基于汉语情感词表的句子情感倾向分类研究

提出了一种基于汉语情感词词表的加权线性组合的句子情感分类方法。该方法通过已有的五种资源构建了中文情感词词表,并采用加权线性组合的句子情感分类方法对句子进行情感类别判断。实验结果表明,直接利用词汇语言粒度的句子情感分类综合F值为78.62％,若加入了否定短语语言粒度后,句子情感分类的综合F值提高了4.14％。     

基于分层式CNN的长文本情感分类模型

为解决文本分类中因文本数据篇幅长且语义情感分布不均导致分类准确度偏低的问题,提出一种基于分层式卷积神经网络(convolutional neural network,CNN)的长文本情感分类模型pos-ACNN-CNN.通过在嵌入层加入位置编码来捕获文本中的词序信息,结合基于注意力机制的CNN识别不同词语的情感语义贡献...     

基于word embedding和CNN的情感分类模型

尝试将word embedding和卷积神经网络（CNN）相结合来解决情感分类问题。首先,利用Skip-Gram模型训练出数据集中每个词的word embedding,然后将每条样本中出现的word embedding组合为二维特征矩阵作为卷积神经网络的输入;此外,每次迭代训练过程中,输入特征也作为参数进行更新。其次,设计了一种具有3种不同大小卷积核的神经网络结构,从而完成多种局部抽象特征的自动提取过程。与传统机器学习方法相比,所提出的基于word embedding和CNN的情感分类模型成功将分类正确率提升了5.04%。     

基于转折句式的文本情感倾向性分析

在文本的情感倾向性研究中缺乏对多种情感共现的转折句式的有效分析,为此提出一种专门对转折句式进行有效情感倾向性分析的方法。充分分析汉语中转折句式的结构特点,通过已有资源构建中文情感词典、转折词表、否定词表,依据转折句式中转折词、否定词、情感词的组合规律提出用于进行情感分析的启发式规则。在公开语料库的实验中,该方法能更好地对转折句式进行情感倾向性分析,将此规则融入到传统的朴素贝叶斯情感分类模型后,能获得更高的情感分析精度。     

Fingerprint classification system using CNN

Multimedia Tools and Applications - Most solitary finger impression check and acknowledgment frameworks / methods are based on the minutiae feature points. Feature Extraction is a fundamental...     

Hybrid sentiment classification on twitter aspect-based sentiment analysis

Social media sites and applications, including Facebook, YouTube, Twitter and blogs, have become major social media attractions today. The huge amount of information from this medium has become an attractive resource for organisations to monitor the opinions of users, and therefore, it is receiving a lot of attention in the field of sentiment analysis. Early work on sentiment analysis approached this problem at a document-level, where the overall sentiment was identified, rather than the details of the sentiment. This research took into account the use of an aspect-based sentiment analysis on Twitter in order to perform a finer-grained analysis. A new hybrid sentiment classification for Twitter is proposed by embedding a feature selection method. A comparison of the accuracy of the classification by the principal component analysis (PCA), latent semantic analysis (LSA), and random projection (RP) feature selection methods are presented in this paper. Furthermore, the hybrid sentiment classification was validated using Twitter datasets to represent different domains, and the evaluation with different classification algorithms also demonstrated that the new hybrid approach produced meaningful results. The implementations showed that the new hybrid sentiment classification was able to improve the accuracy performance from the existing baseline sentiment classification methods by 76.55, 71.62 and 74.24%, respectively.     

Integrated BERT embeddings,BiLSTM-BiGRU and 1-D CNN model for binary sentiment classification analysis of movie reviews

Now a days, understanding the review of the articles, movies are the major issue due to different sentiment present on them. Reviews are short texts which expressing the opinion of the writer on certain texts and express the sentiment related to them. In the recent past, many researchers pay attention to sentiment analysis. In this research work, a novel binary sentiment classification is proposed to classify either positive or negative sentiment. First, the Bidirectional Encoder Representations from Transformers (BERT) embeddings are introduced to tokenize and preprocess the input text. The Bidirectional Long Short-Term Memory (BiLSTM) – Bidirectional Gated Recurrent Unit (BiGRU) and 1-D Convolutional Neural Network (CNN) model is integrated and proposed for sentiment classification. The proposed integrated BERT Embedding and BiLSTM-BiGRU is applied to extract the specified target and self-attention layer is added for better understanding of context, further 1-D CNN along with few other deep learning layers, the sentiment is classified for the selected IMDB movie review dataset. The proposed BERT Embedding + BiLSTM-BiGRU + self-attention and 1-D CNN model is trained and validated with the IMDB movie review dataset. From the simulation, it is found that the testing accuracy and AUC (Area Under the Curve) values are 93.89% and 0.9828 respectively. The performance of the proposed integrated BERT Embedding + BiLSTM-BiGRU+ self-attention and 1-D CNN model is compared with existing models and it is observed that it outperforms better in binary sentiment classification analysis.

