Sentiment Analysis Of Online Reviews Research Articles

Sentiment analysis of online reviews of energy-saving products based on transfer learning and LBBA model

With the exacerbation of global climate change and the growing environmental awareness among the general public, the concept of green consumption has gained significant attention across various sectors of society. As a representative example of green consumer products, energy-saving products play a crucial role in the timely realization of dual carbon goals. However, an analysis of online comments regarding energy-saving products reveals that the majority of these products still exhibit shortcomings in terms of efficacy, noise level, cost-effectiveness, and particularly, energy-saving appliances. This study focuses on the user-generated online comments data from the Taobao e-commerce platform for Grade 1 energy-saving refrigerators. By employing text mining techniques, the study aims to extract the essential information and sentiments expressed in the comments, in order to explore the consumption characteristics of Grade 1 energy-saving refrigerators. Moreover, the LBBA (LDA-Bert-BiLSTM-Attention) model is utilized to investigate the consumer topics of interest and emotional features. Initially, the LDA model is adopted to identify the attributes and weights of consumer concerns. Subsequently, the Bert model is pre-trained with the online comment data, and combined with the BiLSTM algorithm and Attention mechanism to predict sentiment categories. Finally, a transfer learning approach is utilized to determine the sentiment inclination of user-generated online comments and to identify the primary driving factors behind each sentiment category. This research employs sentiment analysis on online comments data regarding energy-saving products to uncover consumer sentiment attributes and emotional characteristics. It provides decision-makers with a comprehensive and systematic understanding of public consumption intentions, offering decision support for the efficient operation and management of the energy-saving product market.

Theme Exploration and Sentiment Analysis of Online Reviews of Wuyishan National Park

The study aimed to explore the relationship and interaction between humans and nature in specific areas. Latent Dirichlet allocation topic recognition and SnowNLP sentiment analysis were used to extract the topics and analyze the sentiments from visitors’ online reviews of Wuyishan National Park. The conclusions were as follows: (1) The tourists mainly expressed positive emotions toward Wuyishan National Park, and the tourists acknowledged its ecological environment and natural and cultural heritage value. (2) The tourists’ comments focused on four themes: tourism activities and facilities, natural and cultural heritage value, characteristic tourism products, and tourism management and services. Natural experience was the main tourism activity in Wuyishan National Park, while cultural activities were related to the tea culture. (3) The tourist facilities, ticket and reservation mechanism, and management and services of Wuyishan National Park were the main concerns of the tourists. The study suggested that Wuyishan National Park could be transformed from a tourist destination into a comprehensive national park that provides recreational experiences and environmental education. This should be conducted by (1) developing detailed natural and cultural education and experience products and (2) improving public service functions and enhancing the public welfare of the national park.

A novel B&B recommendation method based on improved intuitionistic fuzzy sets

PurposeThis paper aims to propose a bed and breakfast (B&B) recommendation method that takes into account review timeliness and user preferences to help consumers choose the most satisfactory B&B.Design/methodology/approachThis paper proposes a B&B ranking method based on improved intuitionistic fuzzy sets. First, text mining and cluster analysis are combined to identify the concerns of consumers and construct an attribute set. Second, an attribute-level-based text sentiment analysis is established. The authors propose an improved intuitionistic fuzzy set, which is more in line with the actual situation of sentiment analysis of online reviews. Next, subjective-objective combinatorial assignments are applied, considering the consumers’ preferences. Finally, the vlsekriterijumska optimizacija i kompromisno resenje (VIKOR) algorithm, based on the improved score function, is advised to evaluate B&Bs.FindingsA case study is presented to illustrate the use of the proposed method. Comparative analysis with other multi-attribute decision-making (MADM) methods proves the effectiveness and superiority of the VIKOR algorithm based on the improved intuitionistic fuzzy sets proposed in this paper.Originality/valueProposing a B&B recommendation method that takes into account review timeliness and user customization is the innovation of this paper. In this approach, the authors propose improved intuitionistic fuzzy sets. Compared with the traditional intuitionistic fuzzy set, the improved intuitionistic fuzzy set increases the abstention membership, which is more in line with the actual situation of attribute-level sentiment analysis of online reviews.

