Personalized ranking for video games based on online reviews: An S-Kano-TOPSIS method integrating requirement categories and public opinion

As one of the ways to reflect the views of the masses in modern society, online reviews have great value in public opinion research. The analysis of potential public opinion information from online reviews has a certain value for the government to clarify the next work direction. In this paper, the event evolution graph is designed to make COVID-19 network public opinion prediction. The causal relationship was extracted in the network reviews after the COVID-19 incident to build an event evolution graph of COVID-19 and predict the possibility of the occurrence of the derivative public opinion. The research results show the hot events and evolution direction of COVID-19 network public opinion in a clear way, and it can provide reference for the network regulatory department to implement intervention.

Our goal is to be able to answer the questions that have arisen because of our research, "Which characteristics make internet reviews useful?" To address this issue, we used douban.com, a prominent consumer movie review site, to gather online movie reviews. This research creates an internet effect factor reviews framework using data mining methods. The findings show that article qualities do have an impact on users' judgments of intervention effectiveness. The duration of online reviews, optimistic attitude, severe emotional inclination, and review reliability all have a significant beneficial influence on the effectiveness of reviews. Moreover, happy emotions are more beneficial and powerful than bad emotions. The findings also imply that review number moderates the association between online reviews’ high emotional inclination and their effectiveness. The research gives insight into how people assess online reviews, and the results may aid professionals in developing methods that utilize reviews to forecast commercial success in the market.

Our goal is to be able to answer the questions that have arisen because of our research, "Which characteristics make internet reviews useful?" To address this issue, we used douban.com, a prominent consumer movie review site, to gather online movie reviews. This research creates an internet effect factor reviews framework using data mining methods. The findings show that article qualities do have an impact on users' judgments of intervention effectiveness. The duration of online reviews, optimistic attitude, severe emotional inclination, and review reliability all have a significant beneficial influence on the effectiveness of reviews. Moreover, happy emotions are more beneficial and powerful than bad emotions. The findings also imply that review number moderates the association between online reviews’ high emotional inclination and their effectiveness. The research gives insight into how people assess online reviews, and the results may aid professionals in developing methods that utilize reviews to forecast commercial success in the market.

Sentiment analysis, also known as opinion mining, is a computational technique that aims to identify and extract subjective information from text data, allowing us to understand and categorize the underlying sentiment expressed by individuals. With the exponential growth of social media, online reviews, and user generated content, sentiment analysis has gained significant attention as a valuable tool for businesses, researchers, and decision-makers to gain insights into public opinion, customers satisfaction, and market trends. The project is about searching the web for public opinion about people, movies, places, companies or any entities. The user of this software can type in a query that is any entity and the system mines the web for public opinion about that entity and generates a sentiment score. A positive score represents a positive public sentiment about that entity and negative score represents a negative sentiment or opinion.

Questionnaire-based surveys and on-line product reviews resemble each other in that they both have user comments and satisfaction ratings. Since a comment might be a general opinion about a product or only one or a set of its attributes, in which case the text might not reflect the rating, surveys and reviews share the problem of pairing free-text comments with these ratings. To train accurate models for automatic evaluation of products from free-text, it is important to distinguish these two kinds of opinions. In this paper we present experiments on detecting general opinions that target a product as a whole, thus, reflect the user sentiments better. The task is different from subjectivity detection, since the goal is to detect generality of an opinion regardless of the rest of the documents being opinionated or not. The task complements feature-based opinion analysis and opinion polarity classification, since it can be applied as a preceding step to both tasks. We show that when used as a classification feature user ratings are not useful in the general opinion detection task. However, they are effective in predicting the polarity of a comment once it is identified as a general opinion.

Online reviews are often the primary factor in a customer’s decision to purchase a product or service, and are a valuable source of information that can be used to determine public opinion on these products or services. Because of their impact, manufacturers and retailers are highly concerned with customer feedback and reviews. Reliance on online reviews gives rise to the potential concern that wrongdoers may create false reviews to artificially promote or devalue products and services. This practice is known as Opinion (Review) Spam, where spammers manipulate and poison reviews (i.e., making fake, untruthful, or deceptive reviews) for profit or gain. Since not all online reviews are truthful and trustworthy, it is important to develop techniques for detecting review spam. By extracting meaningful features from the text using Natural Language Processing (NLP), it is possible to conduct review spam detection using various machine learning techniques. Additionally, reviewer information, apart from the text itself, can be used to aid in this process. In this paper, we survey the prominent machine learning techniques that have been proposed to solve the problem of review spam detection and the performance of different approaches for classification and detection of review spam. The majority of current research has focused on supervised learning methods, which require labeled data, a scarcity when it comes to online review spam. Research on methods for Big Data are of interest, since there are millions of online reviews, with many more being generated daily. To date, we have not found any papers that study the effects of Big Data analytics for review spam detection. The primary goal of this paper is to provide a strong and comprehensive comparative study of current research on detecting review spam using various machine learning techniques and to devise methodology for conducting further investigation.

