A Survey on Deep Learning Techniques for Sentiment Analysis

Social media is a rich source of information nowadays. If we look into social media, sentiment analysis is one of the challenging problems. Sentiment analysis is a substantial area of research in the field of Natural Language Processing. This survey paper reviews and provides the comparative study of deep learning approaches CNN, RNN, LSTM and ensemble-based methods

Stance detection on social media: State of the art and trends

Stance detection on social media is an emerging opinion mining paradigm for various social and political applications in which sentiment analysis may be sub-optimal. There has been a growing research interest for developing effective methods for stance detection methods varying among multiple communities including natural language processing, web science, and social computing. This paper surveys the work on stance detection within those communities and situates its usage within current opinion mining techniques in social media. It presents an exhaustive review of stance detection techniques on social media, including the task definition, different types of targets in stance detection, features set used, and various machine learning approaches applied. The survey reports state-of-the-art results on the existing benchmark datasets on stance detection, and discusses the most effective approaches. In addition, this study explores the emerging trends and different applications of stance detection on social media. The study concludes by discussing the gaps in the current existing research and highlights the possible future directions for stance detection on social media.Comment: We request withdrawal of this article sincerely. We will re-edit this paper. Please withdraw this article before we finish the new versio

Sentiment Analysis or Opinion Mining: A Review

Opinion Mining (OM) or Sentiment Analysis (SA) can be defined as the task of detecting, extracting and classifying opinions on something. It is a type of the processing of the natural language (NLP) to track the public mood to a certain law, policy, or marketing, etc. It involves a way that development for the collection and examination of comments and opinions about legislation, laws, policies, etc., which are posted on the social media. The process of information extraction is very important because it is a very useful technique but also a challenging task. That mean, to extract sentiment from an object in the web-wide, need to automate opinion-mining systems to do it. The existing techniques for sentiment analysis include machine learning (supervised and unsupervised), and lexical-based approaches. Hence, the main aim of this paper presents a survey of sentiment analysis (SA) and opinion mining (OM) approaches, various techniques used that related in this field. As well, it discusses the application areas and challenges for sentiment analysis with insight into the past researcher's works

High-performance network traffic processing systems using commodity hardware

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-36784-7_1The Internet has opened new avenues for information ac- cessing and sharing in a variety of media formats. Such popularity has resulted in an increase of the amount of resources consumed in backbone links, whose capacities have witnessed numerous upgrades to cope with the ever-increasing demand for bandwidth. Consequently, network tra c processing at today's data transmission rates is a very demanding task, which has been traditionally accomplished by means of specialized hard- ware tailored to speci c tasks. However, such approaches lack either of exibility or extensibility|or both. As an alternative, the research com- munity has pointed to the utilization of commodity hardware, which may provide exible and extensible cost-aware solutions, ergo entailing large reductions of the operational and capital expenditure investments. In this chapter, we provide a survey-like introduction to high-performance network tra c processing using commodity hardware. We present the required background to understand the di erent solutions proposed in the literature to achieve high-speed lossless packet capture, which are reviewed and compared

Computational Sociolinguistics: A Survey

Language is a social phenomenon and variation is inherent to its social nature. Recently, there has been a surge of interest within the computational linguistics (CL) community in the social dimension of language. In this article we present a survey of the emerging field of "Computational Sociolinguistics" that reflects this increased interest. We aim to provide a comprehensive overview of CL research on sociolinguistic themes, featuring topics such as the relation between language and social identity, language use in social interaction and multilingual communication. Moreover, we demonstrate the potential for synergy between the research communities involved, by showing how the large-scale data-driven methods that are widely used in CL can complement existing sociolinguistic studies, and how sociolinguistics can inform and challenge the methods and assumptions employed in CL studies. We hope to convey the possible benefits of a closer collaboration between the two communities and conclude with a discussion of open challenges.Comment: To appear in Computational Linguistics. Accepted for publication: 18th February, 201

Preferences and Learning Behaviors of Digital Natives

Because students’ lives today are saturated with digital media at a time when their brains are still developing, several popular press authors suggest that media use has profoundly affected students’ abilities, preferences, and attitudes related to learning. They claim that “digital natives” (often defined as those born after 1980) have a distinctive set of characteristics that includes preference for speed, nonlinear processing, multitasking, and social learning, allegedly developed through immersion in digital technology during childhood and adolescence. The purpose of this project is to explore claims that the digital “native” generation as learners demonstrate different learning behaviors, by exploring relationships between technology use and productive learning habits. This study will test theoretical assumptions in the literature and popular press, and gather data through survey research to address the possible connection between technology use and learning by asking university students in to report patterns of use across a variety of technologies, as well as their preferences and behaviors when learning about topics that interest them. This data will help us better understand how digital natives themselves see their technology use and approaches to learning, which may in turn provide an empirical basis for both curriculum design that provides students with opportunities for more productive learning behaviors, and academic success. Updated research on this topic can provide better direction for developing the tools and approaches best suited to the delivery of higher education

Identifying Emotions in Social Media: Comparison of Word-emotion lexica

In recent years, emotions expressed in social media messages have become a vivid research topic due to their influence on the spread of misinformation and online radicalization over online social networks. Thus, it is important to correctly identify emotions in order to make inferences from social media messages. In this paper, we report on the performance of three publicly available word-emotion lexicons (NRC, DepecheMood, EmoSenticNet) over a set of Facebook and Twitter messages. To this end, we designed and implemented an algorithm that applies natural language processing (NLP) techniques along with a number of heuristics that reflect the way humans naturally assess emotions in written texts. In order to evaluate the appropriateness of the obtained emotion scores, we conducted a questionnaire-based survey with human raters. Our results show that there are noticeable differences between the performance of the lexicons as well as with respect to emotion scores the human raters provided in our surve

Spoken content retrieval: A survey of techniques and technologies

Speech media, that is, digital audio and video containing spoken content, has blossomed in recent years. Large collections are accruing on the Internet as well as in private and enterprise settings. This growth has motivated extensive research on techniques and technologies that facilitate reliable indexing and retrieval. Spoken content retrieval (SCR) requires the combination of audio and speech processing technologies with methods from information retrieval (IR). SCR research initially investigated planned speech structured in document-like units, but has subsequently shifted focus to more informal spoken content produced spontaneously, outside of the studio and in conversational settings. This survey provides an overview of the field of SCR encompassing component technologies, the relationship of SCR to text IR and automatic speech recognition and user interaction issues. It is aimed at researchers with backgrounds in speech technology or IR who are seeking deeper insight on how these fields are integrated to support research and development, thus addressing the core challenges of SCR