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Product reviews are critical high-scope non-marketing cues for consumers to obtain useful product information from various perspectives. However, the effects of inconsistent reviews among multiple sources on new product sales are underexplored. Based on the cue-diagnosticity framework, this research investigates how multi-source review inconsistency (MSRI) affects purchase intention and new product sales in the context of the U.S. automobile industry when firms signal product quality through advertising as a low-scope cue. Using multi-methods of an experiment and secondary data analysis, the results show the negative effects of MSRI on purchase intention and new product sales and explain the mechanisms through information diagnosticity. Further, the findings show how advertising as a low-scope cue can mitigate the effect of multi-source inconsistency on new product sales.

Sentiment analysis is defined as the process of mining of data, view, review or sentence to Predict the emotion of the sentence through natural language processing (NLP) or Machine Learning Techniques. The sentiment analysis involve classification of text into three phase “Positive”, “Negative” or “Neutral”. The process of finding user Opinion about the topic or Product or problem is called as opinion mining. Analyzing the emotions from the extracted Opinions are defined as Sentiment Analysis. The goal of opinion mining and Sentiment Analysis is to make computer able to recognize and express emotion. Using social media, E-commerce website, movies reviews such as Face book, twitter, Amazon, Flipkart etc. user share their views, feelings in a convenient way. Sentiment analysis in a machine learning approach in which machines classify and analyze the human’s sentiments, emotions, opinions etc. about the products. Out of the various classification models, Naïve Bayes, Support Vector Machine (SVM) and Decision Tree are used maximum times for the product analysis. The proposed approach will do better result as compare to other machine learning techniques.

In the world of social networking, consumers tend to refer to expert comments or product reviews before making buying decisions. There is much useful information available on many social networking sites for consumers to make product comparisons. Sentiment analysis is considered appropriate for summarising the opinions. However, the sentences posted online are generally short, which sometimes contains both positive and negative word in the same post. Thus, it may not be sufficient to determine the sentiment polarity of a post by merely counting the number of sentiment words, summing up or averaging the associated scores of sentiment words. In this paper, an unsupervised learning technique, k-means, in conjunction with sentiment analysis, is proposed for assessing public opinions. The proposed approach offers the product designers a tool to promptly determine the critical design criteria for new product planning in the process of new product development by evaluating the user-generated content. The case implementation proves the applicability of the proposed approach.

This paper investigates the data analytics between consumer purchase decisions relative to the on-line reviews. The multi-attributes associated with purchase decisions are comprised of nationalism and consumer preference to be correlated with online reviews using big data analytics. By far, a small fraction of meaningful studies have sought to correlate nationalism and ethnocentrism with big data analytics to date. Globally accepted generic products are selected to expedite the process of data engineering. Two sets were arranged: passenger automobiles for transportation with an estimated $9 trillion global market and the smart phone, boosting its market size of approximately $5 billion. Both products provide minimized cultural, linguistic, gender, age, and/or custom barriers of entry for prospective digital consumers, thereby allowing relatively unrestricted engagement with online reviews and purchases. A series of hypothesis tests indicate that there is a positive correlation between nationalism and automobiles. As to smart cell phones, however, nationalism had nominal control factors. Multi-variate analytics were performed by using R and Tableau Public.

Online reviews and ratings play an important role in shaping the purchase decisions of customers in e-commerce. Many researches have been done to make proper recommendations for users, by exploiting reviews, ratings, user profiles, or behaviors. However, the dynamic evolution of user preferences and item properties haven’t been fully exploited. Moreover, it lacks fine-grained studies at the aspect level. To address the above issues, we define two concepts of user maturity and item popularity, to better explore the dynamic changes for users and items. We strive to exploit fine-grained information at the aspect level and the evolution of users and items, for dynamic sentiment prediction. First, we analyze three real datasets from both the overall level and the aspect level, to discover the dynamic changes (i.e., gradual changes and sudden changes) in user aspect preferences and item aspect properties. Next, we propose a novel model of Aspect-based Sentiment Dynamic Prediction (ASDP), to dynamically capture and exploit the change patterns with uniform time intervals. We further propose the improved model ASDP+ with a bin segmentation algorithm to set the time intervals non-uniformly based on the sudden changes. Experimental results on three real-world datasets show that our work leads to significant improvements.

