A Mixed approach of Deep Learning method and Rule-Based method to improve Aspect Level Sentiment Analysis

Social networks have changed the communication patterns significantly. Information available from different social networking sites can be well utilized for the analysis of users opinion. Hence, the organizations would benefit through the development of a platform, which can analyze public sentiments in the social media about their products and services to provide a value addition in their business process. Over the last few years, deep learning is very popular in the areas of image classification, speech recognition, etc. However, research on the use of deep learning method in sentiment analysis is limited. It has been observed that in some cases the existing machine learning methods for sentiment analysis fail to extract some implicit aspects and might not be very useful. Therefore, we propose a deep learning approach for aspect extraction from text and analysis of users sentiment corresponding to the aspect. A seven layer deep convolutional neural network (CNN) is used to tag each aspect in the opinionated sentences. We have combined deep learning approach with a set of rule-based approach to improve the performance of aspect extraction method as well as sentiment scoring method. We have also tried to improve the existing rule-based approach of aspect extraction by aspect categorization with a predefined set of aspect categories using clustering method and compared our proposed method with some of the state-of-the-art methods. It has been observed that the overall accuracy of our proposed method is 0.87 while that of the other state-of-the-art methods like modified rule-based method and CNN are 0.75 and 0.80 respectively. The overall accuracy of our proposed method shows an increment of 7–12% from that of the state-of-the-art methods.

Download Full-text

A Probabilistic Deep Learning Approach for Twitter Sentiment Analysis

International Journal of Distributed Artificial Intelligence ◽

10.4018/ijdai.2020070102 ◽

2020 ◽

Vol 12 (2) ◽

pp. 21-34

Author(s):

Mostefai Abdelkader

Keyword(s):

Deep Learning ◽

Sentiment Analysis ◽

State Of The Art ◽

Learning Approach ◽

Probabilistic Representation ◽

Effective Manner ◽

Textual Data ◽

Positive Class ◽

Negative Class ◽

Deep Learning Model

In recent years, increasing attention is being paid to sentiment analysis on microblogging platforms such as Twitter. Sentiment analysis refers to the task of detecting whether a textual item (e.g., a tweet) contains an opinion about a topic. This paper proposes a probabilistic deep learning approach for sentiments analysis. The deep learning model used is a convolutional neural network (CNN). The main contribution of this approach is a new probabilistic representation of the text to be fed as input to the CNN. This representation is a matrix that stores for each word composing the message the probability that it belongs to a positive class and the probability that it belongs to a negative class. The proposed approach is evaluated on four well-known datasets HCR, OMD, STS-gold, and a dataset provided by the SemEval-2017 Workshop. The results of the experiments show that the proposed approach competes with the state-of-the-art sentiment analyzers and has the potential to detect sentiments from textual data in an effective manner.

Download Full-text

Deep learning approach for facial age classification: a survey of the state-of-the-art

Artificial Intelligence Review ◽

10.1007/s10462-020-09855-0 ◽

2020 ◽

Author(s):

Olatunbosun Agbo-Ajala ◽

Serestina Viriri

Keyword(s):

Deep Learning ◽

State Of The Art ◽

The State ◽

Learning Approach ◽

Facial Age

Download Full-text

Deep Learning Approach for Automatic Microaneurysms Detection

Sensors ◽

10.3390/s22020542 ◽

2022 ◽

Vol 22 (2) ◽

pp. 542

Author(s):

Muhammad Mateen ◽

Tauqeer Safdar Malik ◽

Shaukat Hayat ◽

Musab Hameed ◽

Song Sun ◽

...

Keyword(s):

Diabetic Retinopathy ◽

Deep Learning ◽

Early Detection ◽

Sensitivity And Specificity ◽

Classification Accuracy ◽

Vision Loss ◽

State Of The Art ◽

The State ◽

Learning Approach ◽

Manual Analysis

In diabetic retinopathy (DR), the early signs that may lead the eyesight towards complete vision loss are considered as microaneurysms (MAs). The shape of these MAs is almost circular, and they have a darkish color and are tiny in size, which means they may be missed by manual analysis of ophthalmologists. In this case, accurate early detection of microaneurysms is helpful to cure DR before non-reversible blindness. In the proposed method, early detection of MAs is performed using a hybrid feature embedding approach of pre-trained CNN models, named as VGG-19 and Inception-v3. The performance of the proposed approach was evaluated using publicly available datasets, namely “E-Ophtha” and “DIARETDB1”, and achieved 96% and 94% classification accuracy, respectively. Furthermore, the developed approach outperformed the state-of-the-art approaches in terms of sensitivity and specificity for microaneurysms detection.

