Enhanced SenticNet with Affective Labels for Concept-Based Opinion Mining
TL;DR: The presented methodology enriches SenticNet concepts with affective information by assigning an emotion label by way of concept-based opinion mining.
Abstract: SenticNet 1.0 is one of the most widely used, publicly available resources for concept-based opinion mining. The presented methodology enriches SenticNet concepts with affective information by assigning an emotion label.
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TL;DR: Extensive experiments on various widely used classification data sets show that the proposed algorithm achieves better and faster convergence than the existing state-of-the-art hierarchical learning methods, and multiple applications in computer vision further confirm the generality and capability of the proposed learning scheme.
Abstract: Extreme learning machine (ELM) is an emerging learning algorithm for the generalized single hidden layer feedforward neural networks, of which the hidden node parameters are randomly generated and the output weights are analytically computed. However, due to its shallow architecture, feature learning using ELM may not be effective for natural signals (e.g., images/videos), even with a large number of hidden nodes. To address this issue, in this paper, a new ELM-based hierarchical learning framework is proposed for multilayer perceptron. The proposed architecture is divided into two main components: 1) self-taught feature extraction followed by supervised feature classification and 2) they are bridged by random initialized hidden weights. The novelties of this paper are as follows: 1) unsupervised multilayer encoding is conducted for feature extraction, and an ELM-based sparse autoencoder is developed via $\ell _{1}$ constraint. By doing so, it achieves more compact and meaningful feature representations than the original ELM; 2) by exploiting the advantages of ELM random feature mapping, the hierarchically encoded outputs are randomly projected before final decision making, which leads to a better generalization with faster learning speed; and 3) unlike the greedy layerwise training of deep learning (DL), the hidden layers of the proposed framework are trained in a forward manner. Once the previous layer is established, the weights of the current layer are fixed without fine-tuning. Therefore, it has much better learning efficiency than the DL. Extensive experiments on various widely used classification data sets show that the proposed algorithm achieves better and faster convergence than the existing state-of-the-art hierarchical learning methods. Furthermore, multiple applications in computer vision further confirm the generality and capability of the proposed learning scheme.
1,166 citations
TL;DR: A rigorous survey on sentiment analysis is presented, which portrays views presented by over one hundred articles published in the last decade regarding necessary tasks, approaches, and applications of sentiment analysis.
Abstract: With the advent of Web 2.0, people became more eager to express and share their opinions on web regarding day-to-day activities and global issues as well. Evolution of social media has also contributed immensely to these activities, thereby providing us a transparent platform to share views across the world. These electronic Word of Mouth (eWOM) statements expressed on the web are much prevalent in business and service industry to enable customer to share his/her point of view. In the last one and half decades, research communities, academia, public and service industries are working rigorously on sentiment analysis, also known as, opinion mining, to extract and analyze public mood and views. In this regard, this paper presents a rigorous survey on sentiment analysis, which portrays views presented by over one hundred articles published in the last decade regarding necessary tasks, approaches, and applications of sentiment analysis. Several sub-tasks need to be performed for sentiment analysis which in turn can be accomplished using various approaches and techniques. This survey covering published literature during 2002-2015, is organized on the basis of sub-tasks to be performed, machine learning and natural language processing techniques used and applications of sentiment analysis. The paper also presents open issues and along with a summary table of a hundred and sixty-one articles.
1,011 citations
Cites background from "Enhanced SenticNet with Affective L..."
...S# Tasks and applications #Articles References 1 Subjectivity Classification 6 [44], [75], [110], [163], [167], [174] 2 Polarity determination 43 [12], [26], [29], [32], [33], [35], [40], [45], [48], [50], [54], [57], [66], [85], [95], [96], [108], [109], [112], [114], [123], [126], [154], [156], [157], [160], [162], [165], [166], [168], [169], [170], [171], [172], [176], [177], [178], [179], [180], [203], [205], [206], [209] 3 Vagueness in opinionated text 5 [22], [41], [86], [216], [217] 4 Multi- & cross-lingual SA 6 [46], [88], [94], [115], [148], [173] 5 Cross-domain SA 4 [36], [98], [99], [121] 6 Review usefulness measurement 13 [76], [78], [81], [130], [221], [222], [223], [224], [225], [226], [227], [228], [229] 7 Opinion spam detection 7 [199], [200], [212], [216], [220], [231], [232] 8 Lexica and corpora creation 22 [21], [23], [24], [30], [52], [55], [56], [69], [74], [97], [106], [111], [116], [117], [118], [127], [136], [202], [207], [211], [213], [214] 9 Opinion word and aspects extraction, entity recognition, name disambiguation 36 [8], [11], [25], [27], [35], [37], [59], [60], [61], [62], [63], [67], [68], [92],[93], [100], [101], [102], [107], [125], [132], [175], [182], [185], [186], [189], [190], [191], [193], [194], [195], [196], [218], [240], [241], [243] 10 Applications of SA 21 [13], [18], [43], [47], [49], [51], [53], [58], [64], [73], [77], [79], [80], [90], [91], [124], [131], [155], [158], [183], [184] Total 163...
