Ontology-based sentiment analysis of twitter posts

Kontopoulos, Efstratios; Berberidis, Christos; Dergiades, Theologos; Bassiliades, Nick

doi:10.1016/j.eswa.2013.01.001

Cited by 321 publications

(185 citation statements)

References 24 publications

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“…Formal Concept Analysis and Fuzzy Formal Concept Analysis (FCA/FFCA) specifically were employed in works by Li and Tsai [23] showing an abstract conceptual classification system of documents and use of training (FFCA-based conceptual classifier training as opposed to document-based training) examples to boost accuracy. Kontopoulos et al [24] have used FCA also to build an ontology domain model. In their work, they proposed the use of ontology-based techniques toward a more efficient sentiment analysis of twitter posts by breaking down each tweet into a set of aspects relevant to the subject.…”

Section: A Sentiment Analysis In Genaralmentioning

confidence: 99%

Arabic Sentiment Analysis: A Survey

Assiri¹,

Emam²,

Al-Dossari³

2015

ijacsa

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Abstract-Most social media commentary in the Arabic language space is made using unstructured non-grammatical slang Arabic language, presenting complex challenges for sentiment analysis and opinion extraction of online commentary and micro blogging data in this important domain. This paper provides a comprehensive analysis of the important research works in the field of Arabic sentiment analysis. An in-depth qualitative analysis of the various features of the research works is carried out and a summary of objective findings is presented. We used smoothness analysis to evaluate the percentage error in the performance scores reported in the studies from their linearly-projected values (smoothness) which is an estimate of the influence of the different approaches used by the authors on the performance scores obtained. To solve a bounding issue with the data as it was reported, we modified existing logarithmic smoothing technique and applied it to pre-process the performance scores before the analysis. Our results from the analysis have been reported and interpreted for the various performance parameters: accuracy, precision, recall and F-score.

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Section: A Sentiment Analysis In Genaralmentioning

confidence: 99%

Arabic Sentiment Analysis: A Survey

Assiri¹,

Emam²,

Al-Dossari³

2015

ijacsa

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“…The proposed method not only characterized by the sentiment score but it also provides sentiment grade for each post. This method provides a detailed analysis of opinion on the specific topic [6].…”

Section: Related Workmentioning

confidence: 99%

Twitter Sentiment Analysis using Machine Learning and Optimization Techniques

Bansal¹,

Kaur²

2018

IJCA

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Sentiment Analysis means determining the views of the user from the text regarding that topic i.e. how one feels about it. It can be used to classify the text content into positive or negative. Various researchers have used a wide range of methods to train the classifiers for the Twitter dataset with varying results. This paper introduces a hybrid approach of using Swarm Intelligence optimization algorithms with classifiers. For each tweet, pre-processing will be done by performing various processes i.e. tokenization; removal of stop-words and emoticons; stemming. Then their feature vectors are being made by the calculation of TF-IDF and optimized with Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) before performing the binary text categorization. Naïve Bayes and Support Vector Machine (SVM) is the machine learning techniques used for the binary classification of tweets. The results drawn using optimization with classifiers is much efficient than using classifier alone.

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“…Individual twitter posts can be represented in the microblogging domain using the following ontologies from the LOD cloud: FOAF [10] to model user profiles, SIOC [9] to describe microblog entries, and OPO [41] for modeling the semantics of a user's appearance in the online world. On the other hand, the application of sentiment analysis on twitter posts can help users to know if the polarity of posts tends towards positive, negative or neutral [29,38].…”

Section: Feedback In Parliamentary Institutionsmentioning

confidence: 99%

A multimedia system to produce and deliver video fragments on demand on parliamentary websites

Sánchez-Nielsen

Chávez-Gutiérrez

Lorenzo-Navarro

et al. 2016

Multimed Tools Appl

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Parliamentary websites have become one of the most important windows for citizens and media to follow the activities of their legislatures and to hold parliaments to account. Therefore, most parliamentary institutions aim to provide new multimedia solutions capable of displaying video fragments on demand on plenary activities. This paper presents a multimedia system for parliamentary institutions to produce video fragments on demand through a website with linked information and public feedback that helps to explain the content shown in these fragments. A prototype implementation has been developed for the Canary Islands Parliament (Spain) and shows how traditional parliamentary streaming systems can be enhanced by the use of semantics and computer vision for video analytics.The semantic web technologies used make search capabilities on parliamentary websites available to users to retrieve video fragments on demand with accurate and timely information. In addition, video analytic techniques enable the automation of identifying representative keyframes to be annotated by parliamentary experts. As a result, parliaments are able to enhance citizens' access to information and ensure that these institutions are more open and accountable on their websites and; at the same time, the labor-intensive tasks of parliamentary experts are considerably reduced.

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Ontology-based sentiment analysis of twitter posts

Cited by 321 publications

References 24 publications

Arabic Sentiment Analysis: A Survey

Arabic Sentiment Analysis: A Survey

Twitter Sentiment Analysis using Machine Learning and Optimization Techniques

A multimedia system to produce and deliver video fragments on demand on parliamentary websites

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