The Experts below are selected from a list of 43806 Experts worldwide ranked by ideXlab platform
Flavius Frasincar - One of the best experts on this subject based on the ideXlab platform.
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survey on aspect level Sentiment Analysis
IEEE Transactions on Knowledge and Data Engineering, 2016Co-Authors: Kim Schouten, Flavius FrasincarAbstract:The field of Sentiment Analysis, in which Sentiment is gathered, analyzed, and aggregated from text, has seen a lot of attention in the last few years. The corresponding growth of the field has resulted in the emergence of various subareas, each addressing a different level of Analysis or research question. This survey focuses on aspect-level Sentiment Analysis, where the goal is to find and aggregate Sentiment on entities mentioned within documents or aspects of them. An in-depth overview of the current state-of-the-art is given, showing the tremendous progress that has already been made in finding both the target, which can be an entity as such, or some aspect of it, and the corresponding Sentiment. Aspect-level Sentiment Analysis yields very fine-grained Sentiment information which can be useful for applications in various domains. Current solutions are categorized based on whether they provide a method for aspect detection, Sentiment Analysis, or both. Furthermore, a breakdown based on the type of algorithm used is provided. For each discussed study, the reported performance is included. To facilitate the quantitative evaluation of the various proposed methods, a call is made for the standardization of the evaluation methodology that includes the use of shared data sets. Semantically-rich concept-centric aspect-level Sentiment Analysis is discussed and identified as one of the most promising future research direction.
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Accounting for negation in Sentiment Analysis
2011Co-Authors: Bmwt Heerschop, Flavius Frasincar, P Van Iterson, Alexander Hogenboom, Uzay KaymakAbstract:Automated ways of analyzing Sentiment in Web data are becoming more and more urgent as virtual utterances of opinions or Sentiment are becoming increasingly abundant on the Web. The role of negation in Sentiment Analysis has been explored only to a limited extent. In this paper, we investigate the impact of accounting for negation in Sentiment Analysis. To this end, we utilize a basic Sentiment Analysis framework ‐ consisting of a wordbank creation part and a document scoring part ‐ taking into account negation. Our experimental results show that by accounting for negation, precision relative to human ratings increases with 1.17%. On a subset of selected documents containing negated words, precision increases with 2.23%.
Erik Cambria - One of the best experts on this subject based on the ideXlab platform.
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Benchmarking Multimodal Sentiment Analysis
arXiv: Multimedia, 2017Co-Authors: Erik Cambria, Soujanya Poria, Devamanyu Hazarika, Amir Hussain, R. B. V. SubramaanyamAbstract:We propose a framework for multimodal Sentiment Analysis and emotion recognition using convolutional neural network-based feature extraction from text and visual modalities. We obtain a performance improvement of 10% over the state of the art by combining visual, text and audio features. We also discuss some major issues frequently ignored in multimodal Sentiment Analysis research: the role of speaker-independent models, importance of the modalities and generalizability. The paper thus serve as a new benchmark for further research in multimodal Sentiment Analysis and also demonstrates the different facets of Analysis to be considered while performing such tasks.
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Tensor Fusion Network for Multimodal Sentiment Analysis
arXiv: Computation and Language, 2017Co-Authors: Amir Zadeh, Soujanya Poria, Erik Cambria, Minghai Chen, Louis-philippe MorencyAbstract:Multimodal Sentiment Analysis is an increasingly popular research area, which extends the conventional language-based definition of Sentiment Analysis to a multimodal setup where other relevant modalities accompany language. In this paper, we pose the problem of multimodal Sentiment Analysis as modeling intra-modality and inter-modality dynamics. We introduce a novel model, termed Tensor Fusion Network, which learns both such dynamics end-to-end. The proposed approach is tailored for the volatile nature of spoken language in online videos as well as accompanying gestures and voice. In the experiments, our model outperforms state-of-the-art approaches for both multimodal and unimodal Sentiment Analysis.
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Sentiment Analysis Is a Big Suitcase
IEEE Intelligent Systems, 2017Co-Authors: Erik Cambria, Soujanya Poria, Alexander Gelbukh, Mike ThelwallAbstract:Although most works approach it as a simple categorization problem, Sentiment Analysis is actually a suitcase research problem that requires tackling many natural language processing (NLP) tasks. The expression “Sentiment Analysis” itself is a big suitcase (like many others related to affective computing, such as emotion recognition or opinion mining) that all of us use to encapsulate our jumbled idea about how our minds convey emotions and opinions through natural language. The authors address the composite nature of the problem via a three-layer structure inspired by the “jumping NLP curves” paradigm. In particular, they argue that there are (at least) 15 NLP problems that need to be solved to achieve human-like performance in Sentiment Analysis.
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Concept-Level Sentiment Analysis
2014Co-Authors: Erik CambriaAbstract:The WWW’14 tutorial on Concept-Level Sentiment Analysis aims to provide its participants means to efficiently design models, techniques, tools, and services for concept-level Sentiment Analysis and their commercial realizations. The tutorial draws on insights resulting from the recent IEEE Intelligent Systems special issues on Concept-Level Opinion and Sentiment Analysis and the IEEE CIM special issue on Computational Intelligence for Natural Language Processing. The tutorial includes a hands-on session to illustrate how to build a concept-level opinion-mining engine step-bystep, from semantic parsing to concept-level reasoning.
