The connection between messaging and action is fundamental both to web applications, such as web search and sentiment analysis, and to economics. However, while prominent online applications exploit messaging in natural (human) language in order to predict non-strategic action selection, the economics literature focuses on the connection between structured stylized messaging to strategic decisions in games and multi-agent encounters. This paper aims to connect these two strands of research, which we consider highly timely and important due to the vast online textual communication on the web. Particularly, we introduce the following question: Can free text expressed in natural language serve for the prediction of action selection in an economic context, modeled as a game In order to initiate the research on this question, we introduce the study of an individual’s action prediction in a one-shot game based on free text he/she provides, while being unaware of the game to be played. We approach the problem by attributing commonsensical personality attributes via crowd-sourcing to free texts written by individuals, and employing transductive learning to predict actions taken by these individuals in one-shot games based on these attributes. Our approach allows us to train a single classifier that can make predictions with respect to actions taken in multiple games. In experiments with three well-studied games, our algorithm compares favorably with strong alternative approaches. In ablation analysis, we demonstrate the importance of our modeling choices—the representation of the text with the commonsensical personality attributes and our classifier—to the predictive power of our model.

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The connection between messaging and action is fundamental both to web applications, such as web search and sentiment analysis, and to economics. However, while prominent online applications exploit messaging in natural (human) language in order to predict non-strategic action selection, the economics literature focuses on the connection between structured stylized messaging to strategic decisions in games and multi-agent encounters. This paper aims to connect these two strands of research, which we consider highly timely and important due to the vast online textual communication on the web. Particularly, we introduce the following question: can free text expressed in natural language serve for the prediction of action selection in an economic context, modeled as a game? We initiate research on this question by providing preliminary positive results.

Product reviews play a key role in e-commerce platforms. Studies show that many users read product reviews before a purchase and trust them to the same extent as personal recommendations. However, in many cases, the number of reviews per product is large and extracting useful information becomes a challenging task. Several websites have recently added an option to post tips – short, concise, practical, and self-contained pieces of advice about the products. These tips are complementary to the reviews and usually add a new non-trivial insight about the product, beyond its title, attributes, and description. Yet, most if not all major e-commerce platforms lack the notion of a tip as a first class citizen and customers typically express their advice through other means, such as reviews. In this work, we propose an extractive method for tip generation from product reviews. We focus on five popular e-commerce domains whose reviews tend to contain useful non-trivial tips that are beneficial for potential customers. We formally define the task of tip extraction in e-commerce by providing the list of tip types, tip timing (before and/or after the purchase), and connection to the surrounding context sentences. To extract the tips, we propose a supervised approach and leverage a publicly-available dataset, annotated by human editors, containing 14,000 product reviews. To demonstrate the potential of our approach, we compare different tip generation methods and evaluate them both manually and over the labeled set. Our approach demonstrates particularly high performance for popular products in the Baby, Home Improvement and Sports & Outdoors domains, with precision of over \(95\% \) for the top 3 tips per product. In addition, we evaluate the performance of our methods on previously-unseen domains. Finally, we discuss the practical usage of our approach in real world applications. Concretely, we explain how tips generated from user reviews can be integrated in various use cases within e-commerce platforms and benefit both buyers and sellers.