It has been seen that there is wide acceleration for an E-commerce platform over the past 10 years. Moreover the E-commerce platform booms in the last year due to this COVID -19 pandemic and potentially the next couple of months. Product Review helps a lot for buying anything online regarding product quality, Service, or delivery time. Sentiment analysis helps to understand the context and the person's intent about the product like +ve, -ve, or Neutral. This paper gives the survey of techniques used by the researcher to identify the most relevant factors by taking into account the frequency of the aspect and the impact of customers at the same time. The abstract view of the proposed system that we are going to implement helps to find a positive, negative, or neutral sense of aspects of the product.

As part of the US Food and Drug Administration (FDA)’s Prescription Drug User Fee Act (PDUFA) VI commitments, the Center for Biologics Evaluation and Research (CBER) and Center for Drug Evaluation and Research (CDER) are conducting a model‐informed drug development (MIDD) pilot program. Sponsor(s) who apply and are selected will be granted meetings that aim to facilitate the application of MIDD approaches throughout the product development lifecycle and the regulatory process. Due to their complex mechanisms of action and limited clinical experience, cell and gene therapies have the potential to benefit from the application of MIDD methods, which may facilitate their safety and efficacy evaluations. Leveraging data that are generated from all stages of drug development into appropriate modeling and simulation techniques that inform decisions remains challenging. Additional discussions regarding the application of quantitative modeling approaches to drug development decisions, such as through the MIDD pilot program, may be crucial for both the sponsor(s) and regulatory review teams. Here, we share some perspectives on the opportunities and challenges for utilizing MIDD approaches for product review, which we hope will encourage investigators to publish their experiences and application of MIDD in gene therapy product development.

At this current digital era, business platforms have been drastically shifted toward online stores on internet. With the internet-based platform, customers can order goods easily using their smart phones and get delivery at their place without going to the shopping mall. However, the drawback of this business platform is that customers do not really know about the quality of the products they ordered. Therefore, such platform service often provides the review section to let previous customers leave a review about the received product. The reviews are a good source to analyze customer's satisfaction. Business owners can assess review trend as either positive or negative based on a feedback score that customers had given, but it takes too much time for human to analyze this data. In this research, we develop computational models using machine learning techniques to classify product reviews as positive or negative based on the sentiment analysis. In our experiments, we use the book review data from amazon.com to develop the models. For a machine learning based strategy, the data had been transformed with the bag of word technique before developing models using logistic regression, naïve bayes, support vector machine, and neural network algorithms. For a deep learning strategy, the word embedding is a technique that we used to transform data before applying the long short-term memory and gated recurrent unit techniques. On comparing performance of machine learning against deep learning models, we compare results from the two methods with both the preprocessed dataset and the non-preprocessed dataset. The result is that the bag of words with neural network outperforms other techniques on both non-preprocess and preprocess datasets.

Nowadays, digital reviews and ratings of E-commerce platforms provide a better way for consumers to buy the products. E-commerce giants like Amazon, Flipkart, etc provide customers with a forum to share their experience and provide potential consumers with true evidence of the product's outcomes. To obtain useful insights from a broad collection of reviews, it is important to separate reviews into positive and negative feelings. In the proposed work, Sentiment Analysis is to be done on the consumer review data and categorize into positive and negative feelings. Naive Bayes Classification, LSTM and Support Vector Machine (SVM) were employed for the classification of reviews from the various classification models. Many of the current SA techniques for these customer online product review text data have low accuracy and often takes longer time in the course of training. In this research work, BERT Base Uncased model which is a powerful Deep Learning Model is presented to elucidate the issue of Sentiment Analysis. The BERT model gave an improved performance with good prediction and high accuracy compared to the other methods of Machine Learning in the experimental evaluation.