Which Behaviors Generate The Best Reviews? A Sentiment Analysis of Online Reviews on AOSSM Surgeons

Background Online surgeon reviews can significantly influence a patient’s selection of a provider, and are important in the movement towards quality-based physician compensation models. Written reviews, however, are subjective and are thus difficult to quantitatively analyze. Sentiment analysis using artificial intelligence (AI) provides the ability to quantitatively assess surgeon reviews to provide actionable feedback. Purpose The objective of this study is to quantitatively analyze the online written reviews of AOSSM surgeons utilizing sentiment analysis and report trends in the most frequently used words in the best and worst reviews. Study Design Cross-sectional study using publicly-available online reviews Methods Online reviews and star-ratings of AOSSM surgeons were obtained from healthgrades.com and zocdoc.com. A sentiment analysis algorithm was used to compute sentiment analysis scores of each written review. Sentiment scores were validated against star-ratings. Positive and negative word and word-pair frequency analysis was performed to identify common items associated with high and low scores. A multiple logistic regression was run on clinically relevant phrases. Results Following the inclusion and exclusion criteria, 18,386 AOSSM surgeon reviews were analyzed for 2071 surgeons. There was no significant difference in sentiment scores by provider gender. Surgeons who are younger than 50 years old had more positive reviews (mean sentiment = +0.536 versus +0.458, p < 0.01). The most frequently used and meaningful bi-grams used to describe top-rated surgeons are words correlating with kindness, caring personalities, and efficiency in pain management; whereas, those with the worst reviews are often characterized as unable to relieve the pain of their patients. The multiple logistic regression was significant for several clinically relevant words that confer greater or less odds of an improved score. Pain is significantly correlated with a decreased odds of receiving a positive review and positive behavioral factors confer a greater odds of receiving a positive review. Conclusion Sentiment analysis provides a means of quantifying written reviews of surgeons, and analysis of the reviews. This study provides insight into factors contributing to positive reviews, especially surgeon confidence, staff friendliness, warm disposition, and pain relief. Clinical Relevance This study delineates factors that impact the public reviews on AOSSM providers.

Sentiment analysis of online reviews for electric vehicles using the SMAA-2 method and interval type-2 fuzzy sets

Numerous electric vehicles (EVs) consumers express their opinions about EVs on automotive websites. These online reviews can be mined and analyzed to understand consumer sentiment and preferences. Although the existing sentiment analysis (SA) has made insight into sentiment polarity of EVs, the SA results on different EV attributes do not consider the accuracy of sentiment classification and the difficulty of accurately obtaining attribute weights in the process of all attribute information fusion. To address these issues, we provide an integrated framework for the EV SA problem using the stochastic multi-criteria acceptability analysis (SMAA) method with interval type-2 fuzzy sets (IT2FSs). We first use a logistic regression model to classify the sentiment polarity for each online comment and construct IT2FS by sentiment classification accuracy to represent the SA results for different EV attributes. Based on this, we considered all feasible weights in the weight space and utilize the SMAA-IT2FS method to aggregate SA results and achieve EV ranking. In addition, our approach provide decision makers with information to assist in decision-making such as the use of central weight vectors to identify the strengths and weaknesses of EVs and the analysis of potential competitors through dominance relationships. Finally, real data extracted from AutoHome websites are used as experimental data to illustrate the implementation of the proposed method and to demonstrate the effectiveness of our approach.

Evaluating Customer and Employee Satisfaction at Luna Park: A Sentiment Analysis of Online Reviews and Internal Feedback

Evaluating Customer and Employee Satisfaction at Luna Park: A Sentiment Analysis of Online Reviews and Internal Feedback

Modeling and Sentiment Analysis of Online Reviews in Hospitality Industry

With the great extent of use of smartphones and the internet, the online hotel booking service providers have excessively increased thus producing more user-generated content in the form of reviews and comments about the customer experience. These reviews of visited customer’s aids hotel management personnel not only to forecast the future demand but also to implement effective strategies for better service.
 It is becoming a tuff job in this scenario for the hotel management to get exact information from the wide range of reviews.
 In this analysis, it is to identify the classification of the sentiment from the customer reviews. The classification can be done with text mining approach with the source of information. Two dictionaries are developed for the usage of data classification around 431 reviews taken from Tripadvisor.com and Booking.com. Finally Latent Dirichent Allocation (LDA) modeling algorithm is applied to identify related topics and it was used to sort out the issues in consumer sentiment analysis. 
 Study findings revealed that majority of the reviews were with positive sentiments and the topics found best with hospitality domain and sentiment term were such as “food”, “hospitality”, “room”, “people”, “friendly” , “Relax”, “feelings”, and “holiday” as hospitality terms and “Strong Positive” and “Ordinary Positive” as sentiment terms.

