scholarly journals Sentiment Classification in Customer Service Dialogue with Topic-Aware Multi-Task Learning

2020 ◽  
Vol 34 (05) ◽  
pp. 9177-9184
Author(s):  
Jiancheng Wang ◽  
Jingjing Wang ◽  
Changlong Sun ◽  
Shoushan Li ◽  
Xiaozhong Liu ◽  
...  
Keyword(s):  
Customer Service ◽  
Large Scale ◽  
Empirical Studies ◽  
Critical Role ◽  
Classification Performance ◽  
Sentiment Classification ◽  
Classification Task ◽  
High Quality ◽  
Task Learning ◽  
Novel Approach

Sentiment analysis in dialogues plays a critical role in dialogue data analysis. However, previous studies on sentiment classification in dialogues largely ignore topic information, which is important for capturing overall information in some types of dialogues. In this study, we focus on the sentiment classification task in an important type of dialogue, namely customer service dialogue, and propose a novel approach which captures overall information to enhance the classification performance. Specifically, we propose a topic-aware multi-task learning (TML) approach which learns topic-enriched utterance representations in customer service dialogue by capturing various kinds of topic information. In the experiment, we propose a large-scale and high-quality annotated corpus for the sentiment classification task in customer service dialogue and empirical studies on the proposed corpus show that our approach significantly outperforms several strong baselines.

Locally-Adaptive Naïve Bayes Framework Design via Density-Based Clustering for Large Scale Datasets

2021 ◽  
pp. 278-292
Author(s):  
Faruk Bulut
Keyword(s):  
Learning Style ◽  
Large Scale ◽  
Naive Bayes ◽  
Empirical Studies ◽  
Classification Performance ◽  
Naïve Bayes ◽  
Test Point ◽  
Framework Design ◽  
Locally Adaptive ◽  
Generalized Framework

In this chapter, local conditional probabilities of a query point are used in classification rather than consulting a generalized framework containing a conditional probability. In the proposed locally adaptive naïve Bayes (LANB) learning style, a certain amount of local instances, which are close the test point, construct an adaptive probability estimation. In the empirical studies of over the 53 benchmark UCI datasets, more accurate classification performance has been obtained. A total 8.2% increase in classification accuracy has been gained with LANB when compared to the conventional naïve Bayes model. The presented LANB method has outperformed according to the statistical paired t-test comparisons: 31 wins, 14 ties, and 8 losses of all UCI sets.

Sentiment classification of Chinese cosmetic reviews based on integration of collocations and concepts

2019 ◽  
Vol 38 (1) ◽  
pp. 155-169
Author(s):  
Chihli Hung ◽  
You-Xin Cao
Keyword(s):  
Design Methodology ◽  
Classification Performance ◽  
Sentiment Classification ◽  
Support Vector ◽  
Research Approach ◽  
Content Type ◽  
Novel Approach ◽  
The Relationship ◽  
Modelling Approach

Purpose This paper aims to propose a novel approach which integrates collocations and domain concepts for Chinese cosmetic word of mouth (WOM) sentiment classification. Most sentiment analysis works by collecting sentiment scores from each unigram or bigram. However, not every unigram or bigram in a WOM document contains sentiments. Chinese collocations consist of the main sentiments of WOM. This paper reduces the complexity of the document dimensionality and makes an improvement for sentiment classification. Design/methodology/approach This paper builds two contextual lexicons for feature words and sentiment words, respectively. Based on these contextual lexicons, this paper uses the techniques of associated rules and mutual information to build possible Chinese collocation sets. This paper applies preference vector modelling as the vector representation approach to catch the relationship between Chinese collocations and their associated concepts. Findings This paper compares the proposed preference vector models with benchmarks, using three classification techniques (i.e. support vector machine, J48 decision tree and multilayer perceptron). According to the experimental results, the proposed models outperform all benchmarks evaluated by the criterion of accuracy. Originality/value This paper focuses on Chinese collocations and proposes a novel research approach for sentiment classification. The Chinese collocations used in this paper are adaptable to the content and domains. Finally, this paper integrates collocations with the preference vector modelling approach, which not only achieves a better sentiment classification performance for Chinese WOM documents but also avoids the curse of dimensionality.

