Data Mining - Concepts and Techniques.

Text summarization and sentiment classification both aim to capture the main ideas of the text but at different levels. Text summarization is to describe the text within a few sentences, while sentiment classification can be regarded as a special type of summarization which "summarizes" the text into a even more abstract fashion, i.e., a sentiment class. Based on this idea, we propose a hierarchical end-to-end model for joint learning of text summarization and sentiment classification, where the sentiment classification label is treated as the further "summarization" of the text summarization output. Hence, the sentiment classification layer is put upon the text summarization layer, and a hierarchical structure is derived. Experimental results on Amazon online reviews datasets show that our model achieves better performance than the strong baseline systems on both abstractive summarization and sentiment classification.

A Hierarchical End-to-End Model for Jointly Improving Text Summarization and Sentiment Classification

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

A survey on opinion summarization techniques for social media

Health care twitter analysis deals with the health related tweets through sentimental analysis by the patients themselves. The application of sentiment analysis has grown enormously. Its application in health care has great potential to analyze and improve the health of a country. In this paper, we try to analyze health tweets for Depression, Anxiety from the mixed tweets by using Multinomial Naive Bayes and Support Vector Regression (SVR) Algorithm as a classifier.

Mining Twitter Data for Depression Detection

Thyroid disease arises from an anomalous growth of thyroid tissue at the verge of the thyroid gland. Thyroid disorderliness normally ensues when this gland releases abnormal amounts of hormones where hypothyroidism (inactive thyroid gland) and hyperthyroidism (hyperactive thyroid gland) are the two main types of thyroid disorder. This study proposes the use of efficient classifiers by using machine learning algorithms in terms of accuracy and other performance evaluation metrics to detect and diagnose thyroid disease. This research presents an extensive analysis of different classifiers which are K-nearest neighbor (KNN), Naive Bayes, support vector machine, decision tree and logistic regression implemented with or without feature selection techniques. Thyroid data were taken from DHQ Teaching Hospital, Dera Ghazi Khan, Pakistan. Thyroid dataset was unique and different from other existing studies because it included three additional features which were pulse rate, body mass index and blood pressure. Experiment was based on three iterations; the first iteration of the experiment did not employ feature selection while the second and third were with L1-, L2-based feature selection technique. Evaluation and analysis of the experiment have been done which consisted of many factors such as accuracy, precision and receiver operating curve with area under curve. The result indicated that classifiers which involved L1-based feature selection achieved an overall higher accuracy (Naive Bayes 100%, logistic regression 100% and KNN 97.84%) compared to without feature selection and L2-based feature selection technique.

Performance Analysis of Machine Learning Algorithms for Thyroid Disease

Personality is an important parameter as it differentiates various individuals from one another. Personality prediction is an evergreen area of research. Predicting personality with the help of data through social media is a promising approach as this method does not require any questionnaires to be filled by users thus reducing time and increasing credibility. Thus having knowledge of personality is an interesting domain for researchers to work on. Predicting personality has many applications in real world. Use of social media is increasing day by day. Huge amount of textual data as well as images continue to explode to the web daily. Current work focuses on Linear Discriminate Analysis, Multinomial Naive Bayes and AdaBoost over Twitter standard dataset.

Using textual data for Personality Prediction:A Machine Learning Approach

Online health communities continue to offer huge variety of medical information useful for medical practitioners, system administrators and patients alike. In this work we collect real time health posts from reputed websites, where patients express their views, including their experiences and side-effects on drugs used by them. We propose to perform Summarization of user posts per drug, and come out with useful conclusions for medical fraternity as well as patient community at a glance. Further, we propose to classify the users based on their ‘emotional state of mind’. Also, we shall perform knowledge discovery from user posts, whereby useful ‘patterns’ about the triad ‘drugs-symptoms-medicine’ is done by Association Rule Mining.

Summarization and sentiment analysis from user health posts

Machine learning has gained tremendous attention from researchers recently. It has wide applications in tasks such as prediction and classification. Current work focuses on the effective detection of the personality of social network users. Personality is a combination of one’s thinking and behavior. Having knowledge about personality of a person has many applications in real world such as varied recommendation systems or HR departments. Personality of a person can be better understood by interacting with him/her. Predicting personality using social media is a new approach where direct interaction with people can be eliminated and accurate predictions can be built. Although different machine learning methods have been used by researchers recently for the task of prediction, the use of Ensembles has not been explored. Current work focuses on advanced classifiers such as XGBoost and Ensemble for prediction. Experimentation on the real-time Twitter dataset indicates high accuracy of 82.59% with an Ensemble. These results are encouraging for future research.

Personality Prediction of Social Network Users Using Ensemble and XGBoost

Determining the image sentiment is a tedious task for classification algorithms, owing to complexities in the raw images as well as the intangible nature of human sentiments. Classifying image sentiments is an evergreen research area, especially in social data analytics. In current times, it is a common practice for majority people to precise their feelings on the web by substituting text with the upload of images via a multiplicity of social media sites like Facebook, Instagram, Twitter as well as any other platform. To identify the emotions from visual cues, some visual features as well as image processing techniques are used. Several existing systems have already introduced emotion detection using machine learning techniques, but the traditional feature extraction strategies do not achieve the required accuracy on random objects. In the entire process, normalization of image, feature extraction, and feature selection are important tasks in the train module. This work articulates the newest developments in the field of image sentiment employing deep learning techniques. Also, the use of conventional machine learning techniques is compared along with deep learning algorithms. It is indicative that a combination of fast recurrent neural networks and CNN may produce high accuracy with minimum time complexity. It is noted from the survey that existing researchers reflect CNN provides around 96.50% average accuracy for sentiment classification on the flicker image dataset.

Image Sentiment Analysis Using Deep Learning

Diagnosis is an important task in medical science because of its criticality, efficiency and accuracy in determining whether or not a patient has a particular disease. This shall further decide the most suitable line of treatment. There has been a large increase in the number of thyroid cases over the past few years. Since thyroid has a complex relation with metabolism and body weight, it is extremely important to diagnose thyroid disease as early as possible. This paper presents an exhaustive survey of work done in the past with respect to semi-automated medical diagnosis in general and thyroid disease diagnosis in particular. Medical Diagnosis encompassed the use of classifiers like Fuzzy Neural Networks, k- Nearest Neighbor and Decision Tree, while the latter included the use of Computer-Aided Diagnosis, different Neural Networks and Support Vector Machine. Amongst these, the impact of Feature Selection using Particle Swarm Optimization and Ant Colony Optimization on classification was also surveyed.

Suja S. Panicker

Papers

Using textual data for Personality Prediction:A Machine Learning Approach

Summarization and sentiment analysis from user health posts

Personality Prediction of Social Network Users Using Ensemble and XGBoost

Image Sentiment Analysis Using Deep Learning

Thyroid disease diagnosis: A survey