Restaurant survival prediction using customer-generated content: An aspect-based sentiment analysis of online reviews

Business failure prediction or survival analysis can assist corporate organizations in better understanding their performance and improving decision making. Based on aspect-based sentiment analysis (ABSA), this study investigates the effect of customer-generated content (i.e., online reviews) in predicting restaurant survival using datasets for restaurants in two world famous tourism destinations in the United States. ABSA divides the overall review sentiment of each online review into five categories, namely location, tastiness, price, service, and atmosphere. By employing the machine learning–based conditional survival forest model, empirical results show that compared with overall review sentiment, aspect-based sentiment for various factors can improve the prediction performance of restaurant survival. Based on feature importance analysis, this study also highlights the effects of different types of aspect sentiment on restaurant survival prediction to identify which features of online reviews are optimal indicators of restaurant survival.

An integrated method for product ranking through online reviews based on evidential reasoning theory and stochastic dominance

Online reviews play an important role in consumers’ purchasing decisions. However, many online reviews confuse consumers when they wish to make a purchase but lack experience. To solve the problem of product ranking based on online reviews, two important issues must be addressed: sentiment analysis and product ranking based on multi-criteria decision-making (MCDM) methods. Therefore, this paper proposes an integrated MCDM method for product ranking through online reviews based on evidential reasoning (ER) theory and stochastic dominance (SD) rules. First, online reviews are preprocessed to obtain product attributes and weight values. Then, we use naive Bayes (NB), logistic regression (LR), and support vector machines (SVM) for the sentiment analysis of online reviews, and the results of the three classifiers are aggregated using ER theory. In addition, according to the confidence distribution matrix of sentiment orientations, SD rules are used to determine the stochastic dominance relations between pairwise alternatives for each attribute. Furthermore, we use the stochastic multi-criteria acceptability analysis (SMAA)-PROMETHEE method to obtain the final product ranking results and conduct sensitivity analysis. Finally, a case study on ranking computer products from JD Mall through online reviews is provided to illustrate the validity of the proposed method.

Travellers’ perspectives on historic squares and railway stations in Italian heritage cities revealed through sentiment analysis

ABSTRACT This study undertakes sentiment analysis of online reviews of public exterior spaces – historic squares and railway stations – in popular destinations in Italy, with the aim of offering new perspectives of community engagement in urban design analysis. The experience of walking through urban spaces in Italian heritage cities is evaluated under indicators of place quality and connectivity, i.e., aesthetic perception, social interaction, body mobility, facilities and amenities, sense of safety, and destination loyalty. Such advanced analysis can reshape the way we interpret the thoughts and emotions of wider communities so that these are included in local place-focused development strategies.

How are Patients Describing You Online? A Natural Language Processing Driven Sentiment Analysis of Online Reviews on CSRS Surgeons.

A quantitative analysis of written, online reviews of Cervical Spine Research Society (CSRS) surgeons. This study quantitatively analyzes the written reviews of members of the CSRS to report biases associated with demographic factors and frequently used words in reviews to help aid physician practices. Physician review websites have influence on a patient's selection of a provider, but written reviews are subjective. Sentiment analysis of writing through artificial intelligence can quantify surgeon reviews to provide actionable feedback. Online written and star-rating reviews of CSRS surgeons were obtained from healthgrades.com. A sentiment analysis package was used to obtain compound scores of each physician's reviews. The relationship between demographic variables and average sentiment score of written reviews were evaluated through t -tests. Positive and negative word and bigram frequency analysis was performed to indicate trends in the reviews' language. In all, 2239 CSRS surgeon's reviews were analyzed. Analysis showed a positive correlation between the sentiment scores and overall average star-rated reviews ( r2 =0.60, P <0.01). There was no difference in review sentiment by provider sex. However, the age of surgeons showed a significant difference as those <55 had more positive reviews (mean=+0.50) than surgeons >=55 (mean=+0.37) ( P <0.01). The most positive reviews focused both on pain and behavioral factors, whereas the most negative focused mainly on pain. Behavioral attributes increased the odds of receiving positive reviews while pain decreased them. The top-rated surgeons were described as considerate providers and effective at managing pain in their most frequently used words and bigrams. However, the worst-rated ones were mainly described as unable to relieve pain. Through quantitative analysis of physician reviews, pain is a clear factor contributing to both positive and negative reviews of surgeons, reinforcing the need for proper pain expectation management. Level 4-retrospective case-control study.

