A survey on sentiment analysis and opinion mining for social multimedia

Li, Zuhe; Fan, Yangyu; Jiang, Bin; Lei, Tao; Liu, Weihua

doi:10.1007/s11042-018-6445-z

Cited by 111 publications

(59 citation statements)

References 81 publications

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“…However, their performance on short texts (like Facebook posts or tweets) is worse than the ones reported in Kim et al [37]. It has been observed that using pre-trained word embeddings for short texts yields better performance than using the same for long texts [24,25].…”

Section: Related Workmentioning

confidence: 86%

“…In the last paragraph, we finally take into account the most important works that focus on SA and ABSA in the Bangla language. More information can be found in wide survey papers such as [24,25].…”

Section: Related Workmentioning

confidence: 99%

“…On the mentioned datasets, many classification techniques were exploited in the context of ABSA. For instance, latent dirichlet allocation (LDA) [29] is one of the most used techniques in the classification of the aspects [24,25]. Many works use variations of LDA to discover latent topics in a collection of sentences or documents, with the hope that these topics will correspond to rateable aspects for the considered entity [30][31][32].…”

Section: Related Workmentioning

confidence: 99%

“…As the authors did in such articles, we had to preprocess the sentences to remove noise in the text (i.e., information not useful in performing the classification task), for instance, numbers, stop words and punctuation. We remark that this step was performed in the majority of the works we analyzed and it has been experimentally tested that it doesn't reduce classification performance: it has been experimentally tested that elements such as numbers, stop words and punctuation are not useful in the classification of the aspects, as intuitively one can imagine [24,25]. Further, the removal of such elements implies a reduction of the dimensionality of the feature space.…”

Section: Preprocessingmentioning

confidence: 99%

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Aspect Extraction from Bangla Reviews Through Stacked Auto-Encoders

Bodini

2019

Data

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Interactions between online users are growing more and more in recent years, due to the latest developments of the web. People share online comments, opinions, and reviews about many topics. Aspect extraction is the automatic process of understanding the topic (the aspect) of such comments, which has obtained huge interest from commercial and academic points of view. For instance, reviews available in webshops (like eBay, Amazon, Aliexpress, etc.) can help the customers in purchasing products and automatic analysis of reviews would be useful, as sometimes it is almost impossible to read all the available ones. In recent years, aspect extraction in the Bangla language has been regarded more and more as a task of growing importance. In the previous literature, a few methods have been introduced to classify Bangla texts according to the aspect they were focused on. This kind of research is limited mainly due to the lack of publicly available datasets for aspect extraction in the Bangla language. We take into account the only two publicly available datasets, recently published, collected for the task of aspect extraction in the Bangla language. Then, we introduce several classification methods based on stacked auto-encoders, as far as we know never exploited in the task of aspect extraction in Bangla, and we achieve better aspect classification performance with respect to the state-of-the-art: the experiments show an average improvement of 0.17 , 0.31 and 0.30 (across the two datasets), respectively in precision, recall and F1-score, reported in the state-of-the-art works that tackled the problem.

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Section: Related Workmentioning

confidence: 86%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Preprocessingmentioning

confidence: 99%

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Aspect Extraction from Bangla Reviews Through Stacked Auto-Encoders

Bodini

2019

Data

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“…Sentiment analysis technologies enable the automatic analysis of the information distributed through social media to identify the polarity of posted opinions [1]. These technologies have been extended in the last years to analyze other aspects, such as the stance of a user towards a topic [2] or the users' emotions [3], even combining text analytics with other inputs, including multimedia analysis [4] or social network analysis [5].…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis for Social Media

Iglesias¹,

Moreno

2019

Applied Sciences

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Sentiment analysis has become a key technology to gain insight from social networks. The field has reached a level of maturity that paves the way for its exploitation in many different fields such as marketing, health, banking or politics. The latest technological advancements, such as deep learning techniques, have solved some of the traditional challenges in the area caused by the scarcity of lexical resources. In this Special Issue, different approaches that advance this discipline are presented. The contributed articles belong to two broad groups: technological contributions and applications.

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Harnessing the Power of Data Science to Grasp Insights About Human Behaviour, Thinking, and Feeling from Social Media Images

Dudău

2021

Pathways Between Social Science and Computational Social Science

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A survey on sentiment analysis and opinion mining for social multimedia

Cited by 111 publications

References 81 publications

Aspect Extraction from Bangla Reviews Through Stacked Auto-Encoders

Aspect Extraction from Bangla Reviews Through Stacked Auto-Encoders

Sentiment Analysis for Social Media

Harnessing the Power of Data Science to Grasp Insights About Human Behaviour, Thinking, and Feeling from Social Media Images

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