Emotion recognition in text is an important natural language processing (NLP) task whose solution can benefit several applications in different fields, including data mining, e-learning, information filtering systems, human–computer interaction, and psychology. Explicit emotion recognition in text is the most addressed problem in the literature. The solution to this problem is mainly based on identifying keywords. Implicit emotion recognition is the most challenging problem to solve because such emotion is typically hidden within the text, and thus, its solution requires an understanding of the context. There are four main approaches for implicit emotion recognition in text: rule-based approaches, classical learning-based approaches, deep learning approaches, and hybrid approaches. In this paper, we critically survey the state-of-the-art research for explicit and implicit emotion recognition in text. We present the different approaches found in the literature, detail their main features, discuss their advantages and limitations, and compare them within tables. This study shows that hybrid approaches and learning-based approaches that utilize traditional text representation with distributed word representation outperform the other approaches on benchmark corpora. This paper also identifies the sets of features that lead to the best-performing approaches; highlights the impacts of simple NLP tasks, such as part-of-speech tagging and parsing, on the performances of these approaches; and indicates some open problems.

A survey of state-of-the-art approaches for emotion recognition in text

This chapter aims to examine the challenges to, and opportunities for, promoting Human Computer Interaction (HCI) and usability guidelines and principles through reflective journal assessment by information systems students from the Australian and Portuguese higher education sectors. In order to raise students' awareness of HCI and aspects of usability, especially in the Web development process, a new unit was developed by the first researcher called Information Systems 650 (IS650) in Australia. From this unit was derived the Web Site Planning and Development (WSPD) course introduced in Portugal. The reflective journal assessment approach was employed to enhance students' learning and knowledge of HCI and its usability aspects. This study provides empirical evidence from 64 students from Australia and Portugal, based on quantitative and qualitative data derived from three sources: students' formal and informal feedback and an online survey. Students confirmed that the use of reflective journal assessment consolidated their understanding of HCI and usability guidelines and principles and improved their reading, searching, researching, and writing skills, and their proficiency with the endnote software.

Promoting human-computer interaction and usability guidelines and principles through reflective journal assessment

Emotion classification in text has growing interest among NLP experts due to the enormous availability of people's emotions and its emergence on various Web 2.0 applications/services. Emotion classification in the Bengali texts is also gradually being considered as an important task for sports, e-commerce, entertainments, and security applications. However, It is a very critical task to develop an automatic emotion classification system for low-resource languages such as, Bengali. Scarcity of resources and deficiency of benchmark corpora make the task more complicated. Thus, the development of a benchmark corpus is the prerequisite to develop an emotion classifier for Bengali texts. This paper describes the development of an emotional corpus (hereafter called 'BEmoC') for classifying six emotions in Bengali texts. The corpus development process consists of four key steps: data crawling, pre-processing, labelling, and verification. A total of 7000 texts are labelled into six basic emotion categories such as anger, fear, surprise, sadness, joy, and disgust, respectively. Dataset evaluation with 0.969 Cohen's κ score indicates the close agreement between the corpus annotators and the expert. The analysis of evaluation also represents that the distribution of emotion words obeys Zipf's law. Moreover, the results of BEmoC analysis shown in terms of coding reliability, emotion density, and most frequent emotion words, respectively. 

/pdf/bemoc-a-corpus-for-identifying-emotion-in-bengali-texts-2gvqmnru.pdf

BEmoC: A Corpus for Identifying Emotion in Bengali Texts

The purpose of this study was to investigate the effectiveness of Emotional Motivational Feedback Message (EMFEM) in an online learning environment. This exploratory research was conducted using mixed method single case study design. Participants were 15 undergraduate students enrolled in an instructional technology course in a large state university located in an urban region in the southeastern part of the United States during Fall 2013. Multiple data collection strategies were employed in this study such as a course interest survey, IT attitudes survey, open-ended questionnaires, research journals, forum/discussion postings, emails, reflection papers and warm-up surveys. The findings showed EMFEM helped to increase and maintain students’ motivation. Students liked the EMFEM and the style of the instructor’s teaching by giving emotional motivational feedback. Students had a closer and friendlier relationship with the instructor; they also started to use more emotional content themselves.

