Predicting the helpfulness score of online reviews using convolutional neural network

Cultivation strategies of English thinking ability in the environment of Internet of Things

The study aims to broaden the horizons of English learners and solve the problem of insufficient cultivation of English thinking. With the widespread use of the Internet of Things (IoT) and from the perspective of deep learning, the Local Similar Convolutional Neural Network (LSNN) recommendation model is designed by adding adjustment layers to the Convolutional Neural Network (CNN). The LSNN model alleviates the sparsity of data. Through comparative experiments on related data, the data sparsity is 0.7-0.9. The LSNN prediction is higher than that of Euclidean Distance and Pearson correlation coefficient, which proves that LSNN can alleviate data sparsity. The LSNN model has the lowest mean absolute error (MAE) of 0.83, which is smaller than the previous CNN's MAE. The LSNN model recommends the expansion books they need most for English learners, and then improves the vision of English learners, thereby strengthening the cultivation of English learners' thinking ability.

Identification of significant features and machine learning technique in predicting helpful reviews

Consumers nowadays rely heavily on online reviews in making their purchase decisions. However, they are often overwhelmed by the mass amount of product reviews that are being generated on online platforms. Therefore, it is deemed essential to determine the helpful reviews, as it will significantly reduce the number of reviews that each consumer has to ponder. A review is identified as a helpful review if it has significant information that helps the reader in making a purchase decision. Many reviews posted online are lacking a sufficient amount of information used in the decision-making process. Past research has neglected much useful information that can be utilized in predicting helpful reviews. This research identifies significant information which is represented as features categorized as linguistic, metadata, readability, subjectivity, and polarity that have contributed to predicting helpful online reviews. Five machine learning models were compared on two Amazon open datasets, each consisting of 9,882,619 and 65,222 user reviews. The significant features used in the Random Forest technique managed to outperform other techniques used by previous researchers with an accuracy of 89.36%.

A Text Analytics Framework for Performance Assessment and Weakness Detection From Online Reviews

Present research proposes a framework that integrates aspect-level sentiment analysis with multi-criteria decision making (TOPSIS) and control charts to uncover hidden quality patterns. While sentiment analysis quantifies consumer opinions corresponding to various product features, TOPSIS uses the sentiment scores to rank manufacturers based on their relative performance. Finally, U and P control charts assist in discovering the weak aspects and corresponding attributes. To extract aspect-level sentiments from reviews, we developed the ontology of passenger cars and designed a heuristic that connects the opinion-bearing texts to the exact automobile attribute. The proposed framework was applied to a review dataset collected from a well-known car portal in India. Considering five manufacturers from the mid-size car segment, we identified the weakest and discovered the aspects and attributes responsible for its perceived weakness.

An Approach Combining DEA and ANN for Hotel Performance Evaluation

For a hotel to succeed in the long run, it becomes vital to achieve higher profits along with increased performance. The performance evaluation of a hotel can signify its sustainable competitiveness within the hospitality industry. This article performs a two-stage study that combines data envelopment analysis (DEA) and artificial neural network (ANN) to evaluate hotel performance. The first stage to evaluate the efficiency for hotels is by using the DEA technique. The input variables considered are the number of rooms and the ratings corresponding to six aspects of a hotel (service, room, value, location, sleep quality, and cleanliness). Also, revenue per available room (RevPAR) and customer satisfaction (CS) are the output variables. The distinguishing factor of this article is that it involves the use of EWOM for performance evaluation. In the second stage, the performance of the hotels is judged by using the ANN technique. The ANN results showed that the performance of the hotels is quite good. Finally, discussions based on the results and scope for future studies are provided.

Profiling and Predicting the Cumulative Helpfulness (Quality) of Crowd-Sourced Reviews

With easy access to the Internet and the popularity of online review platforms, the volume of crowd-sourced reviews is continuously rising. Many studies have acknowledged the importance of reviews in making purchase decisions. The consumer’s feedback plays a vital role in the success or failure of a business. The number of studies on predicting helpfulness and ranking reviews is increasing due to the increasing importance of reviews. However, previous studies have mainly focused on predicting helpfulness of “reviews” and “reviewer”. This study aimed to profile cumulative helpfulness received by a business and then use it for business ranking. The reliability of proposed cumulative helpfulness for ranking was illustrated using a dataset of 1,92,606 businesses from Yelp.com. Seven business and four reviewer features were identified to predict cumulative helpfulness using Linear Regression (LNR), Gradient Boosting (GB), and Neural Network (NNet). The dataset was subdivided into 12 datasets based on business categories to predict the cumulative helpfulness. The results reported that business features, including star rating, review count and days since the last review are the most important features among all business categories. Moreover, using reviewer features along with business features improves the prediction performance for seven datasets. Lastly, the implications of this study are discussed for researchers, review platforms and businesses.