Download Full-text

A rule based approach for aspect extraction in hindi reviews

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189869 ◽

2021 ◽

pp. 1-9

Author(s):

Chinmayee Ojha ◽

Manju Venugopalan ◽

Deepa Gupta

Keyword(s):

Public Opinion ◽

Social Networking ◽

Sentiment Analysis ◽

Fast Growth ◽

Rule Based ◽

The Public ◽

Aspect Extraction ◽

Automated Process ◽

Rule Based Approach ◽

Baseline Approach

Fast growth of technology and the tremendous growth of population has made millions of people to be active participants on social networking forums. The experiences shared by the participants on different websites is highly useful not only to customers to make decisions but also helps companies to maintain sustainability in businesses. Sentiment analysis is an automated process to analyze the public opinion behind certain topics. Identifying targets of user’s opinion from text is referred to as aspect extraction task, which is the most crucial and important part of Sentiment Analysis. The proposed system is a rule-based approach to extract aspect terms from reviews. A sequence of patterns is created based on the dependency relations between target and its nearby words. The system of rules works on a benchmark of dataset for Hindi shared by Akhtar et al., 2016. The evaluated results show that the proposed approach has significant improvement in extracting aspects over the baseline approach reported on the same dataset.

Download Full-text

A Novel Bio-Inspired Deep Learning Approach for Liver Cancer Diagnosis

Information ◽

10.3390/info11020080 ◽

2020 ◽

Vol 11 (2) ◽

pp. 80 ◽

Cited By ~ 1

Author(s):

Rania M. Ghoniem

Keyword(s):

Deep Learning ◽

Liver Cancer ◽

State Of The Art ◽

The State ◽

Convergence Time ◽

Computational Time ◽

Learning Approach ◽

Liver Lesions ◽

Learning Models ◽

Abc Algorithm

Current research on computer-aided diagnosis (CAD) of liver cancer is based on traditional feature engineering methods, which have several drawbacks including redundant features and high computational cost. Recent deep learning models overcome these problems by implicitly capturing intricate structures from large-scale medical image data. However, they are still affected by network hyperparameters and topology. Hence, the state of the art in this area can be further optimized by integrating bio-inspired concepts into deep learning models. This work proposes a novel bio-inspired deep learning approach for optimizing predictive results of liver cancer. This approach contributes to the literature in two ways. Firstly, a novel hybrid segmentation algorithm is proposed to extract liver lesions from computed tomography (CT) images using SegNet network, UNet network, and artificial bee colony optimization (ABC), namely, SegNet-UNet-ABC. This algorithm uses the SegNet for separating liver from the abdominal CT scan, then the UNet is used to extract lesions from the liver. In parallel, the ABC algorithm is hybridized with each network to tune its hyperparameters, as they highly affect the segmentation performance. Secondly, a hybrid algorithm of the LeNet-5 model and ABC algorithm, namely, LeNet-5/ABC, is proposed as feature extractor and classifier of liver lesions. The LeNet-5/ABC algorithm uses the ABC to select the optimal topology for constructing the LeNet-5 network, as network structure affects learning time and classification accuracy. For assessing performance of the two proposed algorithms, comparisons have been made to the state-of-the-art algorithms on liver lesion segmentation and classification. The results reveal that the SegNet-UNet-ABC is superior to other compared algorithms regarding Jaccard index, Dice index, correlation coefficient, and convergence time. Moreover, the LeNet-5/ABC algorithm outperforms other algorithms regarding specificity, F1-score, accuracy, and computational time.