Namita Mittal - One of the best experts on this subject based on the ideXlab platform.
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Machine Learning Approach for Sentiment Analysis
Socio-Affective Computing, 2015Co-Authors: Basant Agarwal, Namita MittalAbstract:Machine learning algorithms have been widely used for Sentiment Analysis [66]. The bag-of-words (BoW) representation is commonly used for Sentiment Analysis [63, 93]. BoW method assumes the independence of words and ignores the importance of semantic and subjective information in the text. All the words in the text are considered equally important. The BoW representation is commonly used for Sentiment Analysis, resulting into high dimensionality of the feature space. Machine learning algorithms reduce this high-dimensional feature space with the help of feature selection techniques which selects only important features by eliminating the noisy and irrelevant features. Recently, machine learning-based Sentiment Analysis models are gaining prominence in the field [66].
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Prominent Feature Extraction for Sentiment Analysis
2015Co-Authors: Basant Agarwal, Namita MittalAbstract:The objective of this monograph is to improve the performance of the Sentiment Analysis model by incorporating the semantic, syntactic and common-sense knowledge. This book proposes a novel semantic concept extraction approach that uses dependency relations between words to extract the features from the text. Proposed approach combines the semantic and common-sense knowledge for the better understanding of the text. In addition, the book aims to extract prominent features from the unstructured text by eliminating the noisy, irrelevant and redundant features. Readers will also discover a proposed method for efficient dimensionality reduction to alleviate the data sparseness problem being faced by machine learning model. Authors pay attention to the four main findings of the book : -Performance of the Sentiment Analysis can be improved by reducing the redundancy among the features. Experimental results show that minimum Redundancy Maximum Relevance (mRMR) feature selection technique improves the performance of the Sentiment Analysis by eliminating the redundant features. - Boolean Multinomial Naive Bayes (BMNB) machine learning algorithm with mRMR feature selection technique performs better than Support Vector Machine (SVM) classifier for Sentiment Analysis. - The problem of data sparseness is alleviated by semantic clustering of features, which in turn improves the performance of the Sentiment Analysis.- Semantic relations among the words in the text have useful cues for Sentiment Analysis. Common-sense knowledge in form of ConceptNet ontology acquires knowledge, which provides a better understanding of the text that improves the performance of the Sentiment Analysis.
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Machine Learning Approaches for Sentiment Analysis
Data Mining and Analysis in the Engineering Field, 2014Co-Authors: Basant Agarwal, Namita MittalAbstract:Opinion Mining or Sentiment Analysis is the study that analyzes people's opinions or Sentiments from the text towards entities such as products and services. It has always been important to know what other people think. With the rapid growth of availability and popularity of online review sites, blogs', forums', and social networking sites' necessity of analysing and understanding these reviews has arisen. The main approaches for Sentiment Analysis can be categorized into semantic orientation-based approaches, knowledge-based, and machine-learning algorithms. This chapter surveys the machine learning approaches applied to Sentiment Analysis-based applications. The main emphasis of this chapter is to discuss the research involved in applying machine learning methods mostly for Sentiment classification at document level. Machine learning-based approaches work in the following phases, which are discussed in detail in this chapter for Sentiment classification: (1) feature extraction, (2) feature weighting schemes, (3) feature selection, and (4) machine-learning methods. This chapter also discusses the standard free benchmark datasets and evaluation methods for Sentiment Analysis. The authors conclude the chapter with a comparative study of some state-of-the-art methods for Sentiment Analysis and some possible future research directions in opinion mining and Sentiment Analysis.
Hassan Saif - One of the best experts on this subject based on the ideXlab platform.
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Semantic Sentiment Analysis in Social Streams
2017Co-Authors: Hassan SaifAbstract:Microblogs and social media platforms are now considered among the most popular forms of online communication. Through a platform like Twitter, much information reflecting people’s opinions and attitudes is published and shared among users on a daily basis. This has recently brought great opportunities to companies interested in tracking and monitoring the reputation of their brands and businesses, and to policy makers and politicians to support their assessment of public opinions about their policies or political issues. A wide range of approaches to Sentiment Analysis on social media, have been recently built. Most of these approaches rely mainly on the presence of affect words or syntactic structures that explicitly and unambiguously reflect Sentiment. However, these approaches are semantically weak, that is, they do not account for the semantics of words when detecting their Sentiment in text. In order to address this problem, the author investigates the role of word semantics in Sentiment Analysis of microblogs. Specifically, Twitter is used as a case study of microblogging platforms to investigate whether capturing the Sentiment of words with respect to their semantics leads to more accurate Sentiment Analysis models on Twitter. To this end, the author proposes several approaches in this book for extracting and incorporating two types of word semantics for Sentiment Analysis: contextual semantics (i.e., semantics captured from words’ co-occurrences) and conceptual semantics (i.e., semantics extracted from external knowledge sources). Experiments are conducted with both types of semantics by assessing their impact in three popular Sentiment Analysis tasks on Twitter; entity-level Sentiment Analysis, tweet-level Sentiment Analysis and context-sensitive Sentiment lexicon adaptation. The findings from this body of work demonstrate the value of using semantics in Sentiment Analysis on Twitter. The proposed approaches, which consider word semantics for Sentiment Analysis at both entity and tweet levels, surpass non-semantic approaches in most evaluation scenarios. This book will be of interest to students, researchers and practitioners in the semantic Sentiment Analysis field.
