Deep Learning for User Comment Moderation

Experimenting with a new dataset of 1.6M user comments from a Greek news portal and existing datasets of English Wikipedia comments, we show that an RNN outperforms the previous state of the art in moderation. A deep, classification-specific attention mechanism improves further the overall performance of the RNN. We also compare against a CNN and a word-list baseline, considering both fully automatic and semi-automatic moderation

LSTM-DGWO-Based Sentiment Analysis Framework for Analyzing Online Customer Reviews

Sentiment analysis furnishes consumer concerns regarding products, enabling product enhancement development. Existing sentiment analysis using machine learning techniques is computationally intensive and less reliable. Deep learning in sentiment analysis approaches such as long short term memory has adequately evolved, and the selection of optimal hyperparameters is a significant issue. This study combines the LSTM with differential grey wolf optimization (LSTM-DGWO) deep learning model. The app review dataset is processed using the bidirectional encoder representations from transformers (BERT) framework for efficient word embeddings. Then, review features are extracted by the genetic algorithm (GA), and the optimal review feature set is extracted using the firefly algorithm (FA). Finally, the LSTM-DGWO model categorizes app reviews, and the DGWO algorithm optimizes the hyperparameters of the LSTM model. The proposed model outperformed conventional methods with a greater accuracy of 98.89%. The findings demonstrate that sentiment analysis can be practically applied to understand the customer’s perception of enhancing products from a business perspective.publishedVersio

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 “PARAGRAPH VECTOR” 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

Reputation Agent: Prompting Fair Reviews in Gig Markets

Our study presents a new tool, Reputation Agent, to promote fairer reviews from requesters (employers or customers) on gig markets. Unfair reviews, created when requesters consider factors outside of a worker's control, are known to plague gig workers and can result in lost job opportunities and even termination from the marketplace. Our tool leverages machine learning to implement an intelligent interface that: (1) uses deep learning to automatically detect when an individual has included unfair factors into her review (factors outside the worker's control per the policies of the market); and (2) prompts the individual to reconsider her review if she has incorporated unfair factors. To study the effectiveness of Reputation Agent, we conducted a controlled experiment over different gig markets. Our experiment illustrates that across markets, Reputation Agent, in contrast with traditional approaches, motivates requesters to review gig workers' performance more fairly. We discuss how tools that bring more transparency to employers about the policies of a gig market can help build empathy thus resulting in reasoned discussions around potential injustices towards workers generated by these interfaces. Our vision is that with tools that promote truth and transparency we can bring fairer treatment to gig workers.Comment: 12 pages, 5 figures, The Web Conference 2020, ACM WWW 202

Mining a large shopping database to predict where, when, and what consumers will buy next