Voting mechanisms are widely adopted for evaluating the quality and credibility of user-generated content, such as online product reviews. For the reviews that do not receive sufficient votes, techniques and models are developed to automatically assess their helpfulness levels. Existing methods serving this purpose are mostly centered on feature analysis, ignoring the information conveyed in the frequencies and patterns of user votes. Consequently, the accuracy of helpfulness measurement is limited. Inspired by related findings from prediction theories and consumer behavior research, we propose a novel approach characterized by the technique of iterative Bayesian distribution estimation, aiming to more accurately measure the helpfulness levels of reviews used for training prediction models. Using synthetic data and a real-world data set involving 1.67 million reviews and 5.18 million votes from Amazon, a simulation experiment and a two-stage data experiment show that the proposed approach outperforms existing methods on accuracy measures. Moreover, an out-of-sample user study is conducted on Amazon Mechanical Turk. The results further illustrate the predictive power of the new approach. Practically, the research contributes to e-commerce by providing an enhanced method for exploiting the value of user-generated content. Academically, we contribute to the design science literature with a novel approach that may be adapted to a wide range of research topics, such as recommender systems and social media analytics.

References In recent years, online customer reviews have become the main source to determine public opinion about offered products and services. Therefore, manufacturers and sellers are extremely concerned with customer reviews, as these can have a direct impact on their businesses. Unfortunately, there is an increasing trend to write spam reviews to promote or demote targeted products or services. This practice, known as review spamming, has posed many questions regarding the authenticity and dependability of customers’ review-based business processes. Although the spam review detection (SRD) problem has gained much attention from researchers, existing studies on SRD have mostly worked on datasets of English, Chinese, Arabic, Persian, and Malay languages. Therefore, the objective of this research is to identify the spam in Roman Urdu reviews using different classification models based on linguistic features and behavioral features. The performance of each classifier is evaluated in a number of perspectives: (i) linguistic features are used to calculate accuracy (F1 score) of each classifier; (ii) behavioral features combined with distributional and non-distributional aspects are used to evaluate accuracy (F1 score) of each classifier; and (iii) the combination of both linguistic and behavioral features (distributional and non-distributional aspects) are used to evaluate the accuracy of each classifier. The experimental evaluations demonstrated an improved accuracy (F1 score: 0.96), which is the result of combinations of linguistic features and behavioral features with the distributional aspect of reviewers. Moreover, behavioral features using distributional characteristic achieve an accuracy (F1 score: 0.86) and linguistic features shows accuracy (F1 score: 0.69). The outcome of this research can be used to increase customers’ confidence in the South Asian region. It can also help to reduce spam reviews in the South Asian region, particularly in Pakistan.

Aloe vera soothing gel is one of the best-selling products and the most widely reviewed on the Althea Korea website. This product has been reviewed by 1,448 users on the Althea website. The result of the research can be used to minimize mistakes in product purchases. Besides, through a review of a product, the company can analyze the level of customer satisfaction and can be a suggestion for improvements in the future. Therefore, a system is needed to analyze the sentiment towards aloe vera soothing gel to determine the review as a positive or negative sentiment. The method used in this research is the Naïve Bayes method and uses the classification carried out by linguists as a reference for determining positive and negative sentiment. There are two tests carried out in this research, namely confusion matrix testing and black-box testing. The result of the confusion matrix test found an accuracy of 94.62% and the result of black-box testing showed that the output produced was by the application functionality.

The growing interest in online product reviews for legitimate promotion has been accompanied by an increase in fraudulent reviews. However, beyond algorithms for initial fraud detection, little is known about what review portals should do with fraudulent reviews after detecting them. In this paper, we address this question by studying how consumers respond to potentially fraudulent reviews and how review portals can leverage this knowledge to design better fraud management policies. To do this, we combine theoretical development from the trust literature with randomized experiments and statistical analysis using large-scale data from Yelp. We find that consumers tend to increase their trust in the information provided by review portals when the portal displays fraudulent reviews along with non-fraudulent reviews, as opposed to the common practice of censoring suspected fraudulent reviews. The impact of fraudulent reviews on consumers’ decision-making process increases with the uncertainty in the initial evaluation of product quality. We also find that consumers do not effectively process the content of fraudulent reviews (negative or positive). This result furthers the case for a decision heuristic that will incorporate the motivational differences between positive fraudulent reviews and negative fraudulent reviews to effectively aid consumers' decision making.

Sentiment classification aims to detect polarity from a piece of text. The polarity is usually positive or negative, and the text genre is usually product review. The challenges of sentiment classification are that it is hard to capture semantic of reviews, and the labeled data is hard to annotate. Therefore, we propose neural co-training to learn the semantic representation of each review using the neural network model, and learn the information from unlabeled data using a co-training framework. In particular, we use the attention-based bi-directional Gated Recurrent Unit (Att-BiGRU) to model the semantic content of each review and regard different categories of the target product as different views. We then use a co-training framework to learn and predict the unlabeled reviews with different views. Experiment results with the Yelp dataset demonstrate the effectiveness of our approach.

