89 research outputs found
Context-aware Helpfulness Prediction for Online Product Reviews
Modeling and prediction of review helpfulness has become more predominant due
to proliferation of e-commerce websites and online shops. Since the
functionality of a product cannot be tested before buying, people often rely on
different kinds of user reviews to decide whether or not to buy a product.
However, quality reviews might be buried deep in the heap of a large amount of
reviews. Therefore, recommending reviews to customers based on the review
quality is of the essence. Since there is no direct indication of review
quality, most reviews use the information that ''X out of Y'' users found the
review helpful for obtaining the review quality. However, this approach
undermines helpfulness prediction because not all reviews have statistically
abundant votes. In this paper, we propose a neural deep learning model that
predicts the helpfulness score of a review. This model is based on
convolutional neural network (CNN) and a context-aware encoding mechanism which
can directly capture relationships between words irrespective of their distance
in a long sequence. We validated our model on human annotated dataset and the
result shows that our model significantly outperforms existing models for
helpfulness prediction.Comment: Published as a proceeding paper in AIRS 201
Deep Learning Implementation for Comparison of User Reviews and Ratings
Sentiment Analysis is the task of identifying and classifying the sentiment expressed in a piece of text as of positive or negative sentiment and has wide application in E-Commerce. In present time, most e-commerce websites have product review sections, which can be used to identify customer satisfaction/dissatisfaction for their product. In E-COMMERCE websites such as Amazon.com, E-bay.com etc, consumers can submit their reviews along with a specific polarity rating (e.g. 1 to 5 stars at Amazon.com). There is a possibility of mismatch between review submitted and polarity of rating. For Amazon.com, a customer can submit a strongly positive review but give it a low rating. The objective of this thesis is to develop a web-service application which can be used to tackle this situation.
We will perform Sentiment Analysis using Deep Learning on Amazon.com product review data. Product reviews will be converted to vectors using âPARAGRAPH VECTORâ which will later be used to train a Recurrent Neural Network with Gated Recurrent Unit. Our model will incorporate both semantic relationship of review text as well as product information. We have also devel- oped an application in Python, that will predict rating score for the submitted review using the trained model. If there is a mismatch between predicted rating score and submitted rating score, a warning/info will be provided
Top Comment or Flop Comment? Predicting and Explaining User Engagement in Online News Discussions
Comment sections below online news articles enjoy growing popularity among
readers. However, the overwhelming number of comments makes it infeasible for
the average news consumer to read all of them and hinders engaging discussions.
Most platforms display comments in chronological order, which neglects that
some of them are more relevant to users and are better conversation starters.
In this paper, we systematically analyze user engagement in the form of the
upvotes and replies that a comment receives. Based on comment texts, we train a
model to distinguish comments that have either a high or low chance of
receiving many upvotes and replies. Our evaluation on user comments from
TheGuardian.com compares recurrent and convolutional neural network models, and
a traditional feature-based classifier. Further, we investigate what makes some
comments more engaging than others. To this end, we identify engagement
triggers and arrange them in a taxonomy. Explanation methods for neural
networks reveal which input words have the strongest influence on our model's
predictions. In addition, we evaluate on a dataset of product reviews, which
exhibit similar properties as user comments, such as featuring upvotes for
helpfulness.Comment: Accepted at the International Conference on Web and Social Media
(ICWSM 2020); 11 pages; code and data are available at
https://hpi.de/naumann/projects/repeatability/text-mining.htm
Image or Text: Which One is More Influential? A Deep-learning Approach for Visual and Textual Data Analysis in the Digital Economy
In a digital economy, different types of information about products communicate their quality and characteristics to prospective consumers. However, it remains unclear which type of information plays the most important role in individualsâ decision-making processes. In this study, we explore the effect that unstructured data has on and the importance of congruence between textual and visual data in consumersâ purchase decisions. We apply a deep neural network model to rank the importance of different information types and use a regression model to investigate the impact that information consistency has on sales predictions. Based on our empirical analysis, we found that both image-based and text-based information influenced consumersâ purchase decisions but that the former influenced their purchase decisions about âsearch goodsâ more and that the latter influenced their purchase decisions about âexperience goodsâ more. Furthermore, congruence between image- and text-based information was positively associated with purchase decisions, which indicates that information congruence impacts productsâ sales performance in the digital economy. In this study, we also demonstrate how to apply advanced deep-learning techniques to measure the congruence between different information types
Goal-Driven Sequential Data Abstraction
Automatic data abstraction is an important capability for both benchmarking
machine intelligence and supporting summarization applications. In the former
one asks whether a machine can `understand' enough about the meaning of input
data to produce a meaningful but more compact abstraction. In the latter this
capability is exploited for saving space or human time by summarizing the
essence of input data. In this paper we study a general reinforcement learning
based framework for learning to abstract sequential data in a goal-driven way.
The ability to define different abstraction goals uniquely allows different
aspects of the input data to be preserved according to the ultimate purpose of
the abstraction. Our reinforcement learning objective does not require
human-defined examples of ideal abstraction. Importantly our model processes
the input sequence holistically without being constrained by the original input
order. Our framework is also domain agnostic -- we demonstrate applications to
sketch, video and text data and achieve promising results in all domains.Comment: Accepted at ICCV 201
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