360-MAM-Affect: Sentiment Analysis with the Google Prediction API and EmoSenticNet

Mulholland, Eleanor; Kevitt, Paul Mc; Lunney, Tom; Farren, John; Wilson, Judy

doi:10.4108/icst.intetain.2015.259631

Cited by 7 publications

(9 citation statements)

References 20 publications

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“…They confirm that SA classification models can be built on cloud-based ML platforms notably Azure ML. This is in line with previous studies that implemented SA systems based on various cloud-based ML platforms (Mulholland et al, 2015;Roychowdhury, 2015;Bornstein et al, 2016).…”

Section: Discussionsupporting

confidence: 80%

Sentiment Analysis Algorithms through Azure Machine Learning: Analysis and Comparison

Harfoushi¹,

Hasan²,

Obiedat³

2018

MAS

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The Sentimental Analysis (SA) is a widely known and used technique in the natural language processing realm. It is often used in determining the sentiment of a text. It can be used to perform social media analytics. This study sought to compare two algorithms; Logistic Regression, and Support Vector Machine (SVM) using Microsoft Azure Machine Learning. This was demonstrated by performing a series of experiments on three Twitter datasets (TD). Accordingly, data was sourced from Twitter a microblogging platform. Data were obtained in the form of individuals' opinions, image, views, and twits from Twitter. Azure cloud-based sentiment analytics models were created based on the two algorithms. This work was extended with more in-depth analysis from another Master research conducted lately. Results confirmed that Microsoft Azure ML platform can be used to build effective SA models that can be used to perform data analytics.

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Section: Discussionsupporting

confidence: 80%

Sentiment Analysis Algorithms through Azure Machine Learning: Analysis and Comparison

Harfoushi¹,

Hasan²,

Obiedat³

2018

MAS

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“…SA models build of Azure platform had higher F-scores, accuracy, precision and recall scores than SA models build on Amazon Result of this study has not been replicated elsewhere as no study has been conducted to compare the accuracy and precision of these SA models build on these two machine learnings. However, it has been acknowledged in several studies that SA models can be implemented on Azure and Amazon ML platforms [16], [17], [18] and [19]. For example, [20] compared sentiment classification models build on Azure platform to analyse the accuracy of Logistic Regression and Neural Network Algorithms.…”

Section: Discussionmentioning

confidence: 99%

Amazon Machine Learning vs. Microsoft Azure Machine Learning as Platforms for Sentiment Analysis

Harfoushi¹,

Hasan²

2018

IJAST

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Recently, there has been an increasing attention towards the use machine learning platforms notably Amazon Machine learning and Microsoft Azure Machine learning (ML) to undertake sentiment analysis. The present experimental study compared Amazon ML against Microsoft Azure ML as platforms for performing sentiment analysis. The evaluation was done d using the evaluation metrics: accuracy, reliability, precision, F-score and Recall. Data was sourced from Twitter a microblogging platform. The sentiment analytics model was created based on Logistic regression analysis algorithms. Results confirmed that Microsoft Azure ML is more accurate and precise than Amazon ML as platforms for Sentiment Analysis and that Azure could be used reliably, accurately and securely to build Sentiment Analysis models for social media sites.

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“…The key component is the scene production module including modules for understanding, reasoning and multimodal visualisation situated on a server. The understanding module performs natural language processing, sentiment analysis and text layout analysis of input text utilising algorithms and software from CONFUCIUS [1], NewsViz [20] and 360-MAM-Affect [3]. The reasoning module interprets the context based on common, affective and cinematic knowledge bases, updates emotional states and creates plans for actions, their manners and representation of the set environment with algorithms and software from Control-Value Theory emotion models [2] and CONFUCIUS [1].…”

Section: Architecture Of Scenemakermentioning

confidence: 99%

“…The syntactic knowledge base parses input text and identifies parts of speech, e.g., noun, verb, adjective, with the Connexor Part-of-Speech Tagger [33] and determines the constituents in a sentence, e.g., subject, verb and object, with Functional Dependency Grammars [34]. The semantic knowledge base (WordNet [35] and LCS database [37]) and temporal language relations will be extended by an emotional knowledge base, e.g., WordNetAffect [36], emotion processing with 360-MAM-Affect [3], EmoSenticNet [38] and RapidMiner [39] and Control-Value Theory emotional models [2], and context reasoning with ConceptNet [11] to enable an understanding of the deeper meaning of the context and emotions.…”

Section: Implementation Of Scenemakermentioning

confidence: 99%

“…Existing software tools fulfilling sub-tasks will be modified, combined and extended for the implementation of SceneMaker. For the interpretation of input scripts, SceneMaker will build upon the NLP modules of CONFUCIUS [1] and 360-MAM-Affect [3] with GATE [32], but a pre-processing tool will first deconstruct the layout structure of the input screenplay/play script. The syntactic knowledge base parses input text and identifies parts of speech, e.g., noun, verb, adjective, with the Connexor Part-of-Speech Tagger [33] and determines the constituents in a sentence, e.g., subject, verb and object, with Functional Dependency Grammars [34].…”

Section: Implementation Of Scenemakermentioning

confidence: 99%

See 1 more Smart Citation

SceneMaker: Creative Technology for Digital StoryTelling

Akser

Bridges

Campo

et al. 2017

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Abstract. The School of Creative Arts & Technologies at Ulster University (Magee) has brought together the subject of computing with creative technologies, cinematic arts (film), drama, dance, music and design in terms of research and education. We propose here the development of a flagship computer software platform, SceneMaker, acting as a digital laboratory workbench for integrating and experimenting with the computer processing of new theories and methods in these multidisciplinary fields. We discuss the architecture of SceneMaker and relevant technologies for processing within its component modules. SceneMaker will enable the automated production of multimodal animated scenes from film and drama scripts or screenplays. SceneMaker will highlight affective or emotional content in digital storytelling with particular focus on character body posture, facial expressions, speech, non-speech audio, scene composition, timing, lighting, music and cinematography. Applications of SceneMaker include automated simulation of productions and education and training of actors, screenwriters and directors.

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360-MAM-Affect: Sentiment Analysis with the Google Prediction API and EmoSenticNet

Abstract: Abstract-Online

Cited by 7 publications

References 20 publications

Sentiment Analysis Algorithms through Azure Machine Learning: Analysis and Comparison

Sentiment Analysis Algorithms through Azure Machine Learning: Analysis and Comparison

Amazon Machine Learning vs. Microsoft Azure Machine Learning as Platforms for Sentiment Analysis

SceneMaker: Creative Technology for Digital StoryTelling

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