Despite growing expectations that technology journalists serve as critical watchdogs of the technology industry, technology journalism remains under-studied in journalism studies. Drawing on the hi...

An important step when designers use machine learning models is annotating user generated content. In this study we investigate inter-rater reliability measures of qualitative annotations for supervised learning. We work with previously annotated product reviews from Amazon where phrases related to sustainability are highlighted. We measure inter-rater reliability of the annotations using four variations of Krippendorff's U-alpha. Based on the results we propose suggestions to designers on measuring reliability of qualitative annotations for machine learning datasets.

Sentiment analysis can be a very useful aspect for the extraction of useful information from text documents. The main idea for sentiment analysis is how people think for a particular online review, i.e. product reviews, movie reviews, etc. Sentiment analysis is the process where these reviews are classified as positive or negative. The web is enriched with huge amount of reviews which can be analyzed to make it meaningful. This article presents the use of lexicon resources for sentiment analysis of different publicly available reviews. First, the polarity shift of reviews is handled by negations. Intensifiers, punctuation and acronyms are also taken into consideration during the processing phase. Second, words are extracted which have some opinion; these words are then used for computing score. Third, machine learning algorithms are applied and the experimental results show that the proposed model is effective in identifying the sentiments of reviews and opinions.

Sentiment analysis plays an important role in understanding individual opinions expressed in websites such as social media and product review sites. The common approaches to sentiment analysis use the sentiments carried by words that express opinions and are based on either supervised or unsupervised learning techniques. The unsupervised learning approach builds a word-sentiment dictionary, but it requires lengthy time periods and high costs to build a reliable dictionary. The supervised learning approach uses machine learning models to learn the sentiment scores of words; however, training a classifier model requires large amounts of labelled text data to achieve a good performance. In this article, we propose a semisupervised approach that performs well despite having only small amounts of labelled data available for training. The proposed method builds a base sentiment dictionary from a small training dataset using a lasso-based ensemble model with minimal human effort. The scores of words not in the training dataset are estimated using an adaptive instance-based learning model. In a pretrained word2vec model space, the sentiment values of the words in the dictionary are propagated to the words that did not exist in the training dataset. Through two experiments, we demonstrate that the performance of the proposed method is comparable to that of supervised learning models trained on large datasets.

Online product reviews provide vital information to help customers with their purchase decisions. It is therefore critical for administrators of online review websites to ensure the credibility of ...

As the digital information technology domain is growing unprecedently, the process of making the devices mimic human like actions has gained an increased research attention. One of the main features of human like system is the human emotion or sentiment detection. Online shopping has made it easy for everyone in the world to buy different varieties of products from a single place. It also gives the opportunity for them to try new products. While trying to buy new products, people check for the reviews and ratings of those products given by the other customers. Those reviews and ratings can be used to determine the sentimentality of users, who have bought those products by using text mining techniques and classify them either as positive or negative reviews. To analyze these data, the proposed research work has used both Naive Bayes and linear regression algorithms. In our analysis, the accuracy rate of both algorithms is checked.

The purchase of products or administrations through an electronic trade called web based shopping over internet using a web browser. Online Product surveys are significant for up and coming purchasers in helping them decide. To this end, distinctive sentiment mining systems have been proposed, where making a decision about a survey sentence’s direction (e.g., positive or negative) is one of their key difficulties. As of late, Machine learning has risen as powerful methods for taking care of assumption order issues. An AI model inherently learns a helpful portrayal consequently without human endeavors. In any case, we propose a regulated AI structure for item audit conclusion arrangement which utilizes pervasively accessible evaluations as powerless supervision signals. To assess the proposed system, we build a dataset containing 2, 00,000 pitifully named survey sentences and 15000 marked audit sentences from Amazon. Trial results show the more exactness contrasted with past one.

E-business offers designers opportunities to collect consumers’ opinions and context of use experience gainedfrom different products. As reviews possess critical and relevant information, the study presents a robust framework toenrich the product design process by transforming consumer-driven data into actionable knowledge different from onetime,labor-intensive, and time-consuming conventional approaches. To evaluate the framework’s effectiveness, acombination of detailed sentiment and various statistical analyses is conducted to discover customer needs andpreferences on a product category by identifying key product attributes and their importance levels. This paperproposes a semi-automated solution for designers to understand their products and competitors’ products and predictthe future trend of product preference by analyzing a large volume of online data.

