Microblogging as a mechanism for human–robot interaction

Bell, David; Koulouri, Theodora; Lauria, Stanislao; Macredie, Robert D.; Sutton, James H.

doi:10.1016/j.knosys.2014.05.009

Cited by 27 publications

(10 citation statements)

References 16 publications

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“…The last subsection 3.6 presents various existing applications of SA, whereas possible future applications are discussed in the next section. [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], …”

Section: Review Methodologymentioning

confidence: 99%

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A survey on opinion mining and sentiment analysis: Tasks, approaches and applications

Ravi

2015

Knowledge-Based Systems

1,069

528

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Section: Review Methodologymentioning

confidence: 99%

“…Bell et al [203] exploited twitter as a platform to instruct an educational tool called Finch Robot to take a picture and reading temperature, where both instructions could be given in any order. Tweets were preprocessed and POS tagged using tweet tagger [204].…”

Section: Lexicon Based Approachesmentioning

confidence: 99%

A survey on opinion mining and sentiment analysis: Tasks, approaches and applications

Ravi

2015

Knowledge-Based Systems

1,069

528

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“…This is necessary to build a fusion knowledge based systems to provide question answering robot (dialogue) [34] with human users .…”

Section: Corners Stones To Achieve: Health_risks-forecast@people-mentioning

confidence: 99%

Big data based clouds healthcare web service forecasting: Research challenge

Fujita

2015

2015 IEEE 10th Jubilee International Symposium on Applied Computational Intelligence and Informatics

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Web service predictions are increasingly evolving in crucial manner in service providing in intelligent systems, in health care. This article highlights research challenges in health care forecasting based on cloud computing utilizing preferences project on users using subjective criteria (from user preferences) and objective criteria (statistical data in the clouds). The objective is to forecast user health situation for risk predictions.

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“…The dataset could be of more than 5000 tweets that vary in size according to the data needed. It should include all three types of data, where structured data are in an organized format in the repository [11], semi-structured data are formatted in the form of structured data, and unstructured data are not organized and do not contain any pre-defined models [12]. The types of data are depicted in Figure 3.…”

Section: Introductionmentioning

confidence: 99%

Emotion AI-Driven Sentiment Analysis: A Survey, Future Research Directions, and Open Issues

et al. 2019

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The essential use of natural language processing is to analyze the sentiment of the author via the context. This sentiment analysis (SA) is said to determine the exactness of the underlying emotion in the context. It has been used in several subject areas such as stock market prediction, social media data on product reviews, psychology, judiciary, forecasting, disease prediction, agriculture, etc. Many researchers have worked on these areas and have produced significant results. These outcomes are beneficial in their respective fields, as they help to understand the overall summary in a short time. Furthermore, SA helps in understanding actual feedback shared across different platforms such as Amazon, TripAdvisor, etc. The main objective of this thorough survey was to analyze some of the essential studies done so far and to provide an overview of SA models in the area of emotion AI-driven SA. In addition, this paper offers a review of ontology-based SA and lexicon-based SA along with machine learning models that are used to analyze the sentiment of the given context. Furthermore, this work also discusses different neural network-based approaches for analyzing sentiment. Finally, these different approaches were also analyzed with sample data collected from Twitter. Among the four approaches considered in each domain, the aspect-based ontology method produced 83% accuracy among the ontology-based SAs, the term frequency approach produced 85% accuracy in the lexicon-based analysis, and the support vector machine-based approach achieved 90% accuracy among the other machine learning-based approaches.

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Microblogging as a mechanism for human–robot interaction

Cited by 27 publications

References 16 publications

A survey on opinion mining and sentiment analysis: Tasks, approaches and applications

A survey on opinion mining and sentiment analysis: Tasks, approaches and applications

Big data based clouds healthcare web service forecasting: Research challenge

Emotion AI-Driven Sentiment Analysis: A Survey, Future Research Directions, and Open Issues

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