     

SentiVerb system: classification of social media text using sentiment analysis

This article proposes novel frameworks of SentiVerb and Spell Checker system, which extracts the reaction, mood, and opinion of users from social media text (SMT). The opinion of users is extracted from their written text on social media such as comments, tweets, blogs, feedbacks etc. and are classified as positive or negative opinion based on sentiment score of SMT using dictionary-based approach and a binary classifier. The dictionary-based approach uses opinion verb dictionary (OVD) to extract the sentiment of opinion verbs present in SMT. This OVD contain only opinion verbs along with their sentiment score. The various steps of the framework such as lower-case conversion, tokenization, spell checker, Part-of-Speech tagging, stop word elimination, stemming, sentiment score calculation, and classification of SMT has been discussed. A new concept of threshold negative parameter is first time introduced in this article. In the experiment, the proposed SentiVerb system’s performance is evaluated on three datasets such as Facebook comments on goods and services tax (GST) implementation in India, tweets on the debate between former president of USA Mr. Barack Obama and Mr. John McCain, and the movie reviews. Consequently, the implementation of the proposed SentiVerb system using rule-based classifier (RBC) gives the best performance result in term of accuracy with 82.5% on GST comments and 79.18% on Obama-McCain debate, which is better than the existing algorithms on the social issues related domain dataset(s). Also, system performance (accuracy of 71.3%) is better than others results on standard movie dataset.

     

RecogNet-LSTM+CNN: a hybrid network with attention mechanism for aspect categorization and sentiment classification

Journal of Intelligent Information Systems - Sentiment analysis for user reviews has received substantial heed in recent years. There are many deep learning models for natural language processing...     

Document-level sentiment classification using hybrid machine learning approach

It is a practice that users or customers intend to share their comments or reviews about any product in different social networking sites. An analyst usually processes to reviews properly to obtain any meaningful information from it. Classification of sentiments associated with reviews is one of these processing steps. The reviews framed are often made in text format. While processing the text reviews, each word of the review is considered as a feature. Thus, selection of right kind of features needs to be carried out to select the best feature from the set of all features. In this paper, the machine learning algorithm, i.e., support vector machine, is used to select the best features from the training data. These features are then given input to artificial neural network method, to process further. Different performance evaluation parameters such as precision, recall, f-measure, accuracy have been considered to evaluate the performance of the proposed approach on two different datasets, i.e., IMDb dataset and polarity dataset.     

Improving sentiment analysis efficacy through feature synchronization

Multimedia Tools and Applications - Social media platforms are becoming a rich source of valuable information through sharing and publishing user generated reviews and comments. The identification...     

Gastrointestinal tract classification using improved LSTM based CNN

Multimedia Tools and Applications - Automated medical image analysis is a challenging field of research that has become quite widespread recently. This process, which is advantageous in terms of...     

Improving forest type classification using the vegetation local difference index

A spatial feature extraction method was applied to increase the accuracy of land-cover classification of forest type information extraction. Traditional spatial feature extraction applications use high-resolution images. However, improving the classification accuracy is difficult when using medium-resolution images, such as a 30 m resolution Enhanced Thematic Mapper Plus (ETM+) image. In this study, we demonstrated a novel method that used the vegetation local difference index (VLDI) derived from the normalized difference vegetation index (NDVI), which were calculated based on the topographically corrected ETM+ image, to delineate spatial features. A simple maximum likelihood classifier and two different ways to use spatial information were introduced in this study as the frameworks to incorporate both spectral and spatial information for analysis. The results of the experiments, where Landsat ETM+ and digital elevation model (DEM) images, together with ground truth data acquired in the study area were used, show that combining the spatial information extracted from medium-resolution images and spectral information improved both classification accuracy and visual qualities. Moreover, the use of spatial information extracted through the proposed method greatly improved the classification performance of particular forest types, such as sparse woodlands.     

High-dimensional multimedia classification using deep CNN and extended residual units

Multimedia Tools and Applications - Processing multimedia data has emerged as a key area for the application of machine learning methods Building a robust classification model to use in high...     