Clustering Approaches for Top-k Recommender Systems

2019 ◽  
Vol 28 (05) ◽  
pp. 1950019 ◽  
Author(s):  
Nicolás Torres ◽  
Marcelo Mendoza
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Large Scale ◽  
Empirical Studies ◽  
Cluster Structure ◽  
Parallel Architecture ◽  
Evaluation Framework ◽  
Training Data ◽  
Cluster Solution ◽  
Novel Approach

Clustering-based recommender systems bound the seek of similar users within small user clusters providing fast recommendations in large-scale datasets. Then groups can naturally be distributed into different data partitions scaling up in the number of users the recommender system can handle. Unfortunately, while the number of users and items included in a cluster solution increases, the performance in terms of precision of a clustering-based recommender system decreases. We present a novel approach that introduces a cluster-based distance function used for neighborhood computation. In our approach, clusters generated from the training data provide the basis for neighborhood selection. Then, to expand the search of relevant users, we use a novel measure that can exploit the global cluster structure to infer cluster-outside user’s distances. Empirical studies on five widely known benchmark datasets show that our proposal is very competitive in terms of precision, recall, and NDCG. However, the strongest point of our method relies on scalability, reaching speedups of 20× in a sequential computing evaluation framework and up to 100× in a parallel architecture. These results show that an efficient implementation of our cluster-based CF method can handle very large datasets providing also good results in terms of precision, avoiding the high computational costs involved in the application of more sophisticated techniques.

Imbalanced sentiment classification based on sequence generative adversarial nets

2020 ◽  
Vol 39 (5) ◽  
pp. 7909-7919
Author(s):  
Chuantao Wang ◽  
Xuexin Yang ◽  
Linkai Ding
Keyword(s):  
Deep Learning ◽  
Online Reviews ◽  
Classification Performance ◽  
Sentiment Classification ◽  
Classification Task ◽  
Algorithm Optimization ◽  
Minority Class ◽  
Sample Distribution ◽  
Practical Applications ◽  
Deep Model

The purpose of sentiment classification is to solve the problem of automatic judgment of sentiment tendency. In the sentiment classification task of text data (such as online reviews), the traditional deep learning model focuses on algorithm optimization, but ignores the characteristics of the imbalanced distribution of the number of samples in each classification, which will cause the classification performance of the model to decrease in practical applications. In this paper, the experiment is divided into two stages. In the first stage, samples of minority class in the sample distribution are used to train a sequence generative adversarial nets, so that the sequence generative adversarial nets can learn the features of the samples of minority class in depth. In the second stage, the trained generator of sequence generative adversarial nets is used to generate false samples of minority class and mix them with the original samples to balance the sample distribution. After that, the mixed samples are input into the sentiment classification deep model to complete the model training. Experimental results show that the model has excellent classification performance in comparing a variety of deep learning models based on classic imbalanced learning methods in the sentiment classification task of hotel reviews.

scholarly journals Option-based guarantees to accelerate urgent, high risk vaccines: a new market-shaping approach

2020 ◽  
Author(s):  
David Manheim ◽  
Derek Foster
Keyword(s):  
Large Scale ◽  
Additional Time ◽  
High Quality ◽  
Novel Approach ◽  
Quality Product ◽  
The Government ◽  
A Company ◽  
Market Shaping ◽  
High Quality Product ◽  
Production Facilities