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Semantic Sentiment Analysis of microblogs
2015Co-Authors: Hassan SaifAbstract:Microblogs and social media platforms are now considered among the most popular forms of online communication. Through a platform like Twitter, much information reflecting people's opinions and attitudes is published and shared among users on a daily basis. This has recently brought great opportunities to companies interested in tracking and monitoring the reputation of their brands and businesses, and to policy makers and politicians to support their assessment of public opinions about their policies or political issues. A wide range of approaches to Sentiment Analysis on Twitter, and other similar microblogging platforms, have been recently built. Most of these approaches rely mainly on the presence of affect words or syntactic structures that explicitly and unambiguously reflect Sentiment (e.g., "great'', "terrible''). However, these approaches are semantically weak, that is, they do not account for the semantics of words when detecting their Sentiment in text. This is problematic since the Sentiment of words, in many cases, is associated with their semantics, either along the context they occur within (e.g., "great'' is negative in the context "pain'') or the conceptual meaning associated with the words (e.g., "Ebola" is negative when its associated semantic concept is "Virus"). This thesis investigates the role of words' semantics in Sentiment Analysis of microblogs, aiming mainly at addressing the above problem. In particular, Twitter is used as a case study of microblogging platforms to investigate whether capturing the Sentiment of words with respect to their semantics leads to more accurate Sentiment Analysis models on Twitter. To this end, several approaches are proposed in this thesis for extracting and incorporating two types of word semantics for Sentiment Analysis: contextual semantics (i.e., semantics captured from words' co-occurrences) and conceptual semantics (i.e., semantics extracted from external knowledge sources). Experiments are conducted with both types of semantics by assessing their impact in three popular Sentiment Analysis tasks on Twitter; entity-level Sentiment Analysis, tweet-level Sentiment Analysis and context-sensitive Sentiment lexicon adaptation. Evaluation under each Sentiment Analysis task includes several Sentiment lexicons, and up to 9 Twitter datasets of different characteristics, as well as comparing against several state-of-the-art Sentiment Analysis approaches widely used in the literature. The findings from this body of work demonstrate the value of using semantics in Sentiment Analysis on Twitter. The proposed approaches, which consider words' semantics for Sentiment Analysis at both, entity and tweet levels, surpass non-semantic approaches in most datasets.
Meena Rambocas - One of the best experts on this subject based on the ideXlab platform.
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Online Sentiment Analysis in marketing research: a review
Journal of Research in Interactive Marketing, 2018Co-Authors: Meena Rambocas, Barney G. PachecoAbstract:The explosion of internet-generated content, coupled with methodologies such as Sentiment Analysis, present exciting opportunities for marketers to generate market intelligence on consumer attitudes and brand opinions. The purpose of this paper is to review the marketing literature on online Sentiment Analysis and examines the application of Sentiment Analysis from three main perspectives: the unit of Analysis, sampling design and methods used in Sentiment detection and statistical Analysis.,The paper reviews the prior literature on the application of online Sentiment Analysis published in marketing journals over the period 2008-2016.,The findings highlight the uniqueness of online Sentiment Analysis in action-oriented marketing research and examine the technical, practical and ethical challenges faced by researchers.,The paper discusses the application of Sentiment Analysis in marketing research and offers recommendations to address the challenges researchers confront in using this technique.,This study provides academics and practitioners with a comprehensive review of the application of online Sentiment Analysis within the marketing discipline. The paper focuses attention on the limitations surrounding the utilization of this technique and provides suggestions for mitigating these challenges.
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Marketing Research : The Role of Sentiment Analysis
Working Papers (FEP) - Universidade Do Porto, 2013Co-Authors: Meena Rambocas, Jo??o GamaAbstract:This article promotes Sentiment Analysis as an alternative research technique for collecting and analyzing textual data on the internet. Sentiment Analysis is a data mining technique that systematically evaluates textual content using machine learning techniques. As a research method in marketing, Sentiment Analysis presents an efficient and effective evaluation of consumer opinions in real time. It allows data collection and Analysis from a very large sample without hindrances, obstructions and time delays. Through Sentiment Analysis, marketers collect rich data on attitudes and opinion in real time, without compromising reliability, validity and generalizability. Marketers also gather feedback on attitudes and opinions as they occur without having to invest in lengthy and costly market research activities. The paper proposes Sentiment Analysis as an alternative technique capable of triangulating qualitative and quantitative methods through innovative real time data collection and Analysis. The paper concludes with the challenges marketers can face when using this technique in their research work.
