A Proposal for Brand Analysis with Opinion Mining

The popularity of e‐commerce sites has increased the availability of product reviews, most of which are overlooked by customers because of their large number. Opinion mining, a discipline that aims to extract people\u27s opinions regarding some topic from reviews, was developed to address this situation. However, the individual interpretation of the reviews is not enough to take advantage of the massive datasets available on the web; a meaningful summary of the set of opinions is necessary to give users an overall insight into the opinions. We propose a system to extract information from Amazon product reviews, which focuses on a time‐varying comparison among different brands in a given Amazon product department. In this system, the results are summarized so that users can get a representative and detailed overview of the opinions of (possibly) hundreds of other users regarding the strong and weak points of several brands. This information can be used by customers who want to find high‐quality products, or by the enterprises themselves, which could find the aspects with a higher impact in the public perception

Applying Deep Learning Techniques for Sentiment Analysis to Assess Sustainable Transport

Users voluntarily generate large amounts of textual content by expressing their opinions, in social media and specialized portals, on every possible issue, including transport and sustainability. In this work we have leveraged such User Generated Content to obtain a high accuracy sentiment analysis model which automatically analyses the negative and positive opinions expressed in the transport domain. In order to develop such model, we have semiautomatically generated an annotated corpus of opinions about transport, which has then been used to fine-tune a large pretrained language model based on recent deep learning techniques. Our empirical results demonstrate the robustness of our approach, which can be applied to automatically process massive amounts of opinions about transport. We believe that our method can help to complement data from official statistics and traditional surveys about transport sustainability. Finally, apart from the model and annotated dataset, we also provide a transport classification score with respect to the sustainability of the transport types found in the use case dataset.This work has been partially funded by the Spanish Ministry of Science, Innovation and Universities (DeepReading RTI2018-096846-B-C21, MCIU/AEI/FEDER, UE), Ayudas Fundación BBVA a Equipos de Investigación Científica 2018 (BigKnowledge), DeepText (KK-2020/00088), funded by the Basque Government and the COLAB19/19 project funded by the UPV/EHU. Rodrigo Agerri is also funded by the RYC-2017-23647 fellowship and acknowledges the donation of a Titan V GPU by the NVIDIA Corporation