Over the coming year, preventing further waves of COVID-19 and reducing its impact on society will likely require vaccines, so accelerating their availability is critical. However, preparations for large-scale manufacturing, such as building production facilities, are typically delayed until a vaccine is proven safe and effective. This makes sense from a commercial perspective, but the additional time before the vaccine becomes available incurs great costs in terms of lives lost and damage to the economy. There are several potential solutions to reducing the delay between the vaccine being proven effective and its being mass-produced, all of which involve incentives or subsidies to invest in production facilities. We review these, and propose a novel approach using “option-based guarantees,” in which the government commits to paying a proportion of the manufacturer’s preparation costs should the product turn out not to be viable. This counterintuitive approach of payment for failure is appropriate because in the case of success, a company makes money from the product itself, and does not need additional money from the government. This reduces the risk to the company while maintaining an incentive to produce a high-quality product quickly and at scale.

scholarly journals Co-Training Semi-Supervised Deep Learning for Sentiment Classification of MOOC Forum Posts

Symmetry ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 8
Author(s):  
Jing Chen ◽  
Jun Feng ◽  
Xia Sun ◽  
Yang Liu
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Sample Selection ◽  
Classification Performance ◽  
Sentiment Classification ◽  
Unlabeled Data ◽  
Learning Performance ◽  
Average Accuracy ◽  
Complex Features

Sentiment classification of forum posts of massive open online courses is essential for educators to make interventions and for instructors to improve learning performance. Lacking monitoring on learners’ sentiments may lead to high dropout rates of courses. Recently, deep learning has emerged as an outstanding machine learning technique for sentiment classification, which extracts complex features automatically with rich representation capabilities. However, deep neural networks always rely on a large amount of labeled data for supervised training. Constructing large-scale labeled training datasets for sentiment classification is very laborious and time consuming. To address this problem, this paper proposes a co-training, semi-supervised deep learning model for sentiment classification, leveraging limited labeled data and massive unlabeled data simultaneously to achieve performance comparable to those methods trained on massive labeled data. To satisfy the condition of two views of co-training, we encoded texts into vectors from views of word embedding and character-based embedding independently, considering words’ external and internal information. To promote the classification performance with limited data, we propose a double-check strategy sample selection method to select samples with high confidence to augment the training set iteratively. In addition, we propose a mixed loss function both considering the labeled data with asymmetric and unlabeled data. Our proposed method achieved a 89.73% average accuracy and an 93.55% average F1-score, about 2.77% and 3.2% higher than baseline methods. Experimental results demonstrate the effectiveness of the proposed model trained on limited labeled data, which performs much better than those trained on massive labeled data.

scholarly journals Modal Sense Classification At Large

2016 ◽  
Vol 14 ◽  
Author(s):  
Ana Marasović ◽  
Mengfei Zhou ◽  
Alexis Palmer ◽  
Anette Frank
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Classification Task ◽  
High Quality ◽  
Modal Verbs ◽  
Empirical Basis ◽  
Syntactic Features ◽  
Projection Approach ◽  
Annotated Corpora

Modal verbs have different interpretations depending on their context. Their sense categories – epistemic, deontic and dynamic – provide important dimensions of meaning for the interpretation of discourse. Previous work on modal sense classification achieved relatively high performance using shallow lexical and syntactic features drawn from small-size annotated corpora. Due to the restricted empirical basis, it is difficult to assess the particular difficulties of modal sense classification and the generalization capacity of the proposed models. In this work we create large-scale, high-quality annotated corpora for modal sense classification using an automatic paraphrase-driven projection approach. Using the acquired corpora, we investigate the modal sense classification task from different perspectives.

scholarly journals Aggregated Gradient Langevin Dynamics

2020 ◽  
Vol 34 (04) ◽  
pp. 6746-6753
Author(s):  
Chao Zhang ◽  
Jiahao Xie ◽  
Zebang Shen ◽  
Peilin Zhao ◽  
Tengfei Zhou ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Large Scale ◽  
Empirical Studies ◽  
Random Access ◽  
Langevin Dynamics ◽  
High Quality ◽  
Mcmc Sampling ◽  
Learning Tasks ◽  
First Time