Sentiment Analysis of online reviews based on LDA and AP-Bert model

The purpose of this paper is to construct a more accurate behavior matrix by using fine-grained aspect level emotion analysis method. Firstly, LDA topic extraction model is used to extract the topic of online review text, and the concerned attributes are extracted. According to the characteristics of online comments, a BERT emotion analysis model with enhanced pooling was proposed. Activation function layer and max-average pooling layer were designed to solve the over-fitting problem of BERT model in the process of emotion analysis. Finally, by combining LDA extraction results and AP-Bert sentiment analysis results, the proportion matrix is obtained. Experimental results show that the accuracy, recall rate and AUC value of AP-Bert model are better than those of the same type of model and original BERT model.

Product selection based on sentiment analysis of online reviews: an intuitionistic fuzzy TODIM method

Online reviews contain a great deal of information about consumers' purchasing preferences, which seriously affects potential consumers' purchasing decisions. Using the online review data to help customers make purchasing decisions has become a concern of customers, which has theoretical and practical application value. Therefore, a product selection model is presented based on sentiment analysis combined with an intuitionistic fuzzy TODIM method. Firstly, the product features are extracted by the Apriori algorithm based on online reviews. The sentiment orientation and intensity of the sentiment words for the product features are identified by the lexicon-based sentiment analysis approach. Next, the sentiment orientation of the product features is represented by an intuitionistic fuzzy value. Then the intuitionistic fuzzy TODIM method is used to determine the ranking results of the alternative products. Finally, the case study of mobile phone selection is given to illustrate the proposed approach. The results show that the proposed method considers the online reviews’ sentiment orientation and intensity and the consumers’ gain and loss in the purchasing product process and is more reasonable than the previous research.

How Are Patients Reviewing Spine Surgeons Online? A Sentiment Analysis of Physician Review Website Written Comments.

A Sentiment Analysis of online reviews of spine surgeons. Physician review websites have significant impact on a patient's provider selection. Written reviews are subjective, but sentiment analysis through machine learning can quantitatively analyze these reviews. This study analyzes online written reviews of spine surgeons and reports biases associated with demographic factors and trends in words utilized. Online written and star-reviews of spine surgeons were obtained from healthgrades.com. A sentiment analysis package was used to analyze the written reviews. The relationship of demographic variables to these scores was analyzed with t-tests and word and bigram frequency analyses were performed. Additionally, a multiple regression analysis was performed on key terms. 8357 reviews of 480 surgeons were analyzed. There was a significant difference between the means of sentiment analysis scores and star scores for both gender and age. Younger, male surgeons were rated more highly on average (P < .01). Word frequency analysis indicated that behavioral factors and pain were the main contributing factors to both the best and worst reviewed surgeons. Additionally, several clinically relevant words, when included in a review, affected the odds of a positive review. The best reviews laud surgeons for their ability to manage pain and for exhibiting positive bedside manner. However, the worst reviews primarily focus on pain and its management, as exhibited by the frequency and multivariate analysis. Pain is a clear contributing factor to reviews, thus emphasizing the importance of establishing proper pain expectations prior to any intervention.

Aspect Based Opinion Mining on Hotel Reviews

Users often use online reviews to assess the quality of hotels according to their various attributes. In this study, a sentiment analysis of online reviews has been conducted using 11 predetermined attributes pertaining to hotels. Using this analysis, users’ overall assessments of hotels have been determined and summarized from reviews left for a group of various hotels. To identify words with similar meanings to the 11 predetermined hotel attributes, the Word2Vec method has been employed. Additionally, the FastText method has been used to detect words containing spelling errors. The sentiment analysis of the comments has been made by using three different methods belonging to two different approaches. These methods are: VADER method as dictionary-based approach, BERT and RoBERTa as machine learning approaches. Using these methods, the reviews have been evaluated in three categories as positive, negative, and neutral, and the quality score has been calculated. In addition, a software with a user-friendly graphical interface has been implemented in an effort to easily use all the methods used in this study.

