A multi-grained aspect vector learning model for unsupervised aspect identification

2021 ◽  
pp. 1-11
Author(s):  
Jinglei Shi ◽  
Junjun Guo ◽  
Zhengtao Yu ◽  
Yan Xiang
Keyword(s):  
Vector Space ◽  
Sentiment Analysis ◽  
Membership Function ◽  
Topic Models ◽  
Experimental Results ◽  
Coarse Grained ◽  
Product Reviews ◽  
Fine Grained ◽  
Proposed Model ◽  
Identification Model

Unsupervised aspect identification is a challenging task in aspect-based sentiment analysis. Traditional topic models are usually used for this task, but they are not appropriate for short texts such as product reviews. In this work, we propose an aspect identification model based on aspect vector reconstruction. A key of our model is that we make connections between sentence vectors and multi-grained aspect vectors using fuzzy k-means membership function. Furthermore, to make full use of different aspect representations in vector space, we reconstruct sentence vectors based on coarse-grained aspect vectors and fine-grained aspect vectors simultaneously. The resulting model can therefore learn better aspect representations. Experimental results on two datasets from different domains show that our proposed model can outperform a few baselines in terms of aspect identification and topic coherence of the extracted aspect terms.

Using Latent Fine-Grained Sentiment for Cross-Domain Sentiment Analysis

2021 ◽  
Vol 11 (3) ◽  
pp. 29-45
Author(s):  
Kwun-Ping Lai ◽  
Jackie Chun-Sing Ho ◽  
Wai Lam
Keyword(s):  
Knowledge Transfer ◽  
Sentiment Analysis ◽  
Real World ◽  
Coarse Grained ◽  
Target Domain ◽  
Training Strategy ◽  
Fine Grained ◽  
Cross Domain ◽  
Proposed Model ◽  
Novel Model

The authors investigate the problem task of multi-source cross-domain sentiment classification under the constraint of little labeled data. The authors propose a novel model which is capable of capturing both sentiment terms with strong or weak polarity from various source domains which are useful for knowledge transfer to unlabeled target domain. The authors propose a two-step training strategy with different granularities helping the model to identify sentiment terms with different degrees of sentiment polarity. Specifically, the coarse-grained training step captures the strong sentiment terms from the whole review while the fine-grained training step focuses on the latent fine-grained sentence sentiment which are helpful under the constraint of little labeled data. Experiments on a real-world product review dataset show that the proposed model has a good performance even under the little labeled data constraint.

Classifying Sentiment Based on LDA Model

2013 ◽  
Vol 427-429 ◽  
pp. 2614-2617
Author(s):  
Qing Xi Peng
Keyword(s):  
Sentiment Analysis ◽  
Topic Models ◽  
Online Reviews ◽  
Simultaneous Analysis ◽  
Experimental Result ◽  
Proposed Model

Online reviews as a new textual domain offer a unique proposition for sentiment analysis. Their short document length suggests any sentiment they contain is compact and explicit. Although supersized methods have obtained good results, a large amount of corpus should be trained beforehand. Recently, topic models have been introduced for the simultaneous analysis for sentiment in the document. However, the LDA model makes the assumption that, given the parameters the words in the document are all independent. It obviously isnt the case. The words in the document express the sentiment of the author. This paper proposes a model to solve the problem. We assume that the sentiments are related to the topic in the documents. A sentiment layer is added to the LDA model to improve it. Experimental result in the dataset demonstrates the advantage of the proposed model.

Semi-Supervised Aspect-Based Sentiment Analysis for Case-Related Microblog Reviews Using Case Knowledge Graph Embedding

2021 ◽  
pp. 2050012
Author(s):  
Peilian Zhao ◽  
Cunli Mao ◽  
Zhengtao Yu
Keyword(s):  
Sentiment Analysis ◽  
Domain Knowledge ◽  
Opinion Mining ◽  
Data Augmentation ◽  
Training Data ◽  
Knowledge Graph ◽  
Fine Grained ◽  
Learning Framework ◽  
Proposed Model ◽  
Real World Applications

Aspect-Based Sentiment Analysis (ABSA), a fine-grained task of opinion mining, which aims to extract sentiment of specific target from text, is an important task in many real-world applications, especially in the legal field. Therefore, in this paper, we study the problem of limitation of labeled training data required and ignorance of in-domain knowledge representation for End-to-End Aspect-Based Sentiment Analysis (E2E-ABSA) in legal field. We proposed a new method under deep learning framework, named Semi-ETEKGs, which applied E2E framework using knowledge graph (KG) embedding in legal field after data augmentation (DA). Specifically, we pre-trained the BERT embedding and in-domain KG embedding for unlabeled data and labeled data with case elements after DA, and then we put two embeddings into the E2E framework to classify the polarity of target-entity. Finally, we built a case-related dataset based on a popular benchmark for ABSA to prove the efficiency of Semi-ETEKGs, and experiments on case-related dataset from microblog comments show that our proposed model outperforms the other compared methods significantly.

