Sentiment Analysis Using Commonsense Knowledge
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Sentiment Analysis Using Commonsense Knowledge
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Author : Markus Benz
language : en
Publisher:
Release Date : 2009
Sentiment Analysis Using Commonsense Knowledge written by Markus Benz and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with categories.
Acquiring Broad Commonsense Knowledge For Sentiment Analysis Using Human Computation
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Author : Marina Boia
language : en
Publisher:
Release Date : 2016
Acquiring Broad Commonsense Knowledge For Sentiment Analysis Using Human Computation written by Marina Boia and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.
Mots-clés de l'autrice: commonsense knowledge acquisition ; human computation ; crowdsourcing ; gamification ; games with a purpose ; sentiment analysis ; sentiment classification ; fine-grained opinion extraction.
Sentic Computing
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Author : Erik Cambria
language : en
Publisher: Springer
Release Date : 2015-12-11
Sentic Computing written by Erik Cambria and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-12-11 with Medical categories.
This volume presents a knowledge-based approach to concept-level sentiment analysis at the crossroads between affective computing, information extraction, and common-sense computing, which exploits both computer and social sciences to better interpret and process information on the Web. Concept-level sentiment analysis goes beyond a mere word-level analysis of text in order to enable a more efficient passage from (unstructured) textual information to (structured) machine-processable data, in potentially any domain. Readers will discover the following key novelties, that make this approach so unique and avant-garde, being reviewed and discussed: • Sentic Computing's multi-disciplinary approach to sentiment analysis-evidenced by the concomitant use of AI, linguistics and psychology for knowledge representation and inference • Sentic Computing’s shift from syntax to semantics-enabled by the adoption of the bag-of-concepts model instead of simply counting word co-occurrence frequencies in text • Sentic Computing's shift from statistics to linguistics-implemented by allowing sentiments to flow from concept to concept based on the dependency relation between clauses This volume is the first in the Series Socio-Affective Computing edited by Dr Amir Hussain and Dr Erik Cambria and will be of interest to researchers in the fields of socially intelligent, affective and multimodal human-machine interaction and systems.
Commonsense Knowledge In Sentiment Analysis Of Ordinance Reactions For Smart Governance
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Author : Manish Puri
language : en
Publisher:
Release Date : 2019
Commonsense Knowledge In Sentiment Analysis Of Ordinance Reactions For Smart Governance written by Manish Puri and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Big data categories.
Smart Governance is an emerging research area which has attracted scientific as well as policy interests, and aims to improve collaboration between government and citizens, as well as other stakeholders. Our project aims to enable lawmakers to incorporate data driven decision making in enacting ordinances. Our first objective is to create a mechanism for mapping ordinances (local laws) and tweets to Smart City Characteristics (SCC). The use of SCC has allowed us to create a mapping between a huge number of ordinances and tweets, and the use of Commonsense Knowledge (CSK) has allowed us to utilize human judgment in mapping. We have then enhanced the mapping technique to link multiple tweets to SCC. In order to promote transparency in government through increased public participation, we have conducted sentiment analysis of tweets in order to evaluate the opinion of the public with respect to ordinances passed in a particular region. Our final objective is to develop a mapping algorithm in order to directly relate ordinances to tweets. In order to fulfill this objective, we have developed a mapping technique known as TOLCS (Tweets Ordinance Linkage by Commonsense and Semantics). This technique uses pragmatic aspects in Commonsense Knowledge as well as semantic aspects by domain knowledge. By reducing the sample space of big data to be processed, this method represents an efficient way to accomplish this task. The ultimate goal of the project is to see how closely a given region is heading towards the concept of Smart City.
Prominent Feature Extraction For Sentiment Analysis
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Author : Basant Agarwal
language : en
Publisher: Springer
Release Date : 2015-12-14
Prominent Feature Extraction For Sentiment Analysis written by Basant Agarwal and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-12-14 with Medical categories.
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.
Sentiment Analysis In The Bio Medical Domain
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Author : Ranjan Satapathy
language : en
Publisher: Springer
Release Date : 2018-01-23
Sentiment Analysis In The Bio Medical Domain written by Ranjan Satapathy and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-01-23 with Medical categories.
The abundance of text available in social media and health-related forums and blogs have recently attracted the interest of the public health community to use these sources for opinion mining. This book presents a lexicon-based approach to sentiment analysis in the bio-medical domain, i.e., WordNet for Medical Events (WME). This book gives an insight in handling unstructured textual data and converting it to structured machine-processable data in the bio-medical domain. The readers will discover the following key novelties: 1) development of a bio-medical lexicon: WME expansion and WME enrichment with additional features.; 2) ensemble of machine learning and computational creativity; 3) development of microtext analysis techniques to overcome the inconsistency in social communication. It will be of interest to researchers in the fields of socially-intelligent human-machine interaction and biomedical text mining
The Senticnet Sentiment Lexicon Exploring Semantic Richness In Multi Word Concepts
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Author : Raoul Biagioni
language : en
Publisher: Springer
Release Date : 2016-05-28
The Senticnet Sentiment Lexicon Exploring Semantic Richness In Multi Word Concepts written by Raoul Biagioni and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-05-28 with Medical categories.
