Ranking online consumer reviews

YesProduct reviews are posted online by the hundreds and thousands for popular products. Handling such a large volume of continuously generated online content is a challenging task for buyers, sellers and researchers. The purpose of this study is to rank the overwhelming number of reviews using their predicted helpfulness scores. The helpfulness score is predicted using features extracted from review text, product description, and customer question-answer data of a product using the random-forest classifier and gradient boosting regressor. The system classifies reviews into low or high quality with the random-forest classifier. The helpfulness scores of the high-quality reviews are only predicted using the gradient boosting regressor. The helpfulness scores of the low-quality reviews are not calculated because they are never going to be in the top k reviews. They are just added at the end of the review list to the review-listing website. The proposed system provides fair review placement on review listing pages and makes all high-quality reviews visible to customers on the top. The experimental results on data from two popular Indian e-commerce websites validate our claim, as 3–4 newer high-quality reviews are placed in the top ten reviews along with 5–6 older reviews based on review helpfulness. Our findings indicate that inclusion of features from product description data and customer question-answer data improves the prediction accuracy of the helpfulness score.Ministry of Electronics and Information Technology (MeitY), Government of India for financial support during research work through “Visvesvaraya PhD Scheme for Electronics and IT”

Product Review Ranking in e-Commerce using Urgency Level Classification Approach

Review ranking is useful to give users a better experience. Review ranking studies commonly use upvote value, which does not represent urgency, and it causes problems in prediction. In contrast, manual labeling as wide as the upvote value range provides a high bias and inconsistency. The proposed solution is to use a classification approach to rank the review where the labels are ordinal urgency class. The experiment involved shallow learning models (Logistic Regression, Naïve Bayesian, Support Vector Machine, and Random Forest), and deep learning models (LSTM and CNN). In constructing a classification model, the problem is broken down into several binary classifications that predict tendencies of urgency depending on the separation of classes. The result shows that deep learning models outperform other models in classification dan ranking evaluation. In addition, the review data used tend to contain vocabulary of certain product domains, so further research is needed on data with more diverse vocabulary

Measurement of Trustworthiness of the Online Reviews

In electronic commerce (e-commerce)markets, a decision-maker faces a sequential choice problem. Third-party intervention plays an important role in making purchase decisions in this choice process. For instance, while purchasing products/services online, a buyer's choice or behavior is often affected by the overall reviewers' ratings, feedback, etc. Moreover, the reviewer is also a decision-maker. After purchase, the decision-maker would put forth their reviews for the product, online. Such reviews would affect the purchase decision of another potential buyer, who would read the reviews before conforming to his/her final purchase. The question that arises is \textit{how trustworthy are these review reports and ratings?} The trustworthiness of these review reports and ratings is based on whether the reviewer is a rational or an irrational person. Indexing the reviewer's rationality could be a way to quantify a reviewer's rationality but it does not communicate the history of his/her behavior. In this article, the researcher aims at formally deriving a rationality pattern function and thereby, the degree of rationality of the decision-maker or the reviewer in the sequential choice problem in the e-commerce markets. Applying such a rationality pattern function could make it easier to quantify the rational behavior of an agent who participates in the digital markets. This, in turn, is expected to minimize the information asymmetry within the decision-making process and identify the paid reviewers or manipulative reviews

Online reviews and product sales: the role of review visibility

When studying the impact of online reviews on product sales, previous scholars have usually assumed that every review for a product has the same probability of being viewed by consumers. However, decision-making and information processing theories underline that the accessibility of information plays a role in consumer decision-making. We incorporate the notion of review visibility to study the relationship between online reviews and product sales, which is proxied by sales rank information, studying three different cases: (1) when every online review isassumed to have the same probability of being viewed; (2) when we assume that consumers sort online reviews by the most helpful mechanism; and (3) when we assume that consumers sort online reviews by the most recent mechanism. Review non-textual and textual variables are analyzed. The empirical analysis is conducted using a panel of 119 cosmetic products over a period of nine weeks. Using the system generalized method of moments (system GMM) method for dynamic models of panel data, our findings reveal that review variables influence product sales, but the magnitude, and even the direction of the effect, vary amongst visibility cases. Overall, the characteristics of the most helpful reviews have a higher impact on sales.This work was supported by the Spanish Ministry of Science and Innovation grant number ECO2015-65393-