Vietnamese is a tonal and isolated language. Its highly ambiguity makes the designing of methods for sentiment analysis being difficult. For getting the most effectiveness, the designed method has to analyze sentiment of sentences based on combining the grammar and syllable structures of Vietnamese. In this paper, a method to build a Vietnamese dataset of product reviews with many sentiment levels, including very negative, negative, neutral, positive and very positive, is proposed. This method can be scaled to a large dataset using for analyzing sentiment of product reviews. Moreover, a solution to add more grammar rules of Vietnamese into the pre-processing of sentiment analysis is also constructed. Those rules simulate the sentiment recognition of humans and help to increase the accuracy of sentiment determination. The combination of grammar rules and some methods for sentiment analysis are experimented on the Vietnamese dataset of product reviews to classify them into sentiment-levels. The testing results show that their accuracy and F-measure are improved and suitable to apply in the practical business analyzing of customer behaviors.

An increasing number of people use social media to share their consumption experiences. Publicly available online reviews have become a significant source of information, which manufacturers use to better understand customer needs and preferences. To facilitate product improvement, this study first considers the inconsistencies between the numerical product ratings and the textual product reviews to establish the inconsistent ordered choice model (IOCM) for measuring customer preferences with regard to product features. The IOCM model effectively reduces the negative impact of inconsistent reviews on the quality of the customer preference measurement model. On the basis of customer preferences obtained from the IOCM model, we then develop a sentiment-based importance–performance analysis (SIPA) model to analyze the categorization of product features for guiding product development. Compared with the original IPA model, the proposed SIPA model in this paper introduces sentiment-importance into the IPA model that makes the product improvement more adaptive to customer preferences. Finally, we empirically evaluate the effectiveness of our proposed IOCM model and illustrate the utility of our proposed SIPA model.

Everyday many online product sales websites and specialized reviewing forums publish a massive volume of human-generated product reviews. People use these reviews as valuable free source of knowledge when decide to buy products. Therefore, an accurate automated system for distinguishing useful reviews from non-useful ones is of great importance. This article presents a new model for specifying the usefulness of comments using the textual features extracted from the reviews. Various types of features including emotion-related, linguistic and text-related features, valence, arousal, and dominance (VAD) values, review-length and polarity of comments are exploited in this study. Moreover, two new algorithms are presented: an improved evidential algorithm for emotion recognition, and an algorithm for extracting VAD values for each review. Finally, the usefulness of reviews is predicted using the mentioned features and an improved Dempster-Shafer score fusion algorithm. The proposed method is applied to review datasets of Books and Video Games of Amazon. The results show that combining the features associated with emotions, features of VAD, and text-related features improves the accuracy of predicting the usefulness of reviews. Also, in comparison with the original Dempster-Shafer method, the precision of the improved Dempster-Shafer algorithm for both datasets is 15% and 11% higher, respectively.

Sentiment analysis is also known as opinion mining which shows the people’s opinions and emotions about certain products or services. The main problem in sentiment analysis is the sentiment polarity categorization that determines whether a review is positive, negative or neutral. Previous studies proposed different techniques, but still there are some research gaps, i) some studies include only 3 sentiment classes: positive, neutral and negative, but none of them considered more than 3 classes ii) sentiment polarity features were considered on individual basis but none of them considered on both individual and on combined basis iii) No previous technique considered five sentiment classes with 3 sentiment polarity features such as a verb, adverb, adjective and their combinations. In this study, we propose a sentiment polarity categorization technique for a large data set of online reviews of Instant Videos. A comprehensive data set of five hundred thousand online reviews is used in our research. There are five classes (Strongly Negative, Negative, Neutral, Positive and Strongly Positive). We also consider three polarity features Verb, Adverb, Adjective and their combinations with their different senses in review-level categorization. Our experiments for review-level categorization show promising outcomes as the accuracy of our results is 81 percent which is 3 percent better than many previous techniques whose average accuracy is 78 percent.