An Adaptive Scheme to Achieve Fine Grained Video Scaling

2017 ◽  
Vol 8 (1) ◽  
pp. 43
Author(s):  
S Safinaz ◽  
A. V. Ravi Kumar
Keyword(s):  
Reconstruction Error ◽  
Experimental Results ◽  
Adaptive Scheme ◽  
Fine Grained ◽  
Proposed Model ◽  
Reconstruction Quality ◽  
Adaptive Reconstruction ◽  
Robust Adaptive ◽  
Parallel Configuration ◽  
Very High

<p>A robust Adaptive Reconstruction Error Minimization Convolution Neural Network (<strong> ARemCNN</strong>) architecture introduced to provide high reconstruction quality from low resolution using parallel configuration. Our proposed model can easily train the bulky datasets such as YUV21 and Videoset4.Our experimental results shows that our model outperforms many existing techniques in terms of PSNR, SSIM and reconstruction quality. The experimental results shows that our average PSNR result is 39.81 considering upscale-2, 35.56 for upscale-3 and 33.77 for upscale-4 for Videoset4 dataset which is very high in contrast to other existing techniques. Similarly, the experimental results shows that our average PSNR result is 38.71 considering upscale-2, 34.58 for upscale-3 and 33.047 for upscale-4 for YUV21 dataset.</p>

scholarly journals Refining Coarse-Grained Spatial Data Using Auxiliary Spatial Data Sets with Various Granularities

2019 ◽  
Vol 33 ◽  
pp. 5091-5099 ◽  
Author(s):  
Yusuke Tanaka ◽  
Tomoharu Iwata ◽  
Toshiyuki Tanaka ◽  
Takeshi Kurashima ◽  
Maya Okawa ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Spatial Data ◽  
Coarse Grained ◽  
Model Parameters ◽  
Data Sets ◽  
Auxiliary Data ◽  
Fine Grained ◽  
Proposed Model ◽  
Spatial Data Sets ◽  
Target Data

We propose a probabilistic model for refining coarse-grained spatial data by utilizing auxiliary spatial data sets. Existing methods require that the spatial granularities of the auxiliary data sets are the same as the desired granularity of target data. The proposed model can effectively make use of auxiliary data sets with various granularities by hierarchically incorporating Gaussian processes. With the proposed model, a distribution for each auxiliary data set on the continuous space is modeled using a Gaussian process, where the representation of uncertainty considers the levels of granularity. The finegrained target data are modeled by another Gaussian process that considers both the spatial correlation and the auxiliary data sets with their uncertainty. We integrate the Gaussian process with a spatial aggregation process that transforms the fine-grained target data into the coarse-grained target data, by which we can infer the fine-grained target Gaussian process from the coarse-grained data. Our model is designed such that the inference of model parameters based on the exact marginal likelihood is possible, in which the variables of finegrained target and auxiliary data are analytically integrated out. Our experiments on real-world spatial data sets demonstrate the effectiveness of the proposed model.

scholarly journals Earlier Attention? Aspect-Aware LSTM for Aspect-Based Sentiment Analysis

2019 ◽  
Author(s):  
Bowen Xing ◽  
Lejian Liao ◽  
Dandan Song ◽  
Jingang Wang ◽  
Fuzheng Zhang ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Irrelevant Information ◽  
Attention Mechanism ◽  
Experimental Results ◽  
Context Modeling ◽  
Fine Grained ◽  
Related Information ◽  
Modeling Process ◽  
Novel Variant

Aspect-based sentiment analysis (ABSA) aims to predict fine-grained sentiments of comments with respect to given aspect terms or categories. In previous ABSA methods, the importance of aspect has been realized and verified. Most existing LSTM-based models take aspect into account via the attention mechanism, where the attention weights are calculated after the context is modeled in the form of contextual vectors. However, aspect-related information may be already discarded and aspect-irrelevant information may be retained in classic LSTM cells in the context modeling process, which can be improved to generate more effective context representations. This paper proposes a novel variant of LSTM, termed as aspect-aware LSTM (AA-LSTM), which incorporates aspect information into LSTM cells in the context modeling stage before the attention mechanism. Therefore, our AA-LSTM can dynamically produce aspect-aware contextual representations. We experiment with several representative LSTM-based models by replacing the classic LSTM cells with the AA-LSTM cells. Experimental results on SemEval-2014 Datasets demonstrate the effectiveness of AA-LSTM.

