Opinion Extraction Research Articles

A Comparative Analysis of Feature Extraction Techniques for Fake Reviews Detection

The current Internet era is characterized by the widespread circulation of ideas and viewpoints among users across many social media platforms, such as microblogging sites, personal blogs, and reviews. Detecting fake reviews has become a widespread problem on digital platforms, posing a major challenge for both consumers and businesses. Due to the ever-increasing number of online reviews, it is no longer possible to manually identify fraudulent reviews. Artificial intelligence (AI) is essential in addressing the problem of identifying fake reviews. Feature extraction is a crucial stage in detecting fake reviews, and successful feature engineering techniques can significantly improve the accuracy of opinion extraction. The paper compares five feature extraction methods for multiple opinion classification using Twitter on airline and Borderland game reviews. FastText with X-GBoost classifier outperformed all other techniques, achieving 94.10% accuracy on the airline dataset and 100% accuracy in Borderland game reviews.

MuSe-CarASTE: A comprehensive dataset for aspect sentiment triplet extraction in automotive review videos

MuSe-CarASTE: A comprehensive dataset for aspect sentiment triplet extraction in automotive review videos

Fusing semantic and syntactic information for aspect sentiment triplet extraction

Aspect Sentiment Triplet Extraction (ASTE) aims to extract aspect terms, sentiment polarity and opinion terms explaining the reason for the sentiment from a sentence in the form of triplets. Many existing studies model the context by graph neural networks to learn relevant information from the generated graphs. However, some sentences may have syntactic errors or lack significant grammar, which may lead to poor results on the dataset of the model. In this paper, we propose the Fusing Semantic and Syntactic Information for Aspect Sentiment Triplet Extraction (FSSI) model, which incorporates both syntactic structure and semantic relevance in the context. Specifically, we construct a syntactic graph convolutional network to obtain comprehensive syntactic structure information and a semantic graph convolutional network to obtain global semantic relevance of sentences using the self-attention mechanism. Furthermore, we concatenate the graph representations generated by the two graph convolution networks to obtain the final enhanced representation. Finally, we apply an effective inference strategy to extract triplets. Extensive experimental results on benchmark datasets show that our model outperforms state-of-the-art approaches.

Sentiment analysis of student feedback: A comparative study employing lexicon and machine learning techniques

Sentiment analysis of student feedback: A comparative study employing lexicon and machine learning techniques

Identifying the Housing Bubble Predictors in Asian Emerging Economy: Exploring a Novel Model Based on Opinions

This study seeks to identify core housing bubble predictors and empirically investigate a novel model for detecting housing bubbles in Malaysia. We engender new insights based on the extraction of real-time opinions of market players about housing bubble formation and collapse. We employ a practical approach using a comprehensive survey of opinions from relevant walks of life via the survey data analysis software, SmartPLS3. Results indicate that housing demand, economic growth outlook, and buyers’ expectations positively impact the housing bubble. This study’s present housing market condition, foreign investment, and government policies are otherwise insignificant. We found that the present housing condition, government policies, and economic growth outlook do not influence the impact of housing demand, buyers’ expectations, and foreign investment on the housing bubble. We employed a cross-sectional research design in this study to obtain our findings. This might have indirectly suppressed more robust findings, which may have been otherwise obtained in the case of a longitudinal study. We advocate that policymakers should not overlook the significant roles of housing demand, economic growth outlook, and buyers’ expectations of the housing bubble. We show that the present housing market condition has no significant moderating effect on the impact of buyers’ expectations, foreign investment, and housing demand on housing bubbles.

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.

