Topic2Labels: A framework to annotate and classify the social media data through LDA topics and deep learning models for crisis response

Wahid, Junaid Abdul; Shi, Lei; Gao, Yufei; Yang, Beifang; Wei, Lin; Tao, Yongcai; Hussain, Shabir; Ayoub, Muhammad; Yagoub, Imam

doi:10.1016/j.eswa.2022.116562

Cited by 39 publications

(24 citation statements)

References 21 publications

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“…Examples of such models are Latent Dirichlet Allocation (Blei et al 2003), Latent Semantic Indexing (Deerwester et al 1990) and Probabilistic Latent Semantic Indexing (Hofmann, 1999). These models have been successfully implemented in previous studies (Wahid et al 2022;Ozyurt et al 2021;Lv et al 2021;). This will be discussed further in section 3.9…”

Section: Unsupervised Machine Learningmentioning

confidence: 99%

Statistical learning approaches to sentiment analysis in the Nigerian banking context

Ogunleye¹

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Banking is an essential component of our day-to-day activity, and the sector contributes to the development of every Nation's economy. To ensure bank stability, the banks have gone through several deregulation and reformation processes. Unfortunately, these processes and technological advancement have led to increased competitiveness, saturated market, and low profitability. Thus, the need for banks to build customer centric service to gain profitability and stability is vital. This opens the need to investigate customers attitude towards banking.With the growing usage of social network sites (SNS), the user generated content (UGC) has opened opportunities for banks to mine customers opinion . This can help the banks to generate insight into their product and services, create marketing strategies and manage their reputation. To the customers, this serves as source to information that can help in supporting decision making on product or service purchase. In this study, sentiment analysis (SA) techniques were employed to investigate customers attitude towards banking us ing Twitter data. However, the unstructured nature of the data and word ambiguity made sentiment analysis complicated. In the context of this study, SA is more difficult because it involves the natural language processing of Pidgin English and English words in the bank domain.Unfortunately, there are limited or no resources for this purpose.

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Section: Unsupervised Machine Learningmentioning

confidence: 99%

Statistical learning approaches to sentiment analysis in the Nigerian banking context

Ogunleye¹

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“…The three categories into which sentiment analysis can be divided are the machine learning technique, the lexicon-based approach, and the hybrid strategy that combines the previous two approaches [10]. Nowadays, computational technologies are being used in various domains of life, including healthcare [14], security [15] [21] [25] and also in safety purposes [16], disaster [17], and situational awareness [19] [26] [27] in the educational domain [18] as well. Sentiment analysis is a prominent research topic in demand under the category of NLP [20].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The recommended ConvNet-SVMBoVW performed better than the traditional models [11]. The LDA (latent Dirichlet allocation) topic modelling methodology has been employed in [12] to give an automated method of labelling the data using the Topic2labels (T2L) framework. Use Bert (the bidirectional encoder representation from the transformer) embeddings to create a feature vector for the classifier to classify the data contextually.…”

Section: Literature Reviewmentioning

confidence: 99%

Transfer Learning-Based Framework for Sentiment Classification of Cosmetics Products Reviews

Sahar

Ayoub

Hussain

et al. 2022

PakJET

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The exponential growth in online reviews and recommendations availability drives sentiment classification, an interesting topic in industrial research. There is a vital requirement for organizations to explore client behaviour to assess the competitive business environment. This study aspires to examine and predict customer reviews using Transfer learning (TL) approaches. Reviews can span so many domains that it is challenging to gather annotated training data for all of them. Hence, this paper proposed an annotation algorithm to label a large unlabeled dataset. These reviews must be pulled and examined to predict the sentiment polarity, whether the review is positive, neutral, or negative. We propose a deep learning-based approach that learns to extract a meaningful representation for each review in an unsupervised fashion. Sentiment classifiers trained with this high-level feature representation outperform state-of-the-art methods on a benchmark of reviews of cosmetics brands on Amazon or other platforms. Using the BERT for sentiment analysis, we achieved the highest accuracy of 93.21% compared to previous studies.

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“…The analyses become complex when collected data is unstructured and heterogeneous. Currently, every well-known organization uses data mining and machine learning applications frequently to explore the meaningful hidden patterns from raw collected data [20][21] [22]. Nowadays, machine learning is used in various aspects of life [23] [24] to perform complex analyses and explore hidden patterns from data points.…”

Section: Related Workmentioning

confidence: 99%

A Comparative Analysis of Social Communication Applications using Aspect Based Sentiment Analysis

Irfan

Hussain

Ayoub

et al. 2022

PakJET

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Google Play Store is a popular distribution channel with millions of applications. WhatsApp is the most downloaded communication application on Play Store. A few months ago, WhatsApp changed its privacy policy, triggering a wave of user reviews outrage. Privacy is essential in the application; users are worried about their data security and privacy. A computational system must be required to analyze the user’s reviews for WhatsApp authority to make better policies. This study aims to develop a deep learning-based model for automatically assessing reviews that can be adapted for future data analysis. We proposed a deep learning methodology by using Aspect-based sentiment analysis (ABSA) utilizing the communication app reviews scraped from the Google play store using the Google Play scrapper application. This study uses the text mining technique for ABSA on the user’s reviews. For Topic extraction, we have used Latent Dirichlet Allocation (LDA) and the deep learning method Long Short-Term Memory (LSTM) for topic classification. The results show that our proposed model gives us a promising outcome with 90% accuracy by using the LSTM model. WhatsApp authority can use the results to optimize communication applications by adding more efficient features and updating them.

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Topic2Labels: A framework to annotate and classify the social media data through LDA topics and deep learning models for crisis response

Cited by 39 publications

References 21 publications

Statistical learning approaches to sentiment analysis in the Nigerian banking context

Statistical learning approaches to sentiment analysis in the Nigerian banking context

Transfer Learning-Based Framework for Sentiment Classification of Cosmetics Products Reviews

A Comparative Analysis of Social Communication Applications using Aspect Based Sentiment Analysis

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