations and characterizing into a vector form. After pre-processing the data are fed for n-gram evaluation to develop a post- ing attribution system. Then finally, an ensemble support vector machine-based African vulture optimization (ESVM-AVO) approach is employed to detect plagiarism which signifies that the performance based on detection is enhanced and the execution time in obtaining a high rate of detection accuracy is very low. Finally, the performance evaluation and the comparative analysis are carried out to determine the performance of the proposed system. [ABSTRACT FROM AUTHOR]

With the increasing availability of user opinions on the web, understanding the distinct nature of opinions in societal and non-societal contexts becomes crucial for opinion mining and sentiment analysis tasks. Societal topics, encompassing social unrest, terrorist acts, and government policies, differ significantly from non-societal topics like product reviews, movie reviews, and restaurant reviews. Given the regional specificity of societal issues and the lack of sentiment-annotated resources for them, this paper highlights the need to comprehend the differences in opinions between these domains for effective sentiment analysis. Through statistical text and network analysis, it investigates word usage, sentiment word association, and homogeneity in societal versus non-societal contexts. The study also explores graph-based analysis as a novel approach to sentiment analysis, considering its advantage in easily expanding context through the addition of nodes, as opposed to the complexity of inserting relevant tokens in text. The findings suggest that while non-societal sentiment resources might not be directly applicable to societal domains, graph-based analysis offers promising avenues for sentiment analysis in diverse societal topics. [ABSTRACT FROM AUTHOR]

Background and Objectives The coronavirus disease 2019 (COVID-19) pandemic led to many hospital service disruptions and strict visitor restrictions that affected care of older adult populations. This study investigates perceptions of hospital care for persons with dementia during the COVID-19 pandemic as shared on Reddit's social media platform. Research Design and Methods This study combined an Opinion Mining Framework with linguistic processing to conduct a sentiment analysis of word clusters and care-based content in a sample of 1,205 posts shared between February 2020 and March 2023 in Reddit's English-language corpus. Data were classified based on reoccurring contiguous sequences of 2 words from our text sample. Results Hospital dementia care discourse on Reddit advanced 4 negative sentiment themes: (1) fear of poor medication management, hydration, and hygiene, (2) loss of patient advocacy, (3) precipitation of advance directive discussions, and (4) delayed discharge and loss of nursing home bed. One positive sentiment theme also emerged: gratitude toward hospital staff. Discussion and Implications Negative sentiment Reddit posts constituted a larger share of the posts than positive posts regarding hospital care for persons with dementia. People who posted about their experiences shared their concerns about hospital care deficiencies and the importance of including informal caregivers in hospital settings, particularly in the context of a pandemic. Implications exist for dementia training, improved quality of care, advance care planning, and transitions in care policies. [ABSTRACT FROM AUTHOR]

Copyright of Omer Halisdemir Universitesi Iktisadi ve Idari Bilimler Fakültesi Dergisi is the property of Omer Halisdemir University, Faculty of Economics & Admistrative Sciene and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper introduces a methodology to generate review-based natural language justifications supporting personalized suggestions returned by a recommender system. The hallmark of our strategy lies in the fact that natural language justifications are adapted to the different contextual situations in which the items will be consumed. In particular, our strategy relies on the following intuition: Just like the selection of the most suitable item is influenced by the contexts of usage, a justification that supports a recommendation should vary as well. As an example, depending on whether a person is going out with her friends or her family, a justification that supports a restaurant recommendation should include different concepts and aspects. Accordingly, we designed a pipeline based on distributional semantics models to generate a vector space representation of each context. Such a representation, which relies on a term-context matrix, is used to identify the most suitable review excerpts that discuss aspects that are particularly relevant for a certain context. The methodology was validated by means of two user studies, carried out in two different domains (i.e., movies and restaurants). Moreover, we also analyzed whether and how our justifications impact on the perceived transparency of the recommendation process and allow the user to make more informed choices. As shown by the results, our intuitions were supported by the user studies. [ABSTRACT FROM AUTHOR]

In recent years, e-commerce platforms have become popular and transformed the way people buy and sell goods. People are rapidly adopting Internet shopping due to the convenience of purchasing from the comfort of their homes. Online review sites allow customers to share their thoughts on products and services. Customers and businesses increasingly rely on online reviews to assess and improve the quality of products. Existing literature uses natural language processing (NLP) to analyze customer reviews for different applications. Due to the growing importance of NLP for online customer reviews, this study attempts to provide a taxonomy of NLP applications based on existing literature. This study also examined emerging methods, data sources, and research challenges by reviewing 154 publications from 2013 to 2023 that explore state-of-the-art approaches for diverse applications. Based on existing research, the taxonomy of applications divides literature into five categories: sentiment analysis and opinion mining, review analysis and management, customer experience and satisfaction, user profiling, and marketing and reputation management. It is interesting to note that the majority of existing research relies on Amazon user reviews. Additionally, recent research has encouraged the use of advanced techniques like bidirectional encoder representations from transformers (BERT), long short-term memory (LSTM), and ensemble classifiers. The rising number of articles published each year indicates increasing interest of researchers and continued growth. This survey also addresses open issues, providing future directions in analyzing online customer reviews. [ABSTRACT FROM AUTHOR]

In the context of a globalized world where travel enthusiasts seek personalized recommendations for their favourite destinations, the study delves into sentiment analysis and travel recommendation systems. While previous research has explored various aspects of tourism destination selection, this work explores the use of star ratings to create sentiment lexicons tailored to specific domains. However, a notable limitation is the absence of a comprehensive investigation into the effectiveness of sentiment analysis techniques, in conjunction with star ratings, in accurately capturing review sentiment. This article aims to address this limitation by introducing a novel model that combines explicit sentiment scores and star ratings to predict optimal travel destinations based on user preferences. The model collects data from TripAdvisor but faces challenges related to noisy and non-informative elements such as HTML tags. To streamline the categorization process, preprocessing techniques like tokenization, stemming, and stop-word removal are applied. The study leverages Latent Dirichlet Allocation (LDA) topic modelling to extract user choice topics from the collected review data. Additionally, Correlated Topic Modeling (CTM) is employed to capture correlations between latent topics. The Morphological Linear Neural Network (MLNN) model is introduced to generate sentiment scores for textual content. These scores are then combined with star ratings from reviews to determine the most suitable destination. Furthermore, the study predicts average cumulative ratings by considering projected emotion scores and star ratings through the cumulative gain model. Implementation is carried out using Python software on a dataset comprising 67,871 samples. Evaluation metrics, including an F1-score of 89%, precision of 86%, and recall of 87%, indicate high performance in sentiment classification. The model exhibits an accuracy of approximately 95% and an RMSE value of 0.287, affirming its efficiency in polarity classification. Comparative analyses against state-of-the-art methods demonstrate the proposed model's superiority in terms of accuracy, precision, and recall. The practical implications of this model are underscored by its successful implementation and impressive evaluation results, highlighting its potential for enhancing personalized travel recommendations. [ABSTRACT FROM AUTHOR]

Language models in recent decades make a huge step towards solving the tasks that previously could be done only by humans. Development of NLP area is different scopes gives an opportunity to solve domain specific tasks and transfer knowledge from learnt data towards the useful inferences based on that. This article provides the NLP model approach in specific legal domain. Additionally, this article explores performance of pre-training small models and its utilization and checks the scores on fine-tuned task of checking sentence similarities via SBERT. According to this articles it is proven that domainspecific pre-trained models can perform better results than generally trained language model. This article also provides the language model that is adopted to the Ukrainian legal domain. [ABSTRACT FROM AUTHOR]