The research and its outcomes presented in this book, is about lexicon-based sentiment analysis. It uses single-, and multi-word concepts from the SenticNet sentiment lexicon as the source of sentiment information for the purpose of sentiment classification. In 6 chapters the book sheds light on the comparison of sentiment classification accuracy between single-word and multi-word concepts, for which a bespoke sentiment analysis system developed by the author was used. This book will be of interest to students, educators and researchers in the field of Sentic Computing.
Building A Concept Level Sentiment Dictionary Based On Commonsense Knowledge
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Author : 蔡長蓉
language : en
Publisher:
Release Date : 2012
Building A Concept Level Sentiment Dictionary Based On Commonsense Knowledge written by 蔡長蓉 and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with categories.
Application Of Common Sense Computing For The Development Of A Novel Knowledge Based Opinion Mining Engine
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Author : Cambria Erik
language : en
Publisher:
Release Date : 2011
Application Of Common Sense Computing For The Development Of A Novel Knowledge Based Opinion Mining Engine written by Cambria Erik and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with categories.
The ways people express their opinions and sentiments have radically changed in the past few years thanks to the advent of social networks, web communities, blogs, wikis and other online collaborative media. The distillation of knowledge from this huge amount of unstructured information can be a key factor for marketers who want to create an image or identity in the minds of their customers for their product, brand, or organisation. These online social data, however, remain hardly accessible to computers, as they are specifically meant for human consumption. The automatic analysis of online opinions, in fact, involves a deep understanding of natural language text by machines, from which we are still very far. Hitherto, online information retrieval has been mainly based on algorithms relying on the textual representation of web-pages. Such algorithms are very good at retrieving texts, splitting them into parts, checking the spelling and counting their words. But when it comes to interpreting sentences and extracting meaningful information, their capabilities are known to be very limited. Existing approaches to opinion mining and sentiment analysis, in particular, can be grouped into three main categories: keyword spotting, in which text is classified into categories based on the presence of fairly unambiguous affect words; lexical affinity, which assigns arbitrary words a probabilistic affinity for a particular emotion; statistical methods, which calculate the valence of affective keywords and word co-occurrence frequencies on the base of a large training corpus. Early works aimed to classify entire documents as containing overall positive or negative polarity, or rating scores of reviews. Such systems were mainly based on supervised approaches relying on manually labelled samples, such as movie or product reviews where the opinionist's overall positive or negative attitude was explicitly indicated. However, opinions and sentiments do not occur only at document level, nor they are limited to a single valence or target. Contrary or complementary attitudes toward the same topic or multiple topics can be present across the span of a document. In more recent works, text analysis granularity has been taken down to segment and sentence level, e.g., by using presence of opinion-bearing lexical items (single words or n-grams) to detect subjective sentences, or by exploiting association rule mining for a feature-based analysis of product reviews. These approaches, however, are still far from being able to infer the cognitive and affective information associated with natural language as they mainly rely on knowledge bases that are still too limited to efficiently process text at sentence level. In this thesis, common sense computing techniques are further developed and applied to bridge the semantic gap between word-level natural language data and the concept-level opinions conveyed by these. In particular, the ensemble application of graph mining and multi-dimensionality reduction techniques on two common sense knowledge bases was exploited to develop a novel intelligent engine for open-domain opinion mining and sentiment analysis. The proposed approach, termed sentic computing, performs a clause-level semantic analysis of text, which allows the inference of both the conceptual and emotional information associated with natural language opinions and, hence, a more efficient passage from (unstructured) textual information to (structured) machine-processable data. The engine was tested on three different resources, namely a Twitter hashtag repository, a LiveJournal database and a PatientOpinion dataset, and its performance compared both with results obtained using standard sentiment analysis techniques and using different state-of-the-art knowledge bases such as Princeton's WordNet, MIT's ConceptNet and Microsoft's Probase. Differently from most currently available opinion mining services, the developed engine does not base its analysis on a limited set of affect words and their co-occurrence frequencies, but rather on common sense concepts and the cognitive and affective valence conveyed by these. This allows the engine to be domain-independent and, hence, to be embedded in any opinion mining system for the development of intelligent applications in multiple fields such as Social Web, HCI and e-health. Looking ahead, the combined novel use of different knowledge bases and of common sense reasoning techniques for opinion mining proposed in this work, will, eventually, pave the way for development of more bio-inspired approaches to the design of natural language processing systems capable of handling knowledge, retrieving it when necessary, making analogies and learning from experience.
Sentic Computing
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Author : Erik Cambria
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-07-28
Sentic Computing written by Erik Cambria and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-07-28 with Medical categories.
In this book common sense computing techniques are further developed and applied to bridge the semantic gap between word-level natural language data and the concept-level opinions conveyed by these. In particular, the ensemble application of graph mining and multi-dimensionality reduction techniques is exploited on two common sense knowledge bases to develop a novel intelligent engine for open-domain opinion mining and sentiment analysis. The proposed approach, termed sentic computing, performs a clause-level semantic analysis of text, which allows the inference of both the conceptual and emotional information associated with natural language opinions and, hence, a more efficient passage from (unstructured) textual information to (structured) machine-processable data.
