29,845 research outputs found
Modelling Grocery Retail Topic Distributions: Evaluation, Interpretability and Stability
Understanding the shopping motivations behind market baskets has high
commercial value in the grocery retail industry. Analyzing shopping
transactions demands techniques that can cope with the volume and
dimensionality of grocery transactional data while keeping interpretable
outcomes. Latent Dirichlet Allocation (LDA) provides a suitable framework to
process grocery transactions and to discover a broad representation of
customers' shopping motivations. However, summarizing the posterior
distribution of an LDA model is challenging, while individual LDA draws may not
be coherent and cannot capture topic uncertainty. Moreover, the evaluation of
LDA models is dominated by model-fit measures which may not adequately capture
the qualitative aspects such as interpretability and stability of topics.
In this paper, we introduce clustering methodology that post-processes
posterior LDA draws to summarise the entire posterior distribution and identify
semantic modes represented as recurrent topics. Our approach is an alternative
to standard label-switching techniques and provides a single posterior summary
set of topics, as well as associated measures of uncertainty. Furthermore, we
establish a more holistic definition for model evaluation, which assesses topic
models based not only on their likelihood but also on their coherence,
distinctiveness and stability. By means of a survey, we set thresholds for the
interpretation of topic coherence and topic similarity in the domain of grocery
retail data. We demonstrate that the selection of recurrent topics through our
clustering methodology not only improves model likelihood but also outperforms
the qualitative aspects of LDA such as interpretability and stability. We
illustrate our methods on an example from a large UK supermarket chain.Comment: 20 pages, 9 figure
Place Categorization and Semantic Mapping on a Mobile Robot
In this paper we focus on the challenging problem of place categorization and
semantic mapping on a robot without environment-specific training. Motivated by
their ongoing success in various visual recognition tasks, we build our system
upon a state-of-the-art convolutional network. We overcome its closed-set
limitations by complementing the network with a series of one-vs-all
classifiers that can learn to recognize new semantic classes online. Prior
domain knowledge is incorporated by embedding the classification system into a
Bayesian filter framework that also ensures temporal coherence. We evaluate the
classification accuracy of the system on a robot that maps a variety of places
on our campus in real-time. We show how semantic information can boost robotic
object detection performance and how the semantic map can be used to modulate
the robot's behaviour during navigation tasks. The system is made available to
the community as a ROS module
TDAM: a topic-dependent attention model for sentiment analysis
We propose a topic-dependent attention model for sentiment classification and topic extraction. Our model assumes that a global topic embedding is shared across documents and employs an attention mechanism to derive local topic embedding for words and sentences. These are subsequently incorporated in a modified Gated Recurrent Unit (GRU) for sentiment classification and extraction of topics bearing different sentiment polarities. Those topics emerge from the words' local topic embeddings learned by the internal attention of the GRU cells in the context of a multi-task learning framework. In this paper, we present the hierarchical architecture, the new GRU unit and the experiments conducted on users' reviews which demonstrate classification performance on a par with the state-of-the-art methodologies for sentiment classification and topic coherence outperforming the current approaches for supervised topic extraction. In addition, our model is able to extract coherent aspect-sentiment clusters despite using no aspect-level annotations for training
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