Emotion detection using keywords spotting and semantic network IEEE ICOCI 2006

Ling, Huan; Bali, Raghav; Salam, Rosalina Abdul

doi:10.1109/icoci.2006.5276495

Cited by 9 publications

(11 citation statements)

References 3 publications

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“…Semantic networks are used to represent concepts, events, and relationships between them. The authors Ling et al (2006) concluded the paper by declaring better performance in detecting human emotions using semantic networks versus keyword spotting because the semantic networks do not depend on detecting emotions based on keywords. In semantic networks the emotions are detected based on the contextual information.…”

Section: Feature Extractionmentioning

confidence: 99%

“…A state-of-the-art comparison between keyword spotting and semantic analysis has been presented in Ling et al (2006). Ling analyzed the sentence syntactically and identified the basic emotions by analyzing words with respect to context and structure patterns (Ling et al, 2006). The keyword spotting technique is based on keywords specifically used for the description of certain emotions in the text (Wollmer et al, 2009).…”

Section: Feature Extractionmentioning

confidence: 99%

Section: Feature Extractionmentioning

confidence: 99%

“…Keyword spotting technique is used to determine the useful contents from the textual message (Ling et al, 2006). The keyword spotting technique is completely based on the WordNet-Affect, which is a semantic lexicon commonly used for the categorization of words that express similar emotions (Strapparava & Ozbal, 2010).…”

Section: Feature Extractionmentioning

confidence: 99%

“…The sentence is represented in a tree-like structure, termed as parse tree, that is mainly used for analysis of correct grammatical order of a sentence. A parse tree can be constructed by using a top-down or bottom-up approach (Ling et al, 2006).…”

Section: Feature Extractionmentioning

confidence: 99%

See 4 more Smart Citations

A survey on text mining in social networks

Irfan¹,

King²,

Grages³

et al. 2015

The Knowledge Engineering Review

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In this survey, we review different text mining techniques to discover various textual patterns from the social networking sites. Social network applications create opportunities to establish interaction among people leading to mutual learning and sharing of valuable knowledge, such as chat, comments, and discussion boards. Data in social networking websites is inherently unstructured and fuzzy in nature. In everyday life conversations, people do not care about the spellings and accurate grammatical construction of a sentence that may lead to different types of ambiguities, such as lexical, syntactic, and semantic. Therefore, analyzing and extracting information patterns from such data sets are more complex. Several surveys have been conducted to analyze different methods for the information extraction. Most of the surveys emphasized on the application of different text mining techniques for unstructured data sets reside in the form of text documents, but do not specifically target the data sets in social networking website. This survey attempts to provide a thorough understanding of different text mining techniques as well as the application of these techniques in the social networking websites. This survey investigates the recent advancement in the field of text analysis and covers two basic approaches of text mining, such as classification and clustering that are widely used for the exploration of the unstructured text available on the Web.

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Section: Feature Extractionmentioning

confidence: 99%

Section: Feature Extractionmentioning

confidence: 99%

Section: Feature Extractionmentioning

confidence: 99%

Section: Feature Extractionmentioning

confidence: 99%

Section: Feature Extractionmentioning

confidence: 99%

See 3 more Smart Citations

A survey on text mining in social networks

Irfan¹,

King²,

Grages³

et al. 2015

The Knowledge Engineering Review

View full text Add to dashboard Cite

show abstract

A Comparative Study of Text Mining Algorithms for Anomaly Detection in Online Social Networks

Kokatnoor

Balachandran

2020

Data Science and Security

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Identifying and sensing emotional quotient weightage in the outcome using Speech Dialogue

Yadav

Mishra

2021

IOP Conf. Ser.: Mater. Sci. Eng.

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Emotion is a very important part of any interaction whether it is human-human or human- computer interaction as an emotion conveys the essence of the interaction. Decision making is also influenced by the emotional state of a person. If we can capture emotions from a human-computer interaction then it will be very beneficial for the computer to learn from that interaction. It can be efficiently used by an artificial intelligence system to understand and give response accordingly. The method proposed in this paper uses a machine learning based approach to detect emotion from the text obtained from the speech dialogue.

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Emotion detection using keywords spotting and semantic network IEEE ICOCI 2006

Cited by 9 publications

References 3 publications

A survey on text mining in social networks

A survey on text mining in social networks

A Comparative Study of Text Mining Algorithms for Anomaly Detection in Online Social Networks

Identifying and sensing emotional quotient weightage in the outcome using Speech Dialogue

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