Efficient Detection of Irrelevant User Reviews Using Machine Learning

User reviews such as SNS feeds and blog writings have been widely used to extract opinions, complains, and requirements about a given place or product from users’ perspective. However, during the process of collecting them, a lot of reviews that are irrelevant to a given search keyword can be included in the results. Such irrelevant reviews may lead to distorted results in data analysis. In this paper, we discuss a method to detect irrelevant user reviews efficiently by combining various oversampling and machine learning algorithms. About 35,000 user reviews collected from 25 restaurants and 33 tourist attractions in Ulsan Metropolitan City, South Korea, were used for learning, where the ratio of irrelevant reviews in the two kinds of data sets was 53.7% and 71.6%, respectively. To deal with skewness in the collected reviews, oversampling algorithms such as SMOTE, Borderline-SMOTE, and ADASYN were used. To build a model for the detection of irrelevant reviews, RNN, LSTM, GRU, and BERT were adopted and compared, as they are known to provide high accuracy in text processing. The performance of the detection models was examined through experiments, and the results showed that the BERT model presented the best performance, with an F1 score of 0.965.

Online media sentiment analysis for US oysters

AbstractSentiment analysis, a form of data analytics, utilises information from online discussions, reviews, and social media posts to assess consumer sentiments. This study utilised data collected from social media using online listening procedures to assess online sentiments on oysters from January 2019 through December 2022. The analysis utilises machine learning algorithms to extract consumer sentiments, opinions, and demands from online chatter from different online domains. The online sentiments are determined as positive, negative, or neutral based on their word choice, tone, and context. The information provided gives insights into perception, which is valuable information for oyster producers, seafood industry stakeholders, and marketers to identify consumer preferences and formulate appropriate strategies accordingly. The results suggest that while farmed oysters are gaining popularity, there are still some concerns and criticisms around the industry. Positive words associated with mentions of oysters in general include ‘great’, ‘love’, ‘delicious’, ‘enjoyed’, and ‘oyster bar’, while negative words associated with oysters include ‘water’, ‘raw oyster’, ‘bad’, and ‘not eat’. The overall percentage net sentiment associated with all oysters in the United States is positive at 63%. The net sentiment associated with wild oysters is positive, at 51%, and that of farmed oysters is 58%. The oyster industry could invest more in public education, sustainability, and water‐cleaning initiatives to improve its image. Utilising social media to monitor and shape public perception can help the industry address concerns and enhance oyster‐related sentiments, offering valuable insights for marketing and sales strategies.

Machine learning-based opinion extraction approach from movie reviews for sentiment analysis

Machine learning-based opinion extraction approach from movie reviews for sentiment analysis

A robust approach for aspect-based sentiment analysis using deep learning and domain ontologies

PurposeThe purpose of this research study is to improve sentiment analysis (SA) at the aspect level, which is accomplished through two independent goals of aspect term and opinion extraction and subsequent sentiment classification.Design/methodology/approachThe proposed architecture uses neighborhood and dependency tree-based relations for target opinion extraction, a domain–ontology-based knowledge management system for aspect term extraction, and deep learning techniques for classification.FindingsThe authors use different deep learning architectures to test the proposed approach of both review and aspect levels. It is reported that Vanilla recurrent neural network has an accuracy of 83.22%, long short-term memory (LSTM) is 89.87% accurate, Bi-LSTM is 91.57% accurate, gated recurrent unit is 65.57% accurate and convolutional neural network is 82.33% accurate. For the aspect level analysis, ρaspect comes out to be 0.712 and Δ2aspect is 0.384, indicating a marked improvement over previously reported results.Originality/valueThis study suggests a novel method for aspect-based SA that makes use of deep learning and domain ontologies. The use of domain ontologies allows for enhanced aspect identification, and the use of deep learning algorithms enhances the accuracy of the SA task.

Opinion Extraction using Hybrid Learning Algorithm with Feature Set Optimization Approach

Evolution in engineering and technology added large size of data storage and transmission through the web application over the internet. This huge amount of data primarily used for exchange of information in between users and devices and in secondary aspects it has utilization as feedback, ratings and reviews that is supporting in generation of useful information of products, services, incidents etc. The data as opinion, feedback, view & suggestion is explored, organized & analyzed for selection of appropriate options. Sentiment analysis using the opinion extraction is a challenging task that is based on feature extraction and the concepts of Natural Language Processing that is applied in identification of the opinions of a user in terms of positive, neutral or negative ratings hidden in the form of comments typed as the text. Presently many data-processing based feature evaluation techniques for opinion extraction are used for solving the issues faced under sentiment classification applications. This article is based on development and application of algorithms for opinion extraction from text data available on web resources by K-Nearest Neighbor (KNN), Support vector machine (SVM) and hybrid of both named as SVM+KNN for classification of multi-label opinions from extracted text from review data of Twitter and Amazon. The performance of all the classification models (KNN, SVM and SVM+KNN) on both datasets is evaluated in terms of different parameters.