In this paper, we explore a general Aggregated Gradient Langevin Dynamics framework (AGLD) for the Markov Chain Monte Carlo (MCMC) sampling. We investigate the nonasymptotic convergence of AGLD with a unified analysis for different data accessing (e.g. random access, cyclic access and random reshuffle) and snapshot updating strategies, under convex and nonconvex settings respectively. It is the first time that bounds for I/O friendly strategies such as cyclic access and random reshuffle have been established in the MCMC literature. The theoretic results also indicate that methods in AGLD possess the merits of both the low per-iteration computational complexity and the short mixture time. Empirical studies demonstrate that our framework allows to derive novel schemes to generate high-quality samples for large-scale Bayesian posterior learning tasks.

Nursing professionals’ attitudes toward use of physical restraints in Styrian nursing homes Austria

Pflege ◽  
2019 ◽  
Vol 32 (1) ◽  
pp. 57-63
Author(s):  
Hannes Mayerl ◽  
Tanja Trummer ◽  
Erwin Stolz ◽  
Éva Rásky ◽  
Wolfgang Freidl
Keyword(s):  
Nursing Homes ◽  
Large Scale ◽  
Critical Role ◽  
Physical Restraint ◽  
Care Practice ◽  
Physical Restraints ◽  
Convenience Sample ◽  
Restraint Use ◽  
Positive Attitudes ◽  
Nursing Professionals

Abstract. Background: Given that nursing staff play a critical role in the decision regarding use of physical restraints, research has examined nursing professionals’ attitudes toward this practice. Aim: Since nursing professionals’ views on physical restraint use have not yet been examined in Austria to date, we aimed to explore nursing professionals’ attitudes concerning use of physical restraints in nursing homes of Styria (Austria). Method: Data were collected from a convenience sample of nursing professionals (N = 355) within 19 Styrian nursing homes, based on a cross-sectional study design. Attitudes toward the practice of restraint use were assessed by means of the Maastricht Attitude Questionnaire in the German version. Results: The overall results showed rather positive attitudes toward the use of physical restraints, yet the findings regarding the sub-dimensions of the questionnaire were mixed. Although nursing professionals tended to deny “good reasons” for using physical restraints, they evaluated the consequences of physical restraint use rather positive and considered restraint use as an appropriate health care practice. Nursing professionals’ views regarding the consequences of using specific physical restraints further showed that belts were considered as the most restricting and discomforting devices. Conclusions: Overall, Austrian nursing professionals seemed to hold more positive attitudes toward the use of physical restraints than counterparts in other Western European countries. Future nationwide large-scale surveys will be needed to confirm our findings.

Green synthesis, characterization of silver sulfide nanoparticles and antibacterial activity evaluation

2019 ◽  
Author(s):  
Chem Int
Keyword(s):  
Antibacterial Activity ◽  
Large Scale ◽  
Research Work ◽  
Silver Sulfide ◽  
Morphological Properties ◽  
Effective Diameter ◽  
Novel Approach ◽  
Transmission Electron ◽  
Green Route ◽  
Silver Sulfide Nanoparticles

This research work presents a facile and green route for synthesis silver sulfide (Ag2SNPs) nanoparticles from silver nitrate (AgNO3) and sodium sulfide nonahydrate (Na2S.9H2O) in the presence of rosemary leaves aqueous extract at ambient temperature (27 oC). Structural and morphological properties of Ag2SNPs nanoparticles were analyzed by X-ray diffraction (XRD) and transmission electron microscopy (TEM). The surface Plasmon resonance for Ag2SNPs was obtained around 355 nm. Ag2SNPs was spherical in shape with an effective diameter size of 14 nm. Our novel approach represents a promising and effective method to large scale synthesis of eco-friendly antibacterial activity silver sulfide nanoparticles.

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