A hybrid sentiment analysis approach using black widow optimization based feature selection

With the massive development of Internet technologies and e-commerce technology, people rely on the product reviews provided by users through web. Sentiment analysis of online reviews has become a mainstream way for businesses on e-commerce platforms to satisfy the customers. This paper proposes a novel hybrid framework with Black Widow Optimization (BWO) based feature reduction technique which combines the merits of both machine learning and lexicon-based approaches to attain better scalability and accuracy. The scalability problem arises due to noisy, irrelevant and unique features present in the extracted features from proposed approach, which can be eliminated by adopting an effective feature reduction technique. In our proposed BWO approach, without changing the accuracy (90%), the feature-set size is reduced up to 43%. The proposed feature selection technique outperforms other commonly used Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) based feature selection techniques with reduced computation time of 21 sec. Moreover, our sentiment analysis approach is analyzed using performance metrics such as precision, recall, F-measure, and computation time. Many organizations can use these online reviews to make well-informed decisions towards the users’ interests and preferences to enhance customer satisfaction, product quality and to find the aspects to improve the products, thereby to generate more profits.

Deriving customer preferences for hotels based on aspect-level sentiment analysis of online reviews

An increasing number of travelers like to share their experience and feelings about hotel stays through social media, generating a sheer volume of online hotel reviews. The user-generated comments contain their preferences for different aspects of hotels, which are helpful for hoteliers to improve hotels’ services. The key of deriving customer preferences from online hotel reviews is to identify fine-grained sentiment towards hotel attributes. However, the existing fine-grained sentiment analysis approaches cannot address the implicit aspect-level terms extraction very well, which is necessary to deal with the common situation that some aspects are omitted in the online reviews. To better understand customer preferences, we propose an unsupervised approach for aspect-level sentiment analysis with the implicit hotel attributes into consideration by integrating word embedding, co-occurrence and dependency parsing. A method based on overall sentiment values of hotel attributes is used to measure the customer preferences to support the hotel services analysis. Finally, online hotel reviews crawled from Ctrip.com are used to verify the proposed approach, and the results show that the hybrid approach outperforms the individual included techniques with respect to the sentiment classification performance. The analysis of customer preference for Dalian Bayshore Hotel suggests that the hotel’s facility should be upgraded urgently, and different types of customers pay different attention to hotel attributes, such as price, hygiene, and location.

Customer preferences extraction for air purifiers based on fine-grained sentiment analysis of online reviews

Consumers are increasingly caring about air quality, and the air purifier market is facing fierce competition. In the era of e-commerce, it is possible to improve product design and enhance competitiveness by mining consumer demands for product functions from online reviews. However, the existing aspect-level sentiment analysis methods fail to deal with the over-segmentation of multi-word aspects, which is likely to cause the omission of aspects. Moreover, there is still a gap between sentiment analysis and demand recognition. To overcome these limitations, we propose an approach based on fine-grained sentiment analysis and the Kano model to extract consumer demands for product attributes from online reviews. Specifically, a recognition method based on part-of-speech rules is presented to identify multi-word product attributes. Inspired by the Kano model, extraction rules are designed to identify consumer demands for product attributes from the results of sentiment analysis. Finally, online reviews of air purifiers in Chinese market crawled from T-mall.com are used to illustrate the proposed approach. The correlation results show that there exists significantly positive correlation between product sales and the extracted attractive and one-dimensional product attributes. This indirectly demonstrates the effectiveness of the proposed method. Another dataset of refrigerators is further used to check the robustness of our proposed approach, and the results further demonstrate the effectiveness.

Consumer Product Recommendation by Sentiment Analysis of Online Reviews

Consumer Product Recommendation by Sentiment Analysis of Online Reviews

S2SAN: A sentence-to-sentence attention network for sentiment analysis of online reviews

Many existing attention-based deep learning approaches to sentiment analysis have focused on words and represent an entire review text as a word sequence. However, these approaches overlook the differences in the importance of each sentence to the complete text. To solve this problem, some work has been performed to calculate sentence-level attention, but these studies use the same approach that is applied to word-level attention, which leads to unnecessary sequential structures and increased complexity of sentence representation. Therefore, in this paper, we propose a sentence-to-sentence attention network11https://github.com/JingruiHou/S2SAN (S2SAN) using multihead self-attention. We conducted several domain-specific, cross-domain and multidomain sentiment analysis experiments with real-world datasets. The experimental results show that S2SAN outperforms other state-of-the-art models. Some classical sentiment classifiers [e.g., convolutional neural network (CNN), recurrent neural network (RNN), and long short-term memory (LSTM) models] achieve better accuracies when they are reconfigured to include sentence-to-sentence attention.