The growth on Digital growth and the Online Shopping is being done virtually, it becomes very difficult for the users to determine the quality of the product. There is no such model existed that can find out the similar or dissimilar community or group of peoples with respect to the tweets (post/messages) with similar textual content, Tweets based on the same location and the tweets based on the invariant time-period for the user timeline. Today as most of the users remain Online, so the requirement of Social Network Analytics arises. Most of the work done in the area of Sentiment Analysis and opinion mining is done using various types of Machine Learning Algorithms. We have taken forward the work of Sentiment Analysis and used the Twitter Sentiment Analysis. As many challenges faced by Twitter Sentiment Analysis and are considered as the target and tried to find out the solution. The user opinion had always played an important role in identifying the user sentiments and the opinion of the user whether it relates to online voting or the user product review. The work done considering the Sentiments of an individual and then mapping done with the Socio-Economic Data.

In the rapidly growing Internet of China, online shopping has spread to the daily lives of most people. Consumers need to consider the characteristics, quality, price, and other information of the product when making purchases on the Internet platform, and carefully select them to improve the satisfaction of shopping. Online product review is one of the sources of product feature information and is increasingly valued by online consumers. This article will review the literature of online product reviews from the generation of online product reviews, outcome variables, usefulness, and influencing factors. We will sort out current academic achievements, and look for future research directions. Follow-up researches should use scientific analysis techniques and methods to establish theoretical models, and explore the mechanism of usefulness of online product reviews.

This study conceptualizes, operationalizes, and identifies the drivers of online product review (OPR) relevance and examines its relative effect on OPR impact compared to review credibility. In con...

Online product reviews are arguably one of the most easily accessible sources of marketing data for online retailers. It is possible to build machine learning tools to learn consumers' opinions from online word of mouth (WOM). Menu costs are practically trivial for online retailers, and it is not difficult to program automatic price changes based on live feeds of online review data. This paper argues that sellers can use online product reviews to develop better pricing strategies. We first build a theoretical model to examine a seller's optimal pricing strategy when online WOM information is taken into consideration. We find that, with consumer reviews, firms may take price-skimming and penetration strategies depending on the combination of consumer characteristics (such as misfit cost) and product characteristics (such as product quality). We examine a book retailing data set collected from online stores to offer empirical support for the analytical predictions.

With the popularity of e-commerce, posting online product reviews expressing customer’s sentiment or opinion towards products has grown exponentially. Sentiment analysis is a computational method that plays an essential role in automating the extraction of subjective information i.e. customer’s sentiment or opinion from online product reviews. Two approaches commonly used in Sentiment analysis tasks are supervised approaches and lexicon-based approaches. In supervised approaches, Sentiment analysis is seen as a text classification task. The result depends not only on the robustness of the machine learning algorithm but also on the utilized features. Bag-of-word is a common utilized features. As a statistical feature, bag-of-word does not take into account semantic of words. Previous research has indicated the potential of semantic in supervised SA task. To augment the result of sentiment analysis, this paper proposes a method to extract text features named sentence level features (SLF) and domain sensitive features (DSF) which take into account semantic of words in both sentence level and domain level of product reviews. A word sense disambiguation based method was adapted to extract SLF. For every similarity employed in generating SLF, the SentiCircle-based method was enhanced to generate DSF. Results of the experiments indicated that our proposed semantic features i.e. SLF and SLF + DSF favorably increase the performance of supervised sentiment analysis on product reviews.

The purpose of this study is to contribute to the marketing literature and practice by describing a research methodology to identify latent dimensions of customer satisfaction in product reviews, and examining the relationship between these attributes and customer satisfaction. Previous research in product reviews has largely relied only on quantitative ratings, either stars or review score. Advanced techniques for text mining provide the opportunity to extract meaning from customer online reviews. By analyzing 51,110 online reviews for 1,610 restaurants via latent Dirichlet allocation, this study uncovers 30 latent dimensions that are determinants of customer satisfaction. Furthermore, this study developed measurements of sentiment and innovativeness as moderators of the effect of these latent attributes to satisfaction.

