EXPRESS: Attribute Sentiment Scoring with Online Text Reviews: Accounting for Language Structure and Missing Attributes

The authors address two significant challenges in using online text reviews to obtain finegrained attribute level sentiment ratings. First, in contrast to methods that rely on word frequency, they develop a deep learning convolutional-LSTM hybrid model to account for language structure. The convolutional layer accounts for spatial structure (adjacent word groups or phrases) and LSTM accounts for sequential structure of language (sentiment distributed and modified across non-adjacent phrases). Second, they address the problem of missing attributes in text in constructing attribute sentiment scores—as reviewers write only about a subset of attributes and remain silent on others. They develop a model-based imputation strategy using a structural model of heterogeneous rating behavior. Using Yelp restaurant review data, they show superior attribute sentiment scoring accuracy with their model. They find three reviewer segments with different motivations: status seeking, altruism/want voice, and need to vent/praise. Reviewers write to inform and vent/praise, but not based on attribute importance. The heterogeneous model-based imputation performs better than other common imputations; and importantly leads to managerially significant corrections in restaurant attribute ratings. More broadly, our results suggest that social science research should pay more attention to reduce measurement error in variables constructed from text.

Missing data completion in wastewater network databases: the added-value of Graph Convolutional Neural Networks.

<p>Wastewater networks are mandatory for urbanization. Their management, which includes reparation and expansion operations, requires precise information about their underground components, mainly pipes. For hydraulic  modelling purposes, the characteristics of the nodes and pipes in the model must be fully known via specific, complete and consistent attribute tables. However, due to years of service and interventions by different actors, information about the attributes and characteristics associated with the various objects constituting a network are not  properly  tracked and reported. Therefore, databases related to wastewater networks, when available, still suffer from a large amount of missing data.</p><p>A wastewater network constitutes a graph composed of nodes and edges. Nodes represent manholes, equipment, repairs, etc. while edges represent pipes. Each of the nodes and edges has a set of properties in the form of attributes such as diameters of the pipes. In this work, we seek to complete the missing attributes of wastewater networks using machine learning techniques. The main goal is to make use of the graph structures in the learning process, taking into consideration the topology and the relationships between their components (nodes and edges) to predict missing attribute values.</p><p>Graph Convolutional Network models (GCN) have gained a lot of attention in recent years and achieved state of the art in many applications such as chemistry. These models are applied directly on graphs to perform diverse machine learning tasks. We present here the use of GCN models such as ChebConv to complete the missing attribute values of two datasets (1239 and 754 elements) extracted from the wastewater networks of  Montpellier and Angers Metropolis in France. To emphasize the importance of the graph structure in the learning process and thus on the quality of the predictions, GCNs' results are benchmarked against non-topological neural networks. The application on diameter value completion, indicates that using the structure of the wastewater network in the learning process has a significant impact on the prediction results especially for minority classes. Indeed, the diameter classes are very heterogeneous in terms of number of elements with a highly majority class and several classes with few elements. Non-topological neural networks always fail to predict these classes and affect the majority class value to every missing diameter, yielding a perfect precision for this class but a null one for all the others. On the contrary, the ChebConv model precision is slightly lower (0.93) for the majority class but much higher (increases from 0.3 to 0.81) for other classes, using only the structure of the graphs. The use of other available information in the learning process may enhance these results.</p>

Different Approaches for Missing Data Handling in Fuzzy Clustering: A Review

Introduction: Incomplete data sets containing some missing attributes is a prevailing problem in many research areas. The reasons for the lack of missing attributes may be several; human error in tabulating/recording the data, machine failure, errors in data acquisition or refusal of a patient/customer to answer few questions in a questionnaire or survey. Further, clustering of such data sets becomes a challenge. Objective: In this paper, we presented a critical review of various methodologies proposed for handling missing data in clustering. The focus of this paper is the comparison of various imputation techniques based FCM clustering and the four clustering strategies proposed by Hathway and Bezdek. Methods: In this paper, we imputed the missing values in incomplete datasets by various imputation/ non-imputation techniques to complete the data set and then conventional fuzzy clustering algorithm is applied to get the clustering results. Results: Experiments on various synthetic data sets and real data sets from UCI repository are carried out. To evaluate the performance of the various imputation/ non-imputation based FCM clustering algorithm, several performance criteria and statistical tests are considered. Experimental results on various data sets show that the linear interpolation based FCM clustering performs significantly better than other imputation as well as non-imputation techniques. Conclusion: It is concluded that the clustering algorithm is data specific, no clustering technique can give good results on all data sets. It depends upon both the data type and the percentage of missing attributes in the dataset. Through this study, we have shown that the linear interpolation based FCM clustering algorithm can be used effectively for clustering of incomplete data set.

Inpainting of local wavefront attributes using artificial intelligence for enhancement of massive 3-D pre-stack seismic data

SUMMARY We propose an advanced version of non-linear beamforming assisted by artificial intelligence (NLBF-AI) that includes additional steps of encoding and interpolating of wavefront attributes using inpainting with deep neural network (DNN). Inpainting can efficiently and accurately fill the holes in waveform attributes caused by acquisition geometry gaps and data quality issues. Inpainting with DNN delivers excellent quality of interpolation with the negligible computational effort and performs particularly well for a challenging case of irregular holes where other interpolation methods struggle. Since conventional brute-force attribute estimation is very costly, we can further intentionally create additional holes or masks to restrict expensive conventional estimation to a smaller subvolume and obtain missing attributes with cost-effective inpainting. Using a marine seismic data set with ocean bottom nodes, we show that inpainting can reliably recover wavefront attributes even with masked areas reaching 50–75 per cent. We validate the quality of the results by comparing attributes and enhanced data from NLBF-AI and conventional NLBF using full-density data without decimation.

How to Handle Missing Values in Multi-Criteria Decision Aiding?

It is often the case in the applications of Multi-Criteria Decision Making that the values of alternatives are unknown on some attributes. An interesting situation arises when the attributes having missing values are actually not relevant and shall thus be removed from the model. Given a model that has been elicited on the complete set of attributes, we are looking thus for a way -- called restriction operator -- to automatically remove the missing attributes from this model. Axiomatic characterizations are proposed for three classes of models. For general quantitative models, the restriction operator is characterized by linearity, recursivity and decomposition on variables. The second class is the set of monotone quantitative models satisfying normalization conditions. The linearity axiom is changed to fit with these conditions. Adding recursivity and symmetry, the restriction operator takes the form of a normalized average. For the last class of models -- namely the Choquet integral, we obtain a simpler expression. Finally, a very intuitive interpretation is provided.