/pdf/student-and-instructor-responses-to-emotional-motivational-2vuexp5nt1.pdf

Student and Instructor Responses to Emotional Motivational Feedback Messages in an Online Instructional Environment.

/pdf/bemoc-a-corpus-for-identifying-emotion-in-bengali-texts-1ehaypwa.pdf

In this modern era, we depend more and more on machines for day to day activities However, there is a huge gap between computer and human in emotional thinking, which is the central factor in human communication This gap can be bridged by implementing several computational approaches, which induce emotional intelligence into a machine Emotion detection from text is one such method to make the computers emotionally intelligent because text is one of themajor media for communication among humans and with the computers In this paper, we propose an approachwhich adds natural language processing techniques to improve the performance of learning based emotion classifier by considering the syntactic and semantic features of text We also present a comprehensive overview of emerging field of emotion detection from text

A Learning Based Emotion Classifier with Semantic Text Processing

Advances in imaging and computing hardware have led to an explosion in the use of color images in image processing, graphics and computer vision applications across various domains such as medical imaging, satellite imagery, document analysis and biometrics to name a few. However, these images are subjected to a wide variety of distortions during its acquisition, subsequent compression, transmission, processing and then reproduction, which degrade their visual quality. Hence objective quality assessment of color images has emerged as one of the essential operations in image processing. During the last two decades, efforts have been put to design such an image quality metric which can be calculated simply but can accurately reflect subjective quality of human perception. In this paper, the authors evaluated the quality assessment of color images using SSIM structural similarity index metric across various color spaces. They experimented to study the effect of color spaces in metric based and distance based quality assessment. The authors proposed a metric using CIE Lab color space and SSIM, which has better correlation to the subjective assessment in a benchmark dataset.

Analysis of SSIM based Quality Assessment across Color Channels of Images

Advances in imaging and computing hardware have led to an explosion in the use of color images in image processing, graphics and computer vision applications across various domains such as medical imaging, satellite imagery, document analysis and biometrics to name a few. However, these images are subjected to wide variety of distortions during its acquisition, subsequent compression, transmission, processing and then reproduction, which degrade their visual quality. Hence objective quality assessment of color images has emerged as one of the essential operation in image processing. During the last two decades, efforts have been put to design such an image quality metric which can be calculated simply but can accurately reflect subjective quality of human perception. In this paper, we evaluated the quality assessment of color images using CIE proposed Lab color space, which is considered to be perceptually uniform space. In addition we have used two different approaches of quality assessment namely, metric based and distance based.

Quality Assessment of Images Using SSIM Metric and CIEDE2000 Distance Methods in Lab Color Space

Automatic extraction and vectorization of contour lines from color topographic maps is an important precursor to obtaining useful information for many vector based GIS applications. In this work, a novel hybridized algorithm is developed for reconstructing the extracted contour lines from color topographic map. The extraction of contour lines from a topographic map leads to broken contour lines due to inherent characteristics of the map, thus posing a challenging problem of identifying gaps and then filling them. This has been addressed by developing algorithms based on connected components, graph theory, Expectation Maximization (EM) and numerical methods. Our algorithm operates by isolating the segments of those contours which have gaps and achieves in reducing the complexity of the matching of such segments by employing the EM algorithm. We also present a new scheme of filling gaps present in thick contours without the application of thinning algorithms.

Automatic Gap Identification towards Efficient Contour Line Reconstruction in Topographic Maps

In image registration and retrieval developments, feature point detection is essential to find areas in which descriptors are intended. Most of the existing methods use only the intensity information of the images to find the feature points. We inspect the use of color information in feature point detection. Color Information in images is expressed using various color spaces like RGB, HSV, XYZ, LAB, Opponent, YIQ, Y CbCr, and CMY. Deciding the most appropriate color space for a particular application is an open problem in color image processing. To alleviate this problem, a Hybrid color space, with the best features is used for image matching. Feature selection is done using Principal Component Analysis (PCA). Harris corner detection is applied on color images, represented using different color spaces. The feature detection method is compared for viewpoint, rotation, blur and illumination changes. All the experiments use total number of feature points and repeatability measurement for the evaluation.

Evaluation of Color Spaces for Feature Point Detection in Image Matching Application

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