40,428 research outputs found
Research on Online Word-of-mouth Sentiment Analysis and Attribute Extraction Based on Deep Learning
Online word-of-mouth content mining is of great significance to product, service improvement and demand prediction of online marketing enterprises. However, most studies have focused on the identification of the sentiment tendency of online word-of-mouth, and lack of text content mining for online word-of-mouth, especially negative word-of-mouth. This paper introduces deep learning into online word-of-mouth sentiment tendency analysis and negative word-of-mouth word attribute feature extraction, and builds an online word-of-mouth sentiment tendency analysis and attribute extraction model based on LSTM deep learning algorithm. The model was trained and tested through online word-of-mouth data of a fashion apparel e-commerce company. The results show that the LSTM model has a good effect on sentiment analysis and negative word-of-mouth attribute feature extraction. Through comparative experiments, it is shown that the model has a better effect than the traditional machine learning methods (SVM, Naive Bayes) in the analysis of sentiment tendency
OpenTag: Open Attribute Value Extraction from Product Profiles [Deep Learning, Active Learning, Named Entity Recognition]
Extraction of missing attribute values is to find values describing an
attribute of interest from a free text input. Most past related work on
extraction of missing attribute values work with a closed world assumption with
the possible set of values known beforehand, or use dictionaries of values and
hand-crafted features. How can we discover new attribute values that we have
never seen before? Can we do this with limited human annotation or supervision?
We study this problem in the context of product catalogs that often have
missing values for many attributes of interest.
In this work, we leverage product profile information such as titles and
descriptions to discover missing values of product attributes. We develop a
novel deep tagging model OpenTag for this extraction problem with the following
contributions: (1) we formalize the problem as a sequence tagging task, and
propose a joint model exploiting recurrent neural networks (specifically,
bidirectional LSTM) to capture context and semantics, and Conditional Random
Fields (CRF) to enforce tagging consistency, (2) we develop a novel attention
mechanism to provide interpretable explanation for our model's decisions, (3)
we propose a novel sampling strategy exploring active learning to reduce the
burden of human annotation. OpenTag does not use any dictionary or hand-crafted
features as in prior works. Extensive experiments in real-life datasets in
different domains show that OpenTag with our active learning strategy discovers
new attribute values from as few as 150 annotated samples (reduction in 3.3x
amount of annotation effort) with a high F-score of 83%, outperforming
state-of-the-art models.Comment: Proceedings of the 24th ACM SIGKDD International Conference on
Knowledge Discovery and Data Mining, London, UK, August 19-23, 201
What attracts vehicle consumersâ buying:A Saaty scale-based VIKOR (SSC-VIKOR) approach from after-sales textual perspective?
Purpose:
The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint.
Design/methodology/approach:
A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel NaĂŻve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint.
Findings:
The big data analytics argue that âcost-effectivenessâ characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior.
Research limitations/implications:
The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation.
Originality/value:
Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective
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OBOME - Ontology based opinion mining in UBIPOL
Ontologies have a special role in the UBIPOL system, they help to structure the policy related context, provide conceptualization for policy domain and use in the opinion mining process. In this work we presented a system called Ontology Based Opinion Mining Engine (OBOME) for analyzing a domain-specific opinion corpus by first assisting the user with the creation of a domain ontology from the corpus. We determined the polarity of opinion on the various domain aspects. In the former step, the policy domain aspect has are identified (namely which policy category is represented by the concept). This identification is supported by the policy modelling ontology, which describe the most important policy â related classes and structure. Then the most informative documents from the corpus are extracted and asked the user to create a set of aspects and related keywords using these documents. In the latter step, we used the corpus specific ontology to model the domain and extracted aspect-polarity associations using grammatical dependencies between words. Later, summarized results are shown to the user to analyze and store. Finally, in an offline process policy modeling ontology is updated
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