Impact analysis of adverbs for sentiment classification on Twitter product reviews

Haider, Sajjad; Afzal, Muhammad Tanvır; Asif, Muhammad; Maurer, Hermann; Ahmad, Awais; Abuarqoub, Abdelrahman

doi:10.1002/cpe.4956

Cited by 24 publications

(13 citation statements)

References 21 publications

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“…Here, our proposed model performs best for five out of 6 activities. Our proposed model outperforms method proposed in [52] by more than double in recognition accuracy. It also improves on a recent method proposed in [53] by more than 3 percent on average performance.…”

Section: ) Performance On Virat 10 Ground Datasetmentioning

confidence: 78%

Recognizing Human Activities From Video Using Weakly Supervised Contextual Features

et al. 2019

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Human activity recognition from the video is an important problem due to its potential applications in remote surveillance, content-based video retrieval, and in humanoid robots. Most of the visual activity recognition research in the last decade focus on recognizing basic human actions from wellconstrained laboratory videos and depend upon fully annotated video dataset for model training. Whereas activity recognition from unconstrained videos is still very challenging due to large variations in object appearance and pose, occlusion, and inter and intraclass variations. It is an extremely laborious task to prepare large scale realistic video activity dataset with detailed annotations of the human, object, and their mutual interactions in each frame. Although it is intuitive to model contextual relationships form fully annotated dataset, however, it is unknown as to how reliably multilevel contextual features can be extracted in the absence of annotated dataset. To mitigate these challenges, we propose a weakly supervised approach for complex human activity recognition from realistic videos. The proposed approach requires only activity labels for each video to train the model. A novel multilevel contextual features and context estimation procedure from the un-annotated dataset is also introduced. Restricted Boltzman machine is used to systematically integrate multilevel contextual features. We evaluate the proposed approach on benchmark realistic surveillance video datasets for human-human and human-object interaction activity recognition. The experimental results show improved accuracies on benchmark datasets without using fully annotated datasets.INDEX TERMS Activity recognition, multilevel context model, multilevel contextual features, weakly supervised.MUHAMMAD AJMAL is currently pursuing the Ph.D. degree in computer science under the Faculty Development Program. He is currently a Lecturer with the Department of Computer Science, COMSATS University Islamabad, Lahore Campus, Pakistan. He has published numerous articles in well reputed national and international journals and conferences in the past decade. His research interests include computer vision, machine learning, vision-based human activity recognition and summarization, theory and applications of formal methods, and the verification and simulation of systems.

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Section: ) Performance On Virat 10 Ground Datasetmentioning

confidence: 78%

Recognizing Human Activities From Video Using Weakly Supervised Contextual Features

et al. 2019

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“…In particular, the word "sentiment" is associated with a personal experience which leads to having a certain opinion on a specific topic. Haider et al (2021) affirm that feelings are expressed by opinions and emotions that usually are collectively defined as sentiments. A similar vision is shared also by Liu (2012) and by Saad and Saberi (2017), which considers the two terms as belonging essentially to the same concept.…”

Section: Sentiment Opinion Emotion and Affectmentioning

confidence: 99%

KnowMIS-ABSA: an overview and a reference model for applications of sentiment analysis and aspect-based sentiment analysis

D’Aniello

Rocca

2022

Artif Intell Rev

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The analysis of the opinions of customers and users has been always of great interest in supporting decision-making in many fields, especially in marketing. Sentiment analysis (SA) is the umbrella term for techniques and approaches that analyze user’s sentiments, emotions, opinions in text or other media. The need for a better understanding of these opinions paved the way to novel approaches that focus on the analysis of the sentiment related to specific features of a product, giving birth to the field of aspect-based sentiment analysis (ABSA). Although the increasing interest in this discipline, there is still confusion regarding the basic concepts of ABSA: terms like sentiment, affect, emotion, opinion, are used as synonyms while they represent different concepts. This often leads to an incorrect analysis of the users’ opinions.This work presents an overview of the state-of-the-art techniques and approaches for ABSA, highlighting the main critical issues related to current trends in this field. Following this analysis, a new reference model for SA and ABSA, namely the KnowMIS-ABSA model, is proposed. The model is grounded on the consideration that sentiment, affect, emotion and opinion are very different concepts and that it is profoundly wrong to use the same metric and the same technique to measure them. Accordingly, we argue that different tools and metrics should be adopted to measure each of the dimensions of an opinion. A qualitative case study, regarding product reviews, is proposed to motivate the advantages of the KnowMIS-ABSA model.

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“…The total number of iterations is 25 epochs. Experiments are implemented using Tensorflow, 4 and performed on NVIDIA GeForce GTX 1080 with 32 GB on-board memory.…”

Section: Experimental Settingsmentioning

confidence: 99%

“…Such a magnitude of opinion expression reveals public attitudes towards specific events. By analyzing sentimental attitudes from users, we can better boost relevant applications in real world, such as predicting stock market behavior, 1,2 predicting box office of movie, 3 product analysis for providing more satisfactory services for customers, 4 political elections, 5,6 or the measurement of public health concerns 7 . Therefore, how to effectively analyze massive contents on social network has attracted great attention in the field of artificial intelligence.…”

Section: Introductionmentioning

confidence: 99%

Various syncretic co‐attention network for multimodal sentiment analysis

Cao

Zhu

Gao

et al. 2020

Concurrency and Computation

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The multimedia contents shared on social network reveal public sentimental attitudes toward specific events. Therefore, it is necessary to conduct sentiment analysis automatically on abundant multimedia data posted by the public for real-world applications. However, approaches to single-modal sentiment analysis neglect the internal connections between textual and visual contents, and current multimodal methods fail to exploit the multilevel semantic relations of heterogeneous features. In this article, the various syncretic co-attention network is proposed to excavate the intricate multilevel corresponding relations between multimodal data, and combine the unique information of each modality for integrated complementary sentiment classification. Specifically, a multilevel co-attention module is constructed to explore localized correspondences between each image region and each text word, and holistic correspondences between global visual information and context-based textual semantics. Then, all the single-modal features can be fused from different levels, respectively. Except for fused multimodal features, our proposed VSCN also considers unique information of each modality simultaneously and integrates them into an end-to-end framework for sentiment analysis. The superior results of experiments on three constructed real-world datasets and a benchmark dataset of Visual Sentiment Ontology (VSO) prove the effectiveness of our proposed VSCN. Especially qualitative analyses are given for deep explaining of our method.

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Impact analysis of adverbs for sentiment classification on Twitter product reviews

Cited by 24 publications

References 21 publications

Recognizing Human Activities From Video Using Weakly Supervised Contextual Features

Recognizing Human Activities From Video Using Weakly Supervised Contextual Features

KnowMIS-ABSA: an overview and a reference model for applications of sentiment analysis and aspect-based sentiment analysis

Various syncretic co‐attention network for multimodal sentiment analysis

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