With the rapid development of the Internet and social media, public opinions and evaluations have been widely spread in cyberspace. As an important form of user feedback, online comments play a vital role in improving services and products. This study aims to evaluate tourists' satisfaction with the management of the Beijing Palace Museum scenic area by analyzing online comment data and to propose management optimization suggestions based on these findings. A large number of user comments from multiple online travel platforms and social media were collected through web crawler technology, and data were processed using text mining and sentiment analysis methods to identify key factors of tourist satisfaction. Research has found that tourists show varying degrees of satisfaction and dissatisfaction with the cultural heritage protection, tourism environment, service facilities, navigation system, and shopping and dining ex-perience of the Forbidden City. Based on these findings, this study proposes a series of optimization measures, including improving the tourist diversion mechanism, strengthening service personnel training, enhancing the moder-ni-zation level of facilities, enriching digital navigation content, and optimizing tourist experience design. In addition, the study also emphasizes the application prospects of big data and artificial intelligence technology in scenic area man-agement, in order to provide reference and inspiration for the management of the Forbidden City and other historical and cultural scenic areas. The con-clu-sions of this study contribute to promoting the sustainable development of the Forbidden City scenic area, improving tourist satisfaction, and enhancing its international competitiveness

Online reviews have become a significant factor in determining prices, complementing the intrinsic qualities of goods. In the art market, these reviews elevate the interaction between artists and connoisseurs, but the diverse levels of expertise and influence among participants demand a more detailed approach. This study investigates the impact of expert opinions and public sentiment on painting prices, using a hedonic regression model with artist-specific fixed effects. Given the common practice of buying art for investment in the secondary market, we also analyze the relationship between review sentiment and investment intentions. Based on a dataset of 18,100 sold paintings, we find that negative sentiment from all sources and positive public sentiment significantly influence prices. Moderation analysis shows heightened sensitivity to negative opinions from experts and media reports. This research contributes to understanding the interaction between social media and the art market, as well as key price determinants for artworks.

Consumers today shop online. They rely greatly on public opinion in the form of online reviews when making decisions. These online reviews contain both crucial and useful information (e.g., users’ experience, ratings, etc.), and they are presented in a variety of formats (pictures, videos, and/or text) with different degrees of valence. Consumers may be overwhelming by them and not sure which one they should believe between ones that they watch/hear “live” on videos or ones written by unidentified individuals. The presentation of reviews could also increase or decrease source credibility. This research examines the effects of online reviews in different formats and valence.

Online reviews for products, movies, shopping, etc. have become the major purchase decision of customers to make before buying the product. These reviews are kind of public opinion about the product which they bought and this may help another person who is willing to buy the product. The impact of reviews had made manufacturers, and retailers to be more concerned about making the product as a best one. Many retailers such as Reliance, Big Bazaar, More, etc are very concerned about online reviews and there are possibilities for these reviews to affect business either positively or negatively. Certain retailers are trying to create false reviews about the product through AI as a part of promoting the product. This process is termed the Opinion spam or opinion review spam here is where the reviewers manipulate a wrong review for selling the product for profit. Not all online reviews are trustworthy and truthful so in such cases, a model must be created for detecting online review spam. This research paper tries to highlight the online spam review detecting by machine learning approach using NLP (Natural Language Processing) where the extracted features from the text will be taken for further review. In this research main focus was shifted to the ML technique for detecting review spam, and classifying them. The main aim of the research is to provide a comprehensive ad strong ML approach for detecting review spam.

The article examines the gamification of politics, the process of integrating game elements and mechanics into the political sphere, as well as the use of video games as a tool for influencing public opinion. The subject of the study is video games as a sociocultural and political phenomenon, their role in shaping public opinion and political education. The purpose of the research is to study the role of video games as a tool for influencing public opinion. The authors aim to identify the mechanisms of gamification in politics, assess the potential of video games in manipulating public consciousness, and forecast some trends in the development of this sociocultural phenomenon. The research tasks include analyzing the theoretical aspects of political gamification, investigating video games as a tool for political communication, assessing their educational potential, and developing recommendations for promoting national interests. The main hypothesis of the research is that video games are not only an entertainment product but also a tool for influencing public opinion, capable of manipulating consciousness, political education, and preserving historical memory. The methodological foundation of the research is the theoretical analysis of domestic and foreign studies dedicated to public opinion and the gamification of politics. Qualitative methods are used, including case studies (when analyzing cases involving video games). The sources include academic articles, statistics from the gaming industry, official regulatory legal acts, and statements from Russian authorities regarding the regulation of the video game industry. The scientific novelty of the research lies in the comprehensive analysis of video games as a tool for shaping public opinion and political education. The article shows that video games contain political symbols, images, and discourses capable of engaging players and influencing their consciousness. The global video game market is estimated at $185 billion, and in Russia, 88 million people over 18 have tried playing video games at least once. Cases such as "Black Myth: Wukong" and "Immortal Symphony" demonstrate the educational potential of video games and their impact on cultural preferences. In conclusion, it is noted that video games are an important element of modern political communications and require government support to create competitive products, especially in the context of sanctions. It is recommended to develop mechanisms for controlling the content of video games and continue research on their impact on political communications.

