Sentiment Community a New Way to Learn Users’ Sentiments in Social Network- A Preliminary Study

Many enterprises begin to use Social Network Sites (SNS) as an important channel and platform to do online marketing and reputation management, because users’ interactions in SNS have more effective impacts on customers’ buying decisions and images of enterprises than traditional websites. To do this, the enterprises need to learn and trace users’ sentiments on their products/services for designing appropriate business strategies. In this study, the sentiment community is proposed as a method for this. The sentiment communities with different polarities in SNS usually represent groups of users with different preferences, and discovered sentiment communities is very useful for enterprises to do customer segmentation and target marketing. Also the evolvement of sentiment communities is explored, so that enterprises can easily trace users’ sentiments and learn their diffusions in SNS. In this paper, a novel method is proposed for discovering users’ sentiment communities, and an initial experimental evaluation is executed

Real-Time Purchase Prediction Using Retail Video Analytics

The proliferation of video data in retail marketing brings opportunities for researchers to study customer behavior using rich video information. Our study demonstrates how to understand customer behavior of multiple dimensions using video analytics on a scalable basis. We obtained a unique video footage data collected from in-store cameras, resulting in approximately 20,000 customers involved and over 6,000 payments recorded. We extracted features on the demographics, appearance, emotion, and contextual dimensions of customer behavior from the video with state-of-the-art computer vision techniques and proposed a novel framework using machine learning and deep learning models to predict consumer purchase decision. Results showed that our framework makes accurate predictions which indicate the importance of incorporating emotional response into prediction. Our findings reveal multi-dimensional drivers of purchase decision and provide an implementable video analytics tool for marketers. It shows possibility of involving personalized recommendations that would potentially integrate our framework into omnichannel landscape

An Empirical Study of Online Consumer Review Spam: A Design Science Approach

Because of the sheer volume of consumer reviews posted to the Internet, a manual approach for the detection and analysis of fake reviews is not practical. However, automated detection of fake reviews is a very challenging research problem given the fact that fake reviews could just look like legitimate reviews. Guided by the design science research methodology, one of the main contributions of our research work is the development of a novel methodology and an instantiation which can effectively detect untruthful consumer reviews. The results of our experiment confirm that the proposed methodology outperforms other well-known baseline methods for detecting untruthful reviews collected from amazon.com. Above all, the designed artifacts enable us to conduct an econometric analysis to examine the impact of fake reviews on product sales. To the best of our knowledge, this is the first empirical study conducted to analyze the economic impact of fake consumer reviews

Building Comparative Product Relation Maps by Mining Consumer Opinions on the Web

With the Web 2.0 paradigm, users play the active roles in producing Web contents at online forums, wiki, blogs, social networks, etc. Among these users contributed contents, many of them are opinions about products, services, or political issues. Accordingly, extracting the comparative relations about products or services by means of opinion mining techniques could generate significant business values. From the producers’ perspective, they could better understand the relative strength or weakness of their products, and hence developing better products to meet the consumers’ requirements. From the consumers’ perspective, they could exercise more informed purchasing decisions by comparing the various features of certain kind of products. The main contribution of this paper is the development of a novel Support Vector Machine (SVM) based comparative relation map generation method for automatic product features analysis based on the sheer volume of consumer opinions posted on the Web. The proposed method has been empirically evaluated based on the consumer opinions crawled from the Web recently. Our initial experimental results show that the performance of the proposed method is promising, and the precision can achieve 73.15%

Predict Market Share with Users’ Online Activities Data: An Initial Study on Market Share and Search Index of Mobile Phone

Acquiring accurate and timely market share information is very important for producers to arrange producing plan and design marketing strategy. However the high cost and long period of collecting survey data in survey-based method make it much difficult to easily get latest market shares data. Recently, the emerging online web systems provide users with new and convenient ways of searching, learning, experiencing and buying products. The users’ activities data captured by these web systems can reflect users’ buying intentions and behaviours very well, and contain very valuable information for predicting market shares. In this study, the correlation between Google search index and market shares of mobile phones is analyzed with time series analysis technology. The experiment result shows the statistically significant relationships exist between search index and market shares. This indicates the easily got search index data with low cost has the power of timely forecasting market shares. This study opens a door to apply users’ online activities data to accurately and timely predict market shares, which will bring many benefits to producers and customers

A Month in the Life of Groupon

Groupon has become the latest Internet sensation, providing daily deals to customers in the form of discount offers for restaurants, ticketed events, appliances, services, and other items. We undertake a study of the economics of daily deals on the web, based on a dataset we compiled by monitoring Groupon over several weeks. We use our dataset to characterize Groupon deal purchases, and to glean insights about Groupon's operational strategy. Our focus is on purchase incentives. For the primary purchase incentive, price, our regression model indicates that demand for coupons is relatively inelastic, allowing room for price-based revenue optimization. More interestingly, mining our dataset, we find evidence that Groupon customers are sensitive to other, "soft", incentives, e.g., deal scheduling and duration, deal featuring, and limited inventory. Our analysis points to the importance of considering incentives other than price in optimizing deal sites and similar systems.Comment: 6 page

Biosynthesis and genetic engineering of phenazine-1-carboxylic acid in Pseudomonas chlororaphis Lzh-T5

Phenazine-1-carboxylic acid (PCA) is a biologically active substance with the ability to prevent and control crop diseases. It was certified as a pesticide by the Ministry of Agriculture of China in 2011 and was named “Shenzimycin.” Lzh-T5 is a Pseudomonas chlororaphis strain found in the rhizosphere of tomatoes. This strain can produce only 230 mg/L of PCA. We used LDA-4, which produces the phenazine synthetic intermediate trans-2,3-dihydro-3-hydroxyanthranilic acid in high amounts, as the starting strain. By restoring phzF and knocking out phzO, we achieved PCA accumulation. Moreover, PCA production was enhanced after knocking out negative regulators, enhancing the shikimate pathway, and performing fed-batch fermentation, thus resulting in the production of 10,653 mg/L of PCA. It suggested that P. chlororaphis Lzh-T5 has the potential to become an efficiency cell factory of biologically active substances

Mobile Commerce in the New Tablet Economy

The rapid adoption of smartphones and tablets has been fueling the growth of mobile commerce around the world. This paper quantifies the economic impact of tablets in ecommerce markets by examining its complementary and substitution effects with PCs and smartphones. We use an archival data from the largest ecommerce website in China and exploit a quasi-natural experiment to identify our results. Results show that the introduction of tablets enhanced the overall growth of ecommerce markets, with an annual impact of approximately US$3.04 billion. The tablet channel acts as a substitute for the PC and a complement for the smartphone. Further, consumers spend more when a tablet is simultaneously used with a PC, a smartphone, or both. The most revenue generating combination of simultaneous device usage is that of a smartphone and a tablet. We provide insights for retailers about how to increase their sales revenue in the emerging tablet economy