Big data and Sentiment Analysis considering reviews from e-commerce platforms to predict consumer behavior

Treballs Finals del Màster de Recerca en Empresa, Facultat d'Economia i Empresa, Universitat de Barcelona, Curs: 2019-2020, Tutor: Javier Manuel Romaní Fernández ; Jaime Gil LafuenteNowadays and since the last two decades, digital data is generated on a massive scale, this phenomenon is known as Big Data (BD). This phenomenon supposes a change in the way of managing and drawing conclusions from data. Moreover, techniques and methods used in artificial intelligence shape new ways of analysis considering BD. Sentiment Analysis (SA) or Opinion Mining (OM) is a topic widely studied for the last few years due to its potential in extracting value from data. However, it is a topic that has been more explored in the fields of engineering or linguistics and not so much in business and marketing fields. For this reason, the aim of this study is to provide a reachable guide that includes the main BD concepts and technologies to those who do not come from a technical field such as Marketing directors. This essay is articulated in two parts. Firstly, it is described the BD ecosystem and the technologies involved. Secondly, it is conducted a systematic literature review in which articles related with the field of SA are analysed. The contribution of this study is a summarization and a brief description of the main technologies behind BD, as well as the techniques and procedures currently involved in SA

A Feature-Based Reputation Model for Product Evaluation

International audienceKnowing the strengths and weaknesses of a product is very important for manufacturers and customers to make decisions. Several sentiment analysis systems are proposed to determine the opinions of customers about products and product features. However, the aggregation methods used are not able to estimate a true reputation value and to re°ect the recent opinions quickly. Most of these systems are based on single source and therefore su®er from availability and susceptibility issues. In this paper, we propose a multi-source reputation model where several aggregation methods are introduced in order to evaluate product based on features. In addition, we also propose a method which uses four parameters in order to rank the reputability of each rating before considering it for reputation values. The results show that the proposed model estimates good reputation values even in the presence of biased behaviors, robust to false ratings and re°ects the newest opinions about product rapidly