Sentiment Extraction from Naturalistic Video

Radhakrishnan, Vignesh; Joseph, Christina Terese; Chandrasekaran, K.

doi:10.1016/j.procs.2018.10.454

Cited by 7 publications

(3 citation statements)

References 9 publications

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“…In this calculation, we use the base 2 logarithmic formula, so that the statistical result is always between 0 and 1, so the similarity of words can be easily expressed. If the PMI value is greater than 0, it can indicate that the words have a correlation; if the PMI value is equal to 0, the words are independent of each other; if the PMI value is less than 0, the words have no correlation [12].…”

Section: Actual So-pmi Algorithm Operationmentioning

confidence: 99%

Deep Learning for Chinese Language Sentiment Extraction and Analysis

Zhu

2022

Mathematical Problems in Engineering

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In recent years, vocabulary emotion processing has become immensely popular and the requirements for language emotion analysis mining and processing have become significantly abundant. The sentiment extraction and analysis work has always been very challenging; especially, the Chinese word segmentation operation is difficult to deal with effectively, the multiple combinations of implicit and explicit words make the task of sentiment analysis mining more difficult, and, in particular, the efficiency of machine analysis of language sentiment is feeble. We use some expressions and sentiment vocabulary dictionaries combined with hybrid structures and use information synergy methods to get in touch with sentiment analysis methods. We use the relevant sentiment to evaluate the explicit or implicit emotional association of the emotional connection of the vocabulary and add the unique emotional word matrix to analyze the related clustering results of the emotional words to continuously optimize and upgrade the performance, so that our sentiment analysis results are systematic in terms of efficiency and significantly improved.

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Section: Actual So-pmi Algorithm Operationmentioning

confidence: 99%

Deep Learning for Chinese Language Sentiment Extraction and Analysis

Zhu

2022

Mathematical Problems in Engineering

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“…If the same feature points can be detected in an image at different scales, then the feature points have certain scale invariance. In order to find the points with scale invariance, it is the key to construct the scale space first [7,8], considering the construction of a good kernel function in the scale space, namely Gaussian kernel function. So we can use the two-dimensional Gaussian kernel function to convolute the original image to construct the scale space…”

Section: Scale Invariant Feature Points In Scale Spacementioning

confidence: 99%

Optimal Extraction Method of Feature Points in Key Frame Image of Mobile Network Animation

Yin

2022

Mobile Netw Appl

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In order to effectively extract the feature points of mobile network animation images and accurately reflect the main content of the video, an optimization method to extract the feature points of key frame images of mobile network animation is proposed. Firstly, the key frames are selected according to the content change degree of the animation video. The scale invariant feature transformation algorithm is used to describe the feature points of the key frame image of the animation video. The local feature points of the image are estimated by the constraint optimization method to realize the optimization extraction of the feature points of the key frame image of the mobile network animation. The efficiency of feature points extraction is analyzed from the number and effectiveness of feature points extraction, time-consuming and similarity invariance of feature points. The experimental results show that the proposed method has excellent adaptability, and can effectively extract feature points of mobile network animation image.

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“…'s multimodal emotion recognition model extracts features from text and videos using a convolutional neural network architecture, incorporating all three modalities-visual, audio, and text. Radhakrishnan et al (2018) proposed a new approach for sentiment analysis from audio clips, which uses a hybrid of the Keyword Spotting System. The Maximum Entropy classifier was designed to integrate audio and text processing into a single system, and this model outperformed other conventional classifiers.…”

Section: Related Workmentioning

confidence: 99%

Second Grand-Challenge and Workshop on Multimodal Language (Challenge-HML)

Zadeh¹,

Morency²,

Liang³

et al. 2020

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Understanding expressed sentiment and emotions are two crucial factors in human multimodal language. This paper describes a Transformer-based joint-encoding (TBJE) for the task of Emotion Recognition and Sentiment Analysis. In addition to use the Transformer architecture, our approach relies on a modular co-attention and a glimpse layer to jointly encode one or more modalities. The proposed solution has also been submitted to the ACL20: Second Grand-Challenge on Multimodal Language to be evaluated on the CMU-MOSEI dataset. The code to replicate the presented experiments is open-source 1 .

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Sentiment Extraction from Naturalistic Video

Cited by 7 publications

References 9 publications

Deep Learning for Chinese Language Sentiment Extraction and Analysis

Deep Learning for Chinese Language Sentiment Extraction and Analysis

Optimal Extraction Method of Feature Points in Key Frame Image of Mobile Network Animation

Second Grand-Challenge and Workshop on Multimodal Language (Challenge-HML)

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