Retailers with electronic point-of-sale systems continuously amass detailed data about the items each consumer buys (i.e. what item, how often, its package size, how many were bought, whether the item was on special, etc.). Where the retailer can also associate purchases with a particular individual for example, when an account or loyalty card is issued, the buying behaviour of the consumer can be tracked over time, providing the retailer with valuable information about a customer's changing preferences. This project is based on mining a large database, containing the purchase histories of some 300 000 customers of a retailer, for insights into the behaviour of those customers. Specifically, the aim is to build three predictive models, each forming a chapter of the dissertation; forecasting the number of daily customers to visit a store, detecting changes in consumers' inter-purchase times, and predicting repeat customers after being given a special offer. Having too many goods and not enough customers implies loss for a business; having too few goods implies a lost opportunity to turn a profit. The ideal situation is to stock the appropriate number of goods for the number of customers arriving, so you can minimize loss, and maximize profit. To attend to this problem, in the first chapter we forecast the number of customers that will visit a store each day to buy any product (i.e. store daily visits). In the process we also carry out a time-series forecasting methods comparison, with the main aim of comparing machine learning methods to classical statistical methods. The models are fitted into a univariate time-series data and the best model for this particular dataset is selected using three accuracy measures. The results showed that there was not much difference between the methods, but some classical methods slightly performed better than the machine learning algorithms, and this was consistent with outcomes obtained by Makridakis et al. (2018) on similar comparisons. It is also vital for retailers to know when there has been a change in their consumers purchase behaviour. This change can either be the time between purchases, change in brand selection or change in market share. It is critical for such changes to be detected as early as possible, as speedy detection can help managers act before incurring loses. In the second chapter, we use change-point models to detect changes in consumers' inter-purchase times. Change-point models are approaches that offer a flexible, general-purpose solution to the problem of detecting changes in customer historic behaviour. This multiple change-point model assumes that there is a sequence of underlying parameters, and that this sequence is partitioned into contiguous blocks. These partitions are such that the parameter values are equal within, and different between blocks, whereby a beginning of a block is considered to be a change point. This changepoint model is fitted to consumers inter-purchase times (i.e. we model time between purchases) to see whether there were any significant changes on the consumers buying behaviour over a one year purchase period. The results showed that, depending on the length of the sequences, minority to a handful of customers do experience changes in their purchasing behaviours, with the longer sequences having more changes than the shorter ones. The results seemed to be different to those obtained by Clark and Durbach (2014), but analysing a portion of sequences of same lengths as those analysed in Clark and Durbach (2014), lead to similar results. Increasing sales growth is also vital for retailers, and there are various possible ways in which this can be achieved. One of the strategies is what is referred to as up-selling (whereby a customer is persuaded to make an additional purchase of the same product or purchase a more expensive version of the product.) and cross-selling (whereby a retailer sells a different product or service to an existing customer). These involve campaigning to customers and sell certain products, and sometimes include incentives in the campaign with the aim of exposing customers to these products hoping they will become repeat customers afterwards. In Chapter 3 we build a model to predict which customers are likely to become repeat customers after being given a special offer. This model is fitted to customers' time between two purchases, which makes the input time-series data, and is sequential in nature. Therefore, we build models that provide a good way for dealing with sequential inputs (i.e. convolutional neural networks and recurrent neural networks), and compare them to models that do not take into account the sequence of the data (i.e. feedforward neural networks and decision trees). The results showed that, inter-purchase times are only useful when they are about the same product, as models did no better than random if inter-purchase times were from a different product in the same department. Secondly, it is useful to take the order of the sequence into account, as models that do this do better than those who do not, with the latter not doing any better than a null model. Lastly, while none of the models performed well, deep learning models perform better than standard classification models and produce some substantial lift

Deep Learning for Learning Representation and Its Application to Natural Language Processing

As the web evolves even faster than expected, the exponential growth of data becomes overwhelming. Textual data is being generated at an ever-increasing pace via emails, documents on the web, tweets, online user reviews, blogs, and so on. As the amount of unstructured text data grows, so does the need for intelligently processing and understanding it. The focus of this dissertation is on developing learning models that automatically induce representations of human language to solve higher level language tasks. In contrast to most conventional learning techniques, which employ certain shallow-structured learning architectures, deep learning is a newly developed machine learning technique which uses supervised and/or unsupervised strategies to automatically learn hierarchical representations in deep architectures and has been employed in varied tasks such as classification or regression. Deep learning was inspired by biological observations on human brain mechanisms for processing natural signals and has attracted the tremendous attention of both academia and industry in recent years due to its state-of-the-art performance in many research domains such as computer vision, speech recognition, and natural language processing. This dissertation focuses on how to represent the unstructured text data and how to model it with deep learning models in different natural language processing viii applications such as sequence tagging, sentiment analysis, semantic similarity and etc. Specifically, my dissertation addresses the following research topics: In Chapter 3, we examine one of the fundamental problems in NLP, text classification, by leveraging contextual information [MLX18a]; In Chapter 4, we propose a unified framework for generating an informative map from review corpus [MLX18b]; Chapter 5 discusses the tagging address queries in map search [Mok18]. This research was performed in collaboration with Microsoft; and In Chapter 6, we discuss an ongoing research work in the neural language sentence matching problem. We are working on extending this work to a recommendation system