Enhanced SenticNet with Affective Labels for Concept-Based Opinion Mining

Poria, Soujanya; Gelbukh, Alexander; Hussain, Amir; Howard, Newton; Das, Dipankar; Bandyopadhyay, Sivaji

doi:10.1109/mis.2013.4

Cited by 230 publications

(95 citation statements)

References 5 publications

(6 reference statements)

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“…The task of automatically identifying fine grained emotions, such as anger, joy, surprise, fear, disgust, and sadness, explicitly or implicitly expressed in a text, has been addressed by several researchers [44][45][46][47]. So far, approaches to text-based emotion and sentiment detection rely mainly on rule-based techniques, bag of words modeling using a large sentiment or emotion lexicon [48], or statistical approaches that assume the availability of a large dataset annotated with polarity or emotion labels [49].…”

Section: Text: Affect Recognition From Textual Datamentioning

confidence: 99%

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Towards an intelligent framework for multimodal affective data analysis

et al. 2015

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An increasingly large amount of multimodal content is posted on social media websites such as YouTube and Facebook everyday. In order to cope with the growth of such so much multimodal data, there is an urgent need to develop an intelligent multi-modal analysis framework that can effectively extract information from multiple modalities. In this paper, we propose a novel multimodal information extraction agent, which infers and aggregates the semantic and affective information associated with user-generated multimodal data in contexts such as e-learning, e-health, automatic video content tagging and human-computer interaction. In particular, the developed intelligent agent adopts an ensemble feature extraction approach by exploiting the joint use of tri-modal (text, audio and video) features to enhance the multimodal information extraction process. In preliminary experiments using the eNTERFACE dataset, our proposed multi-modal system is shown to achieve an accuracy of 87.95%, outperforming the best state-of-the-art system by more than 10%, or in relative terms, a 56% reduction in error rate

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Section: Text: Affect Recognition From Textual Datamentioning

confidence: 99%

“…EmoSenticNet: The EmoSenticNet dataset [48] contains about 5,700 common-sense knowledge concepts, including those concepts that exist in the WNA list, along with their affective labels in the set {anger, joy, disgust, sadness, surprise, fear}.…”

Section: Figure 1 a Sketch Of Conceptnet Graphmentioning

confidence: 99%

Towards an intelligent framework for multimodal affective data analysis

et al. 2015

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“…We followed a procedure suggested in [1] for a quite different task: effective classification of words [2]. The procedure consists in the following steps:…”

Section: Overview Of the Proceduresmentioning

confidence: 99%

“…2 As we have mentioned, we used the following three kinds of features: short-time features, long-time features, and beat features.…”

Section: Dataset and Featuresmentioning

confidence: 99%

Music Genre Classification: A Semi-supervised Approach

Poria

Gelbukh

Hussain

et al. 2013

Lecture Notes in Computer Science

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Abstract. Music genres can be seen as categorical descriptions used to classify music basing on various characteristics such as instrumentation, pitch, rhythmic structure, and harmonic contents. Automatic music genre classification is important for music retrieval in large music collections on the web. We build a classifier that learns from very few labeled examples plus a large quantity of unlabeled data, and show that our methodology outperforms existing supervised and unsupervised approaches. We also identify salient features useful for music genre classification. We achieve 97.1% accuracy of 10-way classification on real-world audio collections.

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“…We have built a large dictionary of emotions for English [100]. This dictionary is an extension of previously existing SenticNet dictionary [101], built in frame of the novel Sentic Computing paradigm [102].…”

Section: Sentiment Analysis and Opinion Miningmentioning

confidence: 99%

Natutal language processing: Perspective of CIC-IPN

Gelbukh

2013

2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI)

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Abstract-In this paper I outline part of the results of fifteen years of research of the Natural Language Processing (NLP) Laboratory of CIC-IPN. NLP is a set of technologies that in long run will allow for dialogue with robots in our everyday language or for overcoming the language barrier between people. Our group's work is concentrated on the internal tasks of this technology, such as resolving ambiguities and constructing dictionaries.

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Enhanced SenticNet with Affective Labels for Concept-Based Opinion Mining

Cited by 230 publications

References 5 publications

Towards an intelligent framework for multimodal affective data analysis

Towards an intelligent framework for multimodal affective data analysis

Music Genre Classification: A Semi-supervised Approach

Natutal language processing: Perspective of CIC-IPN

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