Exploring reviews and review sequences on e-commerce platform: A study of helpful reviews on Amazon.in

YesProminent e-commerce platforms allow users to write reviews for the available products. User reviews play an important role in creating the perception of the product and impact the sales. Online reviews can be considered as an important source of e-word of mouth (e-WOM) on e-commerce platforms. Various dimensions of e-WOM on product sales have been examined for different products. Broadly, studies have explored the effect of summary statistics of reviews on product sales using data from various e-commerce platforms. Few studies have utilized other review characteristics as length, valence, and content of the reviews. The sequence of reviews has been hardly explored in the literature. This study investigates the impact of sequence of helpful reviews along with other review characteristics as ratings (summary statistics), volume, informativeness, and valence of reviews on product sales. Hence, a holistic approach has been used to explore the role of summary statistics, volume, content and sequence of reviews on product sales with special emphasis on sequence of reviews. Relevant theories such as message persuasion, cognitive overload and belief adjustment model have also been explored during the construction of the model for review data. The proposed model has been validated using the helpful reviews available on Amazon.in website for various products

¿Notas explicativas explican? : Análisis de la comunicación de la gestión de riesgos a partir de técnicas de Text Mining

O estudo objetivou analisar a efetividade da comunicação do gerenciamento de risco nas notas explicativas considerando técnicas de text mining. A partir da análise de 241 trechos de texto de 32 instituições financeiras, utilizou-se a similaridade de cosseno como medida de distância para avaliar a relação da variabilidade dos textos com os índices de risco Basileia e Inad90. Os resultados indicam que as notasanalisadas não estão em conformidade com a OCPC 07, pois seu conteúdo não reflete a flutuação dos índices de risco. A utilidade para o usuário em processos de tomada de decisões pode ser prejudicada, dado o indício de desconexão entre os textos e a política de riscos. Apresentam-se casos de repetição integral dos trechos de texto. Como contribuição, esse artigo avança nos estudos textuais das demonstrações financeiras, indo além de análises léxicas e volumétricas, trazendo o conteúdo semânticoem volume e sua relação com indicadores financeiros.The study aimed to analyze the effectiveness of risk management communication in the explanatory notesconsidering text mining techniques. Based on the analysis of 241 text excerpts from 32 financial institutions, we used the cosine similarity as a distance measurement to assess the relationship between the variability of the texts and the Basel and Inad 90 risk indices. The results indicate that the analyzed notes are not in compliance with OCPC 07, as their content does not reflect the fluctuation of risk indices. The benefit of the notes for the user in decision-making processes may be impaired, given the evidence of disconnection between the texts and the risk policy. Cases of complete repetition of the text excerpts are presented. As a contribution, this article advances in the textual studies of financial statements, going beyond lexical and volumetric analyses, bringing the semantic content in volume and its relationship with finance indicators.El estudio tuvo como objetivo analizar la efectividad de la comunicación de la gestión de riesgos en las notas explicativas considerando técnicas de minería de textos. A partir del análisis de 241 extractos de texto de 32 instituciones financieras, se utilizó la similitud del coseno como medida de distancia para evaluar la relación entre la variabilidad de los textos y los índices de riesgo de Basilea e Inad90. Los resultados indican que las notas analizadas no están en conformidad con OCPC 07, ya que su contenido no refleja la fluctuación de los índices de riesgo. La utilidad para el usuario en los procesos de toma de decisiones puede verse afectada, dada la evidencia de desconexión entre los textos y la política de riesgos. Se presentan casos de repetición completa de extractos del texto. Como contribución, este artículo avanza en los estudios textuales de los estados financieros, yendo más allá de los análisis léxicos y volumétricos, trayendo el contenido semántico en volumen y su relación con los indicadores financieros