Smart home and artificial intelligence technologies are developing rapidly, and various smart home products associated with artificial intelligence (AI) improved the quality of living for occupants. Although some studies discussed the application of artificial intelligence in smart homes, few publications fully considered the integration of literature and products. In this paper, we aim to answer the research questions of “what is the trend of smart home technology and products” and “what is the relationship between literature and products in smart homes with AI”. Literature reviews and product reviews are given to define the functions and roles of artificial intelligence in smart homes. We determined the application status of artificial intelligence in smart home products and how it is utilized in our house so that we could understand how artificial intelligence is used to make smart homes. Furthermore, our results revealed that there is a delay between literature and products, and smart home intelligent interactions will become more and more popular.

Online Shopping has become a part of our daily routine, but it still cannot offer intuitive experience as store shopping. Nowadays, most e-commerce Websites offer a Question Answering (QA) system that allows users to consult other users who have purchased the product. However, users still need to wait patiently for others’ replies. In this paper, we investigate how to provide a quick response to the asker by plausible answer identification from product reviews. By analyzing the similarity and discrepancy between explicit answers and reviews that can be answers, a novel multi-task deep learning method with carefully designed attention mechanisms is developed. The method can well exploit large amounts of user generated QA data and a few manually labeled review data to address the problem. Experiments on data collected from Amazon demonstrate its effectiveness and superiority over competitive baselines.

Product review sites are widespread on the Internet and are rapidly gaining in popularity among consumers. This already large volume of user-generated content is dramatically growing every day, making it hard for consumers to filter out the worthwhile information which appears on the various review sites. There commendation system plays a significant role in solving the problem of information overload. This study proposes a framework which integrates a collaborative filtering approach and an opinion mining technique for movie recommendation. Within the proposed framework, sentiment analysis is first applied to the users’ reviews to detect consumer opinions about the movie they have watched and to explore the individual’s preference profile. Traditional recommendation models are overly dependent on preference ratings and often suffer from the problem of ‘data sparsity’. Experimental results obtained from real online reviews show that our proposed method is effective in dealing with insufficient data and is more accurate and efficient than existing traditional methods.

Online product reviews (OPRs) provide abundant information for potential customers to make optimal purchase decisions, and they apply Big Data to better understand product performance. OPRs contain an enormous stockpile of information; therefore, it is difficult for potential customers to make a comprehensive evaluation of alternative products through qualitative reviews. To facilitate consumer purchase decisions, ranking the alternatives based on the OPRs posted on social media platforms is a worthwhile research topic, although relative study is comparatively rare. Therefore, this article provides an extended probabilistic linguistic TODIM (PL-TODIM) method for assisting potential customers to evaluate alternative products through consumer opinions regarding product performance. In other words, this study introduces a novel multiple attribute decision making (MADM) method to rank products based on OPRs. To realize this goal, some basic theories of probabilistic linguistic term sets (PLTSs) are reviewed. Moreover, a possibility formula is first proposed to compare the probabilistic linguistic term sets (PLTSs). Furthermore, a combined weighting method is developed to determine objective weights based on cross-entropy and entropy measures. Thus, the specific steps of the extended PL-TODIM method are described. After that step, in order to testify to the effectiveness and practicality of the proposed method, a case study of OPRs for SUVs is designed. Last, comparisons with other existing methods are further performed to show its advantages.

With the rapid development of the economy, product design has gradually shifted to emotional design that focuses on satisfying users’ emotional needs. Kansei engineering is the commonly used method in product emotional design, the first and vital stage of which needed to be addressed is the acquisition of Kansei knowledge. Considering the development of natural language processing technology and online shopping, a computerized method to extract Kansei knowledge from online product reviews is firstly proposed in this article, and a relational extraction method to establish the relationship between product features and user perceptions is further provided. This article analyzes and extracts the Kansei words of 10 mice respectively using the proposed computerized method, taking the mouse as the case study. Then three evaluation indicators including diversity, effectiveness, and concentration are defined to assess the method, which evaluates the superiority with the advantage of 19.03% in diversity, 6.91% in effectiveness, 22.18% in the concentration and 8.9 times higher in the total score compared with traditional method. Furthermore, taking the best-selling mouse for example, the relational extraction method is applied to extract the relationship between the user concern and the user attitude, establish the relational table, draw Kansei knowledge tree, and finally model connection between product features and user perceptions. By utilizing natural language processing technology and integrating Kansei engineering, linguistics and computer science, it could be considered that the results of this article can accelerate the traditional user survey process, clarify users’ emotional needs, guide the adjustment of product design, and assist the user-centered product emotional design.