Customer reviews provide helpful information such as usage experiences or critiques; these are critical information resource for future customers. Since the amount of online review is getting bigger, people need a way to find the most helpful ones automatically. Previous studies addressed on the prediction of the percentage of the helpfulness voting results based on a regression model or classified them into a helpful or unhelpful classes. However, the voting result of an online review is not a constant over time, and we also find that there are many reviews getting zero vote. Therefore, we collect the voting results of the same online customer reviews over time, and observe the change of votes to find a better learning target. We collected a dataset with online reviews in five different product categories (“Apple”, “Video Game”, “Clothing, Shoes & Jewelry”, “Sports & Outdoors”, and “Prime Video”) from Amazon.com with the voting result on the helpfulness of the reviews, and monitor the helpfulness voting for six weeks. Experiments are conducted on the dataset to get a reasonable classification on the zero and non-zero vote reviews. We construct a classification system that can classify the online reviews via the deep learning model BERT. The results show that the classifier can get good result on the helpfulness prediction. We also test the classifier on cross-domain prediction and get promising results.

Internet review sites allow consumers to write detailed reviews of products potentially containing information related to user experience (UX) and usability. Using 5198 sentences from 3492 online reviews of software and video games, we investigate the content of online reviews with the aims of (i) charting the distribution of information in reviews among different dimensions of usability and UX, and (ii) extracting an associated vocabulary for each dimension using techniques from natural language processing and machine learning. We (a) find that 13%-49% of sentences in our online reviews pool contain usability or UX information; (b) chart the distribution of four sets of dimensions of usability and UX across reviews from two product categories; (c) extract a catalogue of important word stems for a number of dimensions. Our results suggest that a greater understanding of users' preoccupation with different dimensions of usability and UX may be inferred from the large volume of self-reported experiences online, and that research focused on identifying pertinent dimensions of usability and UX may benefit further from empirical studies of user-generated experience reports.

Online reviews regarding different products or services have become the main source to determine public opinions. Consequently, manufacturers and sellers are extremely concerned with customer reviews as these have a direct impact on their businesses. Unfortunately, to gain profits or fame, spam reviews are written to promote or demote targeted products or services. This practice is known as review spamming. In recent years, the spam review detection problem has gained much attention from communities and researchers, but still there is a need to perform experiments on real-world large-scale review datasets. This can help to analyze the impact of widespread opinion spam in online reviews. In this work, two different spam review detection methods have been proposed: (1) Spam Review Detection using Behavioral Method (SRD-BM) utilizes thirteen different spammer's behavioral features to calculate the review spam score which is then used to identify spammers and spam reviews, and (2) Spam Review Detection using Linguistic Method (SRD-LM) works on the content of the reviews and utilizes transformation, feature selection and classification to identify the spam reviews. Experimental evaluations are conducted on a real-world Amazon review dataset which analyze 26.7 million reviews and 15.4 million reviewers. The evaluations show that both proposed models have significantly improved the detection process of spam reviews. Specifically, SRD-BM achieved 93.1% accuracy whereas SRD-LM achieved 88.5% accuracy in spam review detection. Comparatively, SRD-BM achieved better accuracy because it works on utilizing rich set of spammers behavioral features of review dataset which provides in-depth analysis of spammer behaviour. Moreover, both proposed models outperformed existing approaches when compared in terms of accurate identification of spam reviews. To the best of our knowledge, this is the first study of its kind which uses large-scale review dataset to analyze different spammers' behavioral features and linguistic method utilizing different available classifiers.

Detecting sentiment in online reviews is a key task, and effective analysis of sentiment in online reviews is the foundation of applications such as user preference modeling, consumer behavior monitoring, and public opinion analysis. In previous studies, the sentiment analysis task mainly relied on text content and ignored the effective modeling of visual information in comments. This paper proposes a neural network SFNN based on semantic feature fusion. The model first uses convolutional neural networks and attention mechanism to obtain the effective emotional feature expressions of the image, and then maps the emotional feature expressions to the semantic feature level. Then, the semantic features of the visual modal is combined with the semantic features of the text modal, and finally the emotional polarity of the comment is effectively analyzed by combining the emotional features of the physical level of the image. Feature fusion based on semantic level can reduce the difference of heterogeneous data. Experimental results show that our model could achieve better performance than the existing methods in the benchmark dataset.