Download Full-text

Approach for Machine-Printed Arabic Character Recognition: the-state-of-the-art deep-learning method

Electronic Imaging ◽

10.2352/issn.2470-1173.2018.2.vipc-176 ◽

2018 ◽

Vol 2018 (2) ◽

pp. 176-1-176-8 ◽

Cited By ~ 3

Author(s):

Daegun Ko ◽

Changhyung Lee ◽

Donghyeop Han ◽

Hyeongsu Ohk ◽

Kimin Kang ◽

...

Keyword(s):

Deep Learning ◽

Character Recognition ◽

State Of The Art ◽

The State ◽

Learning Method

Download Full-text

Deep Bidirectional LSTM Network Learning-Based Sentiment Analysis for Arabic Text

Journal of Intelligent Systems ◽

10.1515/jisys-2020-0021 ◽

2020 ◽

Vol 30 (1) ◽

pp. 395-412

Author(s):

Hanane Elfaik ◽

El Habib Nfaoui

Keyword(s):

Deep Learning ◽

Sentiment Analysis ◽

Computational Linguistics ◽

State Of The Art ◽

Contextual Information ◽

The State ◽

Great Success ◽

Bidirectional Lstm ◽

Lstm Network ◽

Arabic Sentiment Analysis

Abstract Sentiment analysis aims to predict sentiment polarities (positive, negative or neutral) of a given piece of text. It lies at the intersection of many fields such as Natural Language Processing (NLP), Computational Linguistics, and Data Mining. Sentiments can be expressed explicitly or implicitly. Arabic Sentiment Analysis presents a challenge undertaking due to its complexity, ambiguity, various dialects, the scarcity of resources, the morphological richness of the language, the absence of contextual information, and the absence of explicit sentiment words in an implicit piece of text. Recently, deep learning has obviously shown a great success in the field of sentiment analysis and is considered as the state-of-the-art model in Arabic Sentiment Analysis. However, the state-of-the-art accuracy for Arabic sentiment analysis still needs improvements regarding contextual information and implicit sentiment expressed in different real cases. In this paper, an efficient Bidirectional LSTM Network (BiLSTM) is investigated to enhance Arabic Sentiment Analysis, by applying Forward-Backward encapsulate contextual information from Arabic feature sequences. The experimental results on six benchmark sentiment analysis datasets demonstrate that our model achieves significant improvements over the state-of-art deep learning models and the baseline traditional machine learning methods.

Download Full-text

Deep Learning for text in limted data settings

10.36227/techrxiv.12100692 ◽

2020 ◽

Author(s):

Pathikkumar Patel ◽

Bhargav Lad ◽

Jinan Fiaidhi

Keyword(s):

Machine Learning ◽

Time Series ◽

Deep Learning ◽

Sentiment Analysis ◽

Transfer Learning ◽

Text Classification ◽

State Of The Art ◽

Time Series Forecasting ◽

Text Data ◽

Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

Download Full-text

Deep Learning for Transient Image Reconstruction from ToF Data

Sensors ◽

10.3390/s21061962 ◽

2021 ◽

Vol 21 (6) ◽

pp. 1962

Author(s):

Enrico Buratto ◽

Adriano Simonetto ◽

Gianluca Agresti ◽

Henrik Schäfer ◽

Pietro Zanuttigh

Keyword(s):

Deep Learning ◽

State Of The Art ◽

Light Response ◽

Real Data ◽

Depth Image ◽

Learning Approach ◽

Multiple Reflections ◽

Noisy Input ◽

Novel Approach ◽

Incoming Light

In this work, we propose a novel approach for correcting multi-path interference (MPI) in Time-of-Flight (ToF) cameras by estimating the direct and global components of the incoming light. MPI is an error source linked to the multiple reflections of light inside a scene; each sensor pixel receives information coming from different light paths which generally leads to an overestimation of the depth. We introduce a novel deep learning approach, which estimates the structure of the time-dependent scene impulse response and from it recovers a depth image with a reduced amount of MPI. The model consists of two main blocks: a predictive model that learns a compact encoded representation of the backscattering vector from the noisy input data and a fixed backscattering model which translates the encoded representation into the high dimensional light response. Experimental results on real data show the effectiveness of the proposed approach, which reaches state-of-the-art performances.

Download Full-text