Emotion Detection for Health and Well-being in Short Messaging Systems

The exposure to unpleasant emotions or content in messages can lead to health complications, including high blood pressure and several heart-related disorders. Hence, the identification of unpleasant emotions in written content can serve as a beneficial instrument in addressing certain health-related issues. Emotions can be communicated through diverse modalities, including written text, spoken language, and facial gestures. The objective of this work is to create a Text-based emotion detection system that possesses the capability to accurately identify emotions within text messages. The use of message filtering mechanisms that detect and block content containing negative emotions can serve as a preventive measure to shield users from accessing messages that have the potential to adversely impact their well-being. Conversely, messages that convey positive or neutral emotions remain accessible for comprehension. In order to accomplish this objective, a combination of three machine learning algorithms, namely Naive Bayes, Support Vector Machine, and Logistic Regression, were employed, adhering to the CRISP-DM approach. The Logistic Regression technique achieved the greatest accuracy rate of 98.4% and was employed in the construction of the detection system. The Graphical User Interface (GUI) of the system was developed utilizing HTML and CSS, with the integration of diverse components to establish a comprehensive and operational interface for the user

Natural Language Processing for Prediction of Election Results on Twitter Engagement and Polls

With the ability to predict political outcomes and provide insights into public opinion, using Twitter data to predict election results has gained popularity. Twitter offers a massive supply of data for analysis due to its enormous user base and real-time nature. Researchers use sentiment analysis tools to categorize tweets as good, harmful, or neutral and follow sentiment patterns over time. Network analysis finds influential users and digs deeper into the dynamics of political discourse. The accuracy of predictions is improved by combining traditional polling data with machine learning methods. Twitter data analysis has the potential to offer insightful information for election campaigns and improve political strategies despite issues like representativeness and identifying genuine sentiment. Ongoing research focuses on refining methodologies and addressing limitations, advancing the reliability of election prediction using Twitter data. The paper shows the results of election prediction for Indian political parties based on Twitter dat

Emotional Tendency Analysis of Twitter Data Streams

The web now seems to be an alive and dynamic arena in which billions of people across the globe connect, share, publish, and engage in a broad range of everyday activities. Using social media, individuals may connect and communicate with each other at any time and from any location. More than 500 million individuals across the globe post their thoughts and opinions on the internet every day. There is a huge amount of information created from a variety of social media platforms in a variety of formats and languages throughout the globe. Individuals define emotions as powerful feelings directed toward something or someone as a result of internal or external events that have a personal meaning. Emotional recognition in text has several applications in human-computer interface and natural language processing (NLP). Emotion classification has previously been studied using bag-of words classifiers or deep learning methods on static Twitter data. For real-time textual emotion identification, the proposed model combines a mix of keyword-based and learning-based models, as well as a real-time Emotional Tendency Analysi

Building A Malay-English Code-Switching Subjectivity Corpus For Sentiment Analysis

Combining of local and foreign language in single utterance has become a norm in multi-ethnic region. This phenomenon is known as code-switching. Code-switching has become a new challenge in sentiment analysis when the Internet users express their opinion in blogs, reviews and social network sites. The resources to process code-switching text in sentiment analysis is scarce especially annotated corpus. This paper develops a guideline to build a code-switching subjectivity corpus for a mix of Malay and English language known as MY-EN-CS. The guideline is suitable for any code-switching textual document. This paper built a new MY-EN-CS to demonstrate the guideline. The corpus consists of opinionated and factual sentences that are constructed from combination of words from these the languages. The sentences were retrieved from blogs and MY-EN-CS sentences are identified and annotated either as opinionated or factual. The annotated task yields 0.83 Kappa value rate that indicates the reliability of this corpus

A multilevel paradigm for deep convolutional neural network features selection with an application to human gait recognition

Human gait recognition (HGR) shows high importance in the area of video surveillance due to remote access and security threats. HGR is a technique commonly used for the identification of human style in daily life. However, many typical situations like change of clothes condition and variation in view angles degrade the system performance. Lately, different machine learning (ML) techniques have been introduced for video surveillance which gives promising results among which deep learning (DL) shows best performance in complex scenarios. In this article, an integrated framework is proposed for HGR using deep neural network and fuzzy entropy controlled skewness (FEcS) approach. The proposed technique works in two phases: In the first phase, deep convolutional neural network (DCNN) features are extracted by pre-trained CNN models (VGG19 and AlexNet) and their information is mixed by parallel fusion approach. In the second phase, entropy and skewness vectors are calculated from fused feature vector (FV) to select best subsets of features by suggested FEcS approach. The best subsets of picked features are finally fed to multiple classifiers and finest one is chosen on the basis of accuracy value. The experiments were carried out on four well-known datasets, namely, AVAMVG gait, CASIA A, B and C. The achieved accuracy of each dataset was 99.8, 99.7, 93.3 and 92.2%, respectively. Therefore, the obtained overall recognition results lead to conclude that the proposed system is very promising

A trend study on the impact of social media in decision making

Social media has grown steadily during the last decade and it is now considered as a new opportunity to use for different purposes such as decision making. The primary objective of this paper is to review articles related to social media and decision making using manual and bibliometrics anal-ysis methods, and to identify top themes in these articles. We have reviewed the papers published between 2008 and the first month of 2019 in Scopus where 1,159 articles were published in this period. These articles come from 733 sources and 3,459 authors. According to our survey, United States is the most productive country. Moreover, most collaborations occurred between two coun-tries of United States and United Kingdom as well as between United States and China. The bibliometrics analysis examines global research in this field from the different point of views