A Machine Learning-Based Lexicon Approach for Sentiment Analysis

2020 ◽  
Vol 16 (2) ◽  
pp. 8-22
Author(s):  
Tirath Prasad Sahu ◽  
Sarang Khandekar
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Main Idea ◽  
Machine Learning Algorithms ◽  
Online Review ◽  
Product Reviews ◽  
Huge Amount ◽  
Text Documents ◽  
Proposed Model ◽  
The Web

Sentiment analysis can be a very useful aspect for the extraction of useful information from text documents. The main idea for sentiment analysis is how people think for a particular online review, i.e. product reviews, movie reviews, etc. Sentiment analysis is the process where these reviews are classified as positive or negative. The web is enriched with huge amount of reviews which can be analyzed to make it meaningful. This article presents the use of lexicon resources for sentiment analysis of different publicly available reviews. First, the polarity shift of reviews is handled by negations. Intensifiers, punctuation and acronyms are also taken into consideration during the processing phase. Second, words are extracted which have some opinion; these words are then used for computing score. Third, machine learning algorithms are applied and the experimental results show that the proposed model is effective in identifying the sentiments of reviews and opinions.

scholarly journals Effect of Initial State and Deformation Conditions on the Hot Deformation Behavior of M50NiL Steel

Materials ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 5367
Author(s):  
Yan Zhang ◽  
Ming Yang ◽  
Shaolei Long ◽  
Bo Li ◽  
Yilong Liang ◽  
...  
Keyword(s):  
Dynamic Recrystallization ◽  
Hot Deformation ◽  
Deformation Behavior ◽  
Experimental Results ◽  
Coarse Grained ◽  
Case Hardening ◽  
Compression Tests ◽  
Hot Deformation Behavior ◽  
Initial State ◽  
Fine Grained

M50NiL steel, which belongs to a new generation of case-hardening steels used in aerospace bearing applications, is used mainly for the manufacturing of aerospace transmission components that operate under high temperatures. In this study, the effects of the hot deformation parameters and the initial microstructure on the hot deformation behavior of M50NiL steel were investigated through Gleeble-3500 isothermal hot compression tests. The experimental results demonstrated that the critical stain of dynamic recrystallization and the deformation activation energy of the coarse-grained samples were higher than those of the fine-grained samples. This is attributed to the difficulty of deformation and the dynamic recrystallization behavior of coarse-grained samples. Moreover, fine-grained samples contain a large number of dispersed phases, which can pin the grain boundaries and inhibit the growth of recrystallized grains. Such phenomena are beneficial for obtaining finer and more uniform microstructures in M50NiL steel. The experimental results can provide a useful reference for preparing M50NiL steel with excellent mechanical properties.

CoCo

2021 ◽  
Vol 2 (3) ◽  
pp. 1-22
Author(s):  
Fang-jing Wu ◽  
Ying-Jun Chen ◽  
Sok-Ian Sou
Keyword(s):  
Mobile Devices ◽  
Detection Algorithm ◽  
Experimental Results ◽  
Coarse Grained ◽  
Use Case ◽  
Fine Grained

As mobility is an important key to many applications, this work proposes a location-less model to represent mobility that is used to quantify correlations between mobility traces collected by built-in sensors on smartphones. We analyze the mobility correlations from two aspects: co-direction relationship and co-movement relationship . The former is to quantify the similarity of macroscopic moving directions between mobility traces, whereas the latter is to quantify the similarity of their microscopic vibrations. To verify the merits of the two proposed metrics, an exemplary use case, termed co-mobility detection , is considered to determine if two mobile devices share the same journey on the same mobile entity (e.g., carried by the same person). Comprehensive experiments with diverse combinations of mobility traces are conducted in three different environments with different density of Wi-Fi networks. The experimental results indicate that the proposed metrics can effectively evaluate both the coarse-grained similarity of moving directions and the fine-grained similarity of movement variations along mobility traces. The accuracy of the co-mobility detection algorithm can achieve 90% on average for mobility traces with a duration of 70 s.

scholarly journals Opinion Relation Co-Extraction Based on Partially-Supervised Topical Relations Word Alignment Model

2020 ◽  
Vol 8 (6) ◽  
pp. 135-142
Keyword(s):  
Experimental Results ◽  
Word Alignment ◽  
Ranking Algorithm ◽  
Fine Grained ◽  
Opinion Extraction ◽  
Proposed Model ◽  
On Line ◽  
Alignment Model ◽  
Associated Variable

The most critical tools for fine-grained opinion extraction are opinion goals and opinion terms extracted from on-line comments. The key part of this process is to identify the connection between terms. To do this, the Word Alignment Model (WAM) was introduced in which the associated variable can be identified by word alignment by an opinion goal. Nevertheless, its ability to extract opinion words was less successful. In order to determine opinion connections as a process of alignment, the partially supervised Word Alienation Model (PSWAM) has therefore been created. Then a visual co-ranking algorithm was implemented together with the Opinion Relationship Map, to model all the candidates and to measure the confidence of each voter by defining their opinion. In addition, higher-confidence candidates were extracted as opinions or opinions. This method, though, involves an added kind of interaction with terms such as topical connections in graphic thought. Therefore the current relationship is assumed in this report in order to model the applicants and derive the feelings, views and opinions. The efficiency of co-extracting thoughts, viewpoints and issues is enhanced effectively by using this method. The experimental results further indicate that compared to the existing paradigm, the efficiency of the proposed model.

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