Download Sentic Computing: A Common-Sense-Based Framework for by Erik Cambria, Amir Hussain PDF

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By Erik Cambria, Amir Hussain

This quantity provides a knowledge-based method of concept-level sentiment research on the crossroads among affective computing, details extraction, and commonsense computing, which exploits either machine and social sciences to raised interpret and method details on the internet.
Concept-level sentiment research is going past an insignificant word-level research of textual content which will allow a extra effective passage from (unstructured) textual details to (structured) machine-processable information, in most likely any domain.

Readers will become aware of the subsequent key novelties, that make this method so designated and avant-garde, being reviewed and discussed:
• Sentic Computing's multi-disciplinary method of sentiment analysis-evidenced through the concomitant use of AI, linguistics and psychology for wisdom illustration and inference
• Sentic Computing’s shift from syntax to semantics-enabled via the adoption of the bag-of-concepts version rather than easily counting be aware co-occurrence frequencies in text
• Sentic Computing's shift from records to linguistics-implemented through permitting sentiments to stream from notion to notion in line with the dependency relation among clauses

This quantity is the 1st within the sequence Socio-Affective Computing edited through Dr Amir Hussain and Dr Erik Cambria and may be of curiosity to researchers within the fields of socially clever, affective and multimodal human-machine interplay and systems.

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Extra resources for Sentic Computing: A Common-Sense-Based Framework for Concept-Level Sentiment Analysis

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Unlike many other sentiment-analysis resources, SenticNet is not built by manually labeling pieces of knowledge coming from general NLP resources such as WordNet or DBPedia. 1 Knowledge Acquisition 25 Fig. net/api) Fig. net/api) Fig. net/api) common-sense knowledge collected from three different sources (Sect. 1). This knowledge is represented redundantly at three levels: semantic network, matrix, and vector space (Sect. 2). Subsequently, semantics and sentics are calculated though the ensemble application of spreading activation, neural networks and an emotion categorization model (Sect.

Therefore, it is interesting to individuate a way to measure the affective meaning of a generic term. To this aim, the use of words in textual productions was studied, and in particular their co-occurrences with the words in which the affective meaning is explicit. , words that indicate possible emotional causes as monster or emotional responses as cry). The former are termed ‘direct affective words’ and the latter ‘indirect affective words’. Direct affective words were first integrated in WNA; then, a selection function (named Affective-Weight) based on a semantic similarity mechanism automatically acquired in an unsupervised way from a large corpus of texts (100 millions of words) was applied in order to individuate the indirect affective lexicon.

2 Towards Machines with Common Sense Communication is one of the most important aspects of human life. It is always associated with a cost in terms of energy and time, since information needs to be encoded, transmitted, and decoded, and, on occasions, such factors can even make the difference between life and death. This is why people, when communicating with each other, provide just the useful information and take the rest for granted. This ‘taken for granted’ information is what is termed ‘common-sense’ – obvious things people normally know and usually leave unstated.

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