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...[23] with emotion label taken from WNA [4]....
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...In order to get a sense of the extracted text, numerous research efforts have been witnessed in recent years leading to automated SA, an extended NLP area of research [23]....
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...Sentence level 20 [24], [29], [46], [58], [100], [108], [124], [125], [160], [168], [171], [173], [174], [178], [183], [186], [190], [191], [193], [213] Concept level 9 [21], [23], [30], [52], [95], [98], [202], [214], [216] Phrase level 3 [12], [162], [172] Link based 3 [11], [49], [70] Clause level 2 [29], [170] Sense level 1 [75]...
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TL;DR: An insight into ELMs in three aspects, viz: random neurons, random features and kernels is provided and it is shown that in theory ELMs (with the same kernels) tend to outperform support vector machine and its variants in both regression and classification applications with much easier implementation.
Abstract: Extreme learning machines (ELMs) basically give answers to two fundamental learning problems: (1) Can fundamentals of learning (i.e., feature learning, clus- tering, regression and classification) be made without tuning hidden neurons (including biological neurons) even when the output shapes and function modeling of these neurons are unknown? (2) Does there exist unified frame- work for feedforward neural networks and feature space methods? ELMs that have built some tangible links between machine learning techniques and biological learning mechanisms have recently attracted increasing attention of researchers in widespread research areas. This paper provides an insight into ELMs in three aspects, viz: random neurons, random features and kernels. This paper also shows that in theory ELMs (with the same kernels) tend to outperform support vector machine and its variants in both regression and classification applications with much easier implementation.
871 citations
01 Jan 2014
TL;DR: This survey article reinterprets the evolution of NLP research as the intersection of three overlapping curves-namely Syntactics, Semantics, and Pragmatics Curves which will eventually lead NLPResearch to evolve into natural language understanding.
768 citations
TL;DR: This article reinterpreted the evolution of NLP research as the intersection of three overlapping curves-namely Syntactics, Semantics, and Pragmatics Curves-which will eventually lead NLP to evolve into natural language understanding.
Abstract: Natural language processing (NLP) is a theory-motivated range of computational techniques for the automatic analysis and representation of human language. NLP research has evolved from the era of punch cards and batch processing (in which the analysis of a sentence could take up to 7 minutes) to the era of Google and the likes of it (in which millions of webpages can be processed in less than a second). This review paper draws on recent developments in NLP research to look at the past, present, and future of NLP technology in a new light. Borrowing the paradigm of `jumping curves? from the field of business management and marketing prediction, this survey article reinterprets the evolution of NLP research as the intersection of three overlapping curves-namely Syntactics, Semantics, and Pragmatics Curves- which will eventually lead NLP research to evolve into natural language understanding.
553 citations
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TL;DR: Key issues in affective computing, " computing that relates to, arises from, or influences emotions", are presented and new applications are presented for computer-assisted learning, perceptual information retrieval, arts and entertainment, and human health and interaction.