SAER : Comparison of Rule Prediction Algorithms on Constructing a Corpus for Taxation Related Tweet Aspect-Based Sentiment Analysis

Twitter is a popular social media in Indonesia, and sentiment analysis on Twitter has an important role in measuring public trust, especially in taxation issues. Aspect extraction is an important task in sentiment analysis. In this research, we propose SAER, a Syntactic Aspect-opinion Extraction and Rule prediction, that used language rule-based approach using syntactic features for aspect and opinion extraction, and we compare several algorithm for rule prediction such as Random Forest Regression, Decision Tree Regression, K-Nearest Neighbor Regression (KNN), Linear Regression, Support Vector Regression (SVR), and Extreme Gradient Boosting Regression (XGBoost) that can generate rules with a tree-based approach. By employing syntactic features and rule prediction, it has been able to explore important features in a sentence. In rule prediction, comparison results show that Support Vector Regression (SVR) was identified as the most effective model for aspects rule prediction, providing the best results with a Mean Squared Error (MSE) of 0.022, Root Mean Squared Error (RMSE) of 0.150, and Mean Absolute Error (MAE) of 0.123. While XGBoost was identified as the most effective model for opinions rule prediction, with MSE of 0.013, RMSE of 0.117, and MAE of 0.075. Since we used syntactic feature-based approaches and rule prediction in this work, it is expected to be implemented for other cases, with other domain datasets.

Opinion Extraction using Hybrid Learning Algorithm with Feature Set Optimization Approach

Evolution in engineering and technology added large size of data storage and transmission through the web application over the internet. This huge amount of data primarily used for exchange of information in between users and devices and in secondary aspects it has utilization as feedback, ratings and reviews that is supporting in generation of useful information of products, services, incidents etc. The data as opinion, feedback, view & suggestion is explored, organized & analyzed for selection of appropriate options. Sentiment analysis using the opinion extraction is a challenging task that is based on feature extraction and the concepts of Natural Language Processing that is applied in identification of the opinions of a user in terms of positive, neutral or negative ratings hidden in the form of comments typed as the text. Presently many data-processing based feature evaluation techniques for opinion extraction are used for solving the issues faced under sentiment classification applications. This article is based on development and application of algorithms for opinion extraction from text data available on web resources by K-Nearest Neighbor (KNN), Support vector machine (SVM) and hybrid of both named as SVM+KNN for classification of multi-label opinions from extracted text from review data of Twitter and Amazon. The performance of all the classification models (KNN, SVM and SVM+KNN) on both datasets is evaluated in terms of different parameters.

Assessing Data Quality in the Age of Digital Social Research: A Systematic Review

While survey data has long been the focus of quantitative social science analyses, observational and content data, although long-established, are gaining renewed attention; especially when this type of data is obtained by and for observing digital content and behavior. Today, digital technologies allow social scientists to track “everyday behavior” and to extract opinions from public discussions on online platforms. These new types of digital traces of human behavior, together with computational methods for analyzing them, have opened new avenues for analyzing, understanding, and addressing social science research questions. However, even the most innovative and extensive amounts of data are hollow if they are not of high quality. But what does data quality mean for modern social science data? To investigate this rather abstract question the present study focuses on four objectives. First, we provide researchers with a decision tree to identify appropriate data quality frameworks for a given use case. Second, we determine which data types and quality dimensions are already addressed in the existing frameworks. Third, we identify gaps with respect to different data types and data quality dimensions within the existing frameworks which need to be filled. And fourth, we provide a detailed literature overview for the intrinsic and extrinsic perspectives on data quality. By conducting a systematic literature review based on text mining methods, we identified and reviewed 58 data quality frameworks. In our decision tree, the three categories, namely, data type, the perspective it takes, and its level of granularity, help researchers to find appropriate data quality frameworks. We, furthermore, discovered gaps in the available frameworks with respect to visual and especially linked data and point out in our review that even famous frameworks might miss important aspects. The article ends with a critical discussion of the current state of the literature and potential future research avenues.

