Sentiment analysis (SA), also called Opinion Mining (OM) is the task of extracting and analyzing people’s opinions, sentiments, attitudes, perceptions, etc., toward different entities such as topics, products, and services. The fast evolution of Internet-based applications like websites, social networks, and blogs, leads people to generate enormous heaps of opinions and reviews about products, services, and day-to-day activities. Sentiment analysis poses as a powerful tool for businesses, governments, and researchers to extract and analyze public mood and views, gain business insight, and make better decisions. This paper presents a complete study of sentiment analysis approaches, challenges, and trends, to give researchers a global survey on sentiment analysis and its related fields. The paper presents the applications of sentiment analysis and describes the generic process of this task. Then, it reviews, compares, and investigates the used approaches to have an exhaustive view of their advantages and drawbacks. The challenges of sentiment analysis are discussed next to clarify future directions.

A comprehensive survey on sentiment analysis: Approaches, challenges and trends

Today, Cancer is one of the major lethal diseases in the world. Globally out of every three cancers diagnosed, one is identified as skin cancer. Some reports suggest that one out of every five Americans might fall prey to skin cancer in the course of their life. Early detection of the disease plays a pivotal role in the treatment of skin cancer. Though these skin lesions can be seen without the help of any external clinical device, it is a challenging task to distinguish between malignant and benign skin lesions as they are alike in their physical appearances. This leads to an increased number of unnecessary biopsies where in one study it was revealed that nearly 5,00,000 biopsies are done in children every year to diagnose a mere 400 melanomas. To tackle this problem and help dermatologists in the diagnosis process, we developed an enhanced image classification model which can act as a preliminary check before moving to a costlier biopsy. This model can identify 7 distinct types of skin lesions. An analysis has been carried out on the HAM10000 dataset. We used transfer learning utilizing multiple pre-trained models, combined with class-weighted loss and data augmentation techniques for the classification process. Experimental analysis shows that the modified ResNet50 model is capable of identifying skin lesion images into one of the seven classes with categorical accuracy, weighted average precision, and recall of 90 percent, 0.89, 0.90, respectively. Our model can be used as a clinical decision support system to help dermatologists in the diagnosis process.

Skin Cancer Classification using Transfer Learning

A comprehensive study for Arabic Sentiment Analysis (Challenges and Applications)

The evaluation of white blood cells is essential to assess the quality of the human immune system; however, the assessment of the blood smear depends on the pathologist’s expertise. Most machine learning tools make a one-level classification for white blood cell classification. This work presents a two-stage hybrid multi-level scheme that efficiently classifies four cell groups: lymphocytes and monocytes (mononuclear) and segmented neutrophils and eosinophils (polymorphonuclear). At the first level, a Faster R-CNN network is applied for the identification of the region of interest of white blood cells, together with the separation of mononuclear cells from polymorphonuclear cells. Once separated, two parallel convolutional neural networks with the MobileNet structure are used to recognize the subclasses in the second level. The results obtained using Monte Carlo cross-validation show that the proposed model has a performance metric of around 98.4% (accuracy, recall, precision, and F1-score). The proposed model represents a good alternative for computer-aided diagnosis (CAD) tools for supporting the pathologist in the clinical laboratory in assessing white blood cells from blood smear images.

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An Efficient Multi-Level Convolutional Neural Network Approach for White Blood Cells Classification

An approach for multiclass skin lesion classification based on ensemble learning

https://digitalcommons.aaru.edu.jo/cgi/viewcontent.cgi?article=1050&context=fcij

A survey on opinion summarization techniques for social media

The recent development of machine learning and deep learning techniques for medical image analysis has led to the development of intelligent diagnosis systems that can help doctors make a better diagnosis to the patients' diseases. In particular, skin diagnostics is a field where these new techniques can be applied with a high rate of accuracy. This study aims to enhance the accuracy of skin lesions classification based on two factors. The first is deeply trained all layers of implemented pre-trained models. Whereas, the second is balancing the number of images within the seven classes of dataset used. The state-of-the-art convolutional neural networks MobileNet and DenseNet-121 were trained on HAM10000 dataset. The two models pass through three phases; preprocessing, training and evaluation. Firstly, the dataset is down sampled, splitted and augmented to resolve misbalancing problem. Then, both models are deeply trained and finally they are evaluated against baseline models without balancing the classes. Multiple metrics were used to evaluate our models; precision, recall, F1-score, specificity and ROC AUC. In addition, the micro-average and macro-average of all previous metrics to extend to multi-classification. The accuracy of MobileNet and DenseNet-121 reach 82.6% and 71.9% on unseen testing images, respectively on the original dataset (i.e. before balancing the dataset). Whereas, they reach 92.7% and 91.2% on unseen testing images, respectively after balancing the dataset. This enhancement proves the necessity of existence of balanced dataset for training, to have better performance. Furthermore, MobileNet after balancing dataset has out performed the highest accuracy of ISIC 2018 challenge on the same dataset by 4.2%. For that, this model is the recommended one as it is a light-weight model, suitable for mobile applications used by dermatologists and its accuracy is comparably equal to DenseNet121.

Enhanced Skin Lesions Classification Using Deep Convolutional Networks

Leukocytes, or white blood cells (WBCs), are microscopic organisms that fight against infectious disease, bacteria, viruses, and others. The manual method to classify and count WBCs is tedious, time-consuming and may has inaccurate results, whereas the automated methods are costly. The objective of this work is to automatically identify and classify WBCs in a microscopic image into four types with higher accuracy. BCCD is the used dataset in this study, which is a scaleddown blood cell detection dataset. BCCD is firstly pre-processed by passing through several processes such as segmentation and augmentation,then it is passed to the proposed model. Our model combines the privilege of deep models in automatically extracting features with the higher classification accuracy of traditional machine learning classifiers.The proposed model consists of two main layers; a shallow tuning pre-trained model and a traditional machine learning classifier on top of it. Here, ten different pretrained models with six different machine learning are used in this study. Moreover, the fully connected network (FCN) of pretrained models is used as a baseline classifier for comparison. The evaluation process shows that the hybrid between MobileNet-224 as feature extractor with logistic regression as classifier has a higher rank-1 accuracy with 97.03%. Besides, the proposed hybrid model outperformed the baseline FCN with 25.78% on average.

/pdf/improved-white-blood-cells-classification-based-on-pre-34di39jrpz.pdf

Improved White Blood Cells Classification based on Pre-trained Deep Learning Models

On the internet, people share their opinions and sentiments in many forms of products or services. This behavior creates an extensive collection of opinions and views in the form of texts, which ne...

Ensaf Hussein Mohamed

Papers

A survey on opinion summarization techniques for social media

Enhanced Skin Lesions Classification Using Deep Convolutional Networks

Improved White Blood Cells Classification based on Pre-trained Deep Learning Models

A generic lexicon-based framework for sentiment analysis

QSST: A Quranic Semantic Search Tool based on word embedding