Pessimists and optimists: Improving collaborative filtering through sentiment analysis

This work presents a novel application of Sentiment Analysis in Recommender Systems by categorizing users according to the average polarity of their comments. These categories are used as attributes in Collaborative Filtering algorithms. To test this solution a new corpus of opinions on movies obtained from the Internet Movie Database (IMDb) has been generated, so both ratings and comments are available. The experiments stress the informative value of comments. By applying Sentiment Analysis approaches some Collaborative Filtering algorithms can be improved in rating prediction tasks. The results indicate that we obtain a more reliable prediction considering only the opinion text (RMSE of 1.868), than when apply similarities over the entire user community (RMSE of 2.134) and sentiment analysis can be advantageous to recommender systems.     

Diabetic retinopathy detection and classification using CNN tuned by genetic algorithm

Multimedia Tools and Applications - The Proposed work intends to automate the detection and classification of diabetic retinopathy from retinal fundus image which is very important in...     

SlangSD: building,expanding and using a sentiment dictionary of slang words for short-text sentiment classification

Sentiment information about social media posts is increasingly considered an important resource for customer segmentation, market understanding, and tackling other socio-economic issues. However, sentiment in social media is difficult to measure since user-generated content is usually short and informal. Although many traditional sentiment analysis methods have been proposed, identifying slang sentiment words remains a challenging task for practitioners. Though some slang words are available in existing sentiment lexicons, with new slang being generated with emerging memes, a dedicated lexicon will be useful for researchers and practitioners. To this end, we propose to build a slang sentiment dictionary to aid sentiment analysis. It is laborious and time-consuming to collect a comprehensive list of slang words and label the sentiment polarity. We present an approach to leverage web resources to construct a Slang Sentiment Dictionary (SlangSD) that is easy to expand. SlangSD is publicly available for research purposes. We empirically show the advantages of using SlangSD, the newly-built slang sentiment word dictionary for sentiment classification, and provide examples demonstrating its ease of use with a sentiment analysis system.     

Fine grained sentiment polarity classification using augmented knowledge sequence-attention mechanism

Sentiment Analysis is a channel by which automated feedback analysis can be processed effectively and efficiently. The reviews reachable are not only useful for customers buying a product or service but also the manufacturers and industrialists to formulate their production and marketing strategies as well as government organizations to access the views of the citizens. Recommender systems, market researches, and predictions on various social media platforms can be made more practical and comprehensive by sentiment analysis. It has evolved in numerous ways starting from the coarsely grained analysis. But when we need to find intricate details scrunched into a single statement or so, aspect-level sentiment analysis is the way. The uprising of deep learning approaches has opened doors in various applications, including Aspect-based Sentiment Analysis. These networks are computationally strong and can be trained easily. However, since they lag in recognizing semantic complexities, various Natural Language Processing techniques have been joined into the neural models. Researchers have been creative with inventing various novel models, combining a myriad of neural networks and attention mechanisms to improve aspect detection and sentiment polarity identification. The promise that this field provides because of its independent nature compels scientists to delve into the topic. In this work, fine grained analysis is not only processed for the aspect and aspect word detection but also for polarity and its intensity analysis. Our Contributions include the enriched input embedding with token, orientation, grammatical function, field and intensity components in the embedding stage, refined pattern extraction with convolutional kernels and improved performance using attention mechanism in the latter stages. Our experimental results reveal that, our procedural methodology has brought out an optimal enhanced performance compared to the near closer designs.     

Multi-scale features for heartbeat classification using directed acyclic graph CNN

ABSTRACT

A new architecture of deep neural networks, directed acyclic graph convolutional neural networks (DAG-CNNs), is used to classify heartbeats from electrocardiogram (ECG) signals into different subject-based classes. DAG-CNNs not only fuse the feature extraction and classification stages of the ECG classification into a single automated learning procedure, but also utilized multi-scale features and perform score-level fusion of multiple classifiers automatically. Therefore, DAG-CNN negates the necessity to extract hand-crafted features. In most of the current approaches, only the high level features which extracted by the last layer of CNN are used. Instead of performing feature level fusion manually and feeding the results into a classifier, the proposed multi-scale system can automatically learn different level of features, combine them and predict the output label. The results over the MIT-BIH arrhythmia benchmarks database demonstrate that the proposed system achieves a superior classification performance compared to most of the state-of-the-art methods.