Meta heuristic approaches for sentiment analysis

AbstractOn the Internet, online microblogging and social networks have experienced tremendous growth. Millions of individuals share opinions on social networking platforms including Twitter, Facebook, YouTube, and Microblogging sites. Millions of individuals express their thoughts on social platforms namely Twitter, Facebook, YouTube, and microblogging sites. As user‐generated content is growing to a huge extent on the internet, the analysis, extraction identification and classification of opinion and sentiment polarity of different aspects has now become a challenging issue in sentiment analysis. In sentiment analysis, aspect level opinion mining, opinion spam review detection, extraction and representation of most relevant accessed data from the user‐generated data is a difficult process. The word vector representation using existing deep learning techniques affects the performance in aspect level opinion mining. Spammers propagate false opinion for payment exchange, which leads to degradation of the financial growth, business, and fame of the organization. To overcome the limitations aspect level opinion mining is used to recognize sentiment and polarity of aspects in the specified text. This work presents various input feature vectors of deep learning approaches aiming to improve performance in aspect level opinion mining. Next, three meta‐heuristic algorithms with k‐means clustering approach for classification of opinion spam reviews on social media transit tweets is presented. The proposed approaches are evaluated and compared with other existing approaches as well as other benchmark datasets namely Restaurant and Amazon reviews.

Harnessing the power of real-time forum opinion: Unveiling its impact on stock market dynamics using intraday high-frequency data in China

Harnessing the power of real-time forum opinion: Unveiling its impact on stock market dynamics using intraday high-frequency data in China

Chinese text dual attention network for aspect-level sentiment classification.

English text has a clear and compact subject structure, which makes it easy to find dependency relationships between words. However, Chinese text often conveys information using situational settings, which results in loose sentence structures, and even most Chinese comments and experimental summary texts lack subjects. This makes it challenging to determine the dependency relationship between words in Chinese text, especially in aspect-level sentiment recognition. To solve this problem faced by Chinese text in the field of sentiment recognition, a Chinese text dual attention network for aspect-level sentiment recognition is proposed. First, Chinese syntactic dependency is proposed, and sentiment dictionary is introduced to quickly and accurately extract aspect-level sentiment words, opinion extraction and classification of sentimental trends in text. Additionally, in order to extract context-level features, the CNN-BILSTM model and position coding are also introduced. Finally, to better extract fine-grained aspect-level sentiment, a two-level attention mechanism is used. Compared with ten advanced baseline models, the model's capabilities are being further optimized for better performance, with Accuracy of 0.9180, 0.9080 and 0.8380 respectively. This method is being demonstrated by a vast array of experiments to achieve higher performance in aspect-level sentiment recognition in less time, and ablation experiments demonstrate the importance of each module of the model.

SENTIMENT ANALYSIS OF WEB 3.0 ENABLED TWITTER DATASET

In this paper, the focus is on sentiment analysis of web 3.0 enabled twitter dataset. The objective of the project is to explore various methods for performing sentiment analysis on Twitter datasets and implementing these methods on the web3.0 Twitter platform. The project involves collecting Twitter data through blockchain-based applications, preprocessing the data to remove noise, and applying machine learning models for sentiment analysis. Sentiment analysis is simply the extraction of thoughts, ideas, opinions, and emotions from sources such as text, speech, tweets, and databases using natural language processing (NLP) This process involves text segmentation mentally makes it "good," "bad," and "neutral" groups. In addition, it is known by other terms such as objective evaluation, mindfulness mining, and rating extraction. Web 3.0, also known as Web3, represents the third contemplated iteration of the World Wide Web, which aspires to establish a connected, transparent and intelligent online environment Based on the concept of decentralization, blockchain technology and the implementation of token-based economies. The main outcome of the project is to gain insights into the sentiment of users by analyzing WEB3.0 enabled Twitter data. By implementing sentiment analysis techniques on a WEB3.0 enabled Twitter dataset, the project aims to contribute to the field of sentiment analysis and showcase the effectiveness of using WEB3.0 enabled for data collection and analysis. The project aims to provide valuable insights for various methods of sentiment analysis for researchers. Keywords – Sentiment Analysis, Blockchain- Enabled, Twitter data, WEB 3.0, Machine Learning Models, User Sentiment, Research Contribution