Abstract: Computers are beginning to acquire the ability to express and recognize affect, and may soon be given the ability to " have emotions. " The essential role of emotion in both human cognition and perception, as demonstrated by recent neurological studies, indicates that affective computers should not only provide better performance in assisting humans, but also might enhance computers' abilities to make decisions. This paper presents and discusses key issues in " affective computing, " computing that relates to, arises from, or influences emotions. Models are suggested for computer recognition of human emotion, and new applications are presented for computer-assisted learning, perceptual information retrieval, arts and entertainment, and human health and interaction. Affective computing, coupled with new wear-able computers, will also provide the ability to gather new data necessary for advances in emotion and cog-nition theory. Nothing in life is to be feared. It is only to be understood. – Marie Curie Emotions have a stigma in science; they are believed to be inherently non-scientific. Scientific principles are derived from rational thought, logical arguments, testable hypotheses, and repeatable experiments. There is room alongside science for " non-interfering " emotions such as those involved in curiosity, frustration, and the pleasure of discovery. In fact, much scientific research has been prompted by fear. Nonetheless, the role of emotions is marginalized at best. Why bring " emotion " or " affect " into any of the deliberate tools of science? Moreover, shouldn't it be completely avoided when considering properties to design into computers? After all, computers control significant parts of our lives – the phone system, the stock market, nuclear power plants, jet landings, and more. Who wants a computer to be able to " feel angry " at them? To feel contempt for any living thing? In this essay I will submit for discussion a set of ideas on what I call " affective computing, " computing that relates to, arises from, or influences emotions. This will need some further clarification which I shall attempt below. I should say up front that I am not proposing the pursuit of computerized cingulotomies 1 or even into the business of building " emotional computers ". 1 The making of small wounds in the ridge of the limbic system known as the cingulate gyrus, a surgical procedure to aid severely depressed patients. Nor will I propose answers to the difficult and intriguing questions , " …
5,700 citations
TL;DR: In this article, the authors define emotion as a phenomenon to be studied, without consensual conceptualization and operationalization of exactly what phenomenon is to be investigated. But progress in theory and research is difficult to a...
Abstract: Defining “emotion” is a notorious problem. Without consensual conceptualization and operationalization of exactly what phenomenon is to be studied, progress in theory and research is difficult to a...
3,247 citations
Proceedings Article•
01 Jan 2006TL;DR: SENTIWORDNET is a lexical resource in which each WORDNET synset is associated to three numerical scores Obj, Pos and Neg, describing how objective, positive, and negative the terms contained in the synset are.
Abstract: Opinion mining (OM) is a recent subdiscipline at the crossroads of information retrieval and computational linguistics which is concerned not with the topic a document is about, but with the opinion it expresses. OM has a rich set of applications, ranging from tracking users opinions about products or about political candidates as expressed in online forums, to customer relationship management. In order to aid the extraction of opinions from text, recent research has tried to automatically determine the PNpolarity of subjective terms, i.e. identify whether a term that is a marker of opinionated content has a positive or a negative connotation. Research on determining whether a term is indeed a marker of opinionated content (a subjective term) or not (an objective term) has been instead much scarcer. In this work we describe SENTIWORDNET, a lexical resource in which each WORDNET synset sis associated to three numerical scores Obj(s), Pos(s) and Neg(s), describing how objective, positive, and negative the terms contained in the synset are. The method used to develop SENTIWORDNET is based on the quantitative analysis of the glosses associated to synsets, and on the use of the resulting vectorial term representations for semi-supervised synset classi.cation. The three scores are derived by combining the results produced by a committee of eight ternary classi.ers, all characterized by similar accuracy levels but different classification behaviour. SENTIWORDNET is freely available for research purposes, and is endowed with a Web-based graphical user interface.
2,625 citations
01 Jan 1996
TL;DR: Cross-cultural research on facial expression and the developments of methods to measure facial expression are briefly summarized and what has been learned about emotion from this work on the face is elucidated.
Abstract: Cross-cultural research on facial expression and the developments of methods to measure facial expression are briefly summarized. What has been learned about emotion from this work on the face is then elucidated. Four questions about facial expression and emotion are discussed. What information does an expression typically convey? Can there be emotion without facial expression? Can there be a facial expression of emotion without emotion? How do individuals differ in their facial expressions of emotion?
2,463 citations
TL;DR: In this paper, cross-cultural research on facial expression and the developments of methods to measure facial expression are summarized and what has been learned about emotion from this work on the face is elucidated.
Abstract: Cross-cultural research on facial expression and the developments of methods to measure facial expression are briefly summarized. What has been learned about emotion from this work on the face is then elucidated. Four questions about facial expression and emotion are discussed: What information does an expression typically convey? Can there be emotion without facial expression? Can there be a facial expression of emotion without emotion? How do individuals differ in their facial expressions of emotion?
2,155 citations