Sentiment Analysis of Public Opinion Towards Tourism in Bangkalan Regency Using Naïve Bayes Method

Sentiment analysis is natural language processing (NLP) that uses text analysis to recognize and extract opinions in text. Analysis is used to convert unstructured information into more structured information, also to determine whether an object has a positive, negative, or neutral tendency, and is an effort to facilitate decision making for tourism managers as a recommendation in developing tourist attractions. In this study, opinions were conducted on tourism reviews in Bangkalan using the Naïve Bayes method. This method is a machine learning algorithm to classify text into concepts that are easy to understand and provide accurate results with high efficiency. This method is proven to provide excellent results with a high level of accuracy, especially for large data, but has some drawbacks, sensitive to feature selection. Thus, a feature selection process is needed to improve classification efficiency by reducing the amount of data analyzed, with the Information Gain feature selection method. The word weighting method uses TF-IDF, while the data used comes from google maps reviews taken through web scraping, where tourist visitors provide reviews and ratings of places that have been visited. However, the large number of reviews can make it difficult for tourist attractions managers to manage them, so the process of labeling the sentiment class of the review data obtained 3649 reviews, with 2583 positive, 275 negative, and 457 neutral. Based on the test results that have been carried out using the Information Gain threshold of 0.0001, 0.0003, and 0.0007 can improve the accuracy of the Naïve Bayes model, for the best test at threshold 0.0007, with an accuracy value of 78.68%, precision 80.44%, recall 82.59%, and f1-score 82.53%, from the test results it shows that the use of information gain feature selection and SMOTE technique has a fairly good performance in classifying public opinion sentiment data on tourism in Bangkalan Regency, meaning that tourism management is good seen from the results of visitor satisfaction sentiment.

텍스트 분석을 활용한 회계 연구의 현황과 전망

[Purpose] The purpose of this study is to examine prior literature using text mining analysis techniques in the accounting area for identifying current research themes, to present other data analysis techniques, and to provide directions for future research and practice.
 [Methodology] For this study, I use the Systematic Literature Review(SLR) methodology adopted in Schmitz and Leoni(2019). The results are obtained by entering keywords of “text analysis’ and “accounting” in Google Scholar, and the prior researches reviewed in this paper are selected among them and their references, mainly with the papers published in major journals.
 [Findings] Sicne the 1990s overseas, and the 2000s in Korea, studies using text analysis in the accounting area have been started. However, there are not enough numbers of studies using the text mining analysis in the accounting, and the journals where these researches published are also limited. I find that most of the studies examine the relationship between quantitative information of financial statements and stock market responses using text analysis. It is also found that business/ audit/annual reports, sustainability reports, and disclosure data are used at the most. The data analysis techniques used in these studies are mostly opinion extraction, tone analysis, sentiment analysis, and frequency analysis, which investigates the amount of words or tone of data. In future business environments, non－financial and descriptive information such as sustainability reports, strategic reports and MD&A are expected to provide more useful information for decision－making, therefore, various types of data analysis techniques are needed to be more actively used in accounting research.
 [Implications] This paper contributes to review current research focus in text mining related accounting studies, identify research gaps, present future avenues of research, and introduce various types of data analysis techniques related to text mining. From a practical policy perspective, it is meaningful to suggest an improvement in the DART system and to drive efforts to establish XBRL, so that the text analysis studies can be more activated.