Machine Learning and Semantic Sentiment Analysis based Algorithms for Suicide Sentiment Prediction in Social Networks

Birjali, Marouane; Beni‐Hssane, Abderrahim; Erritali, Mohammed

doi:10.1016/j.procs.2017.08.290

Cited by 114 publications

(49 citation statements)

References 8 publications

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“…All these processing steps search for text images that contain Arabic text only. The results are illustrated below in figures (5), (6), (7), (8), (9) and (10).…”

Section: System Implementation and Resultsmentioning

confidence: 98%

“…More recently, Erik Boiy et al [10] conducted an ML experiment involving sentiment analysis based on data sourced from online blogs, reviews and forum texts that are written in French, Dutch, or English. In this work, training was performed on a set of example statements or sentences which the authors classified by consensus as negative, positive, or neutral in terms of specific consumption product evaluation.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Proposed Arabic Text and Text Image Classification Technique Using a URL Address

Hussein¹,

Kareem²

2019

Journal of Southwest Jiaotong University

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Sentiment analysis is examined within natural language processing. It is instrumental in finding the sentiment (feeling) or opinion (idea) hidden within a text. This research focuses on finding sentiments in a “text image” and then classifying them whether they are desirable or not. These phrases and words refer to the perspectives of people about anything they think about it, such as services, products, governments, and social media events. In this study, the optical character recognition (OCR) algorithm was used, which is considered as a classification procedure of visual patterns that appear in the form of a digital image. Moreover, the Naïve Bayes machine learning algorithm was employed to classify these texts. These two algorithms form a hybrid system that supports our needs, especially in this day of technological advances and frequent use of websites and sharing of text images through the internet. Finally, the new vision in this work involves dealing with Arabic language texts that are transformed into images, which are extracted from a URL address and then classified into desirable and undesirable content.

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“…All these processing steps search for text images that contain Arabic text only. The results are illustrated below in figures (5), (6), (7), (8), (9) and (10).…”

Section: System Implementation and Resultsmentioning

confidence: 98%

Section: Related Workmentioning

confidence: 99%

A Proposed Arabic Text and Text Image Classification Technique Using a URL Address

Hussein¹,

Kareem²

2019

Journal of Southwest Jiaotong University

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“…From the full-text review, 16 articles were then selected for inclusion [26,[42][43][44][45][46][47][48][49][50][51][52][53][54][55][56]. The flow diagram representing the search process is shown in Fig.…”

Section: Resultsmentioning

confidence: 99%

Suicide Risk Assessment Using Machine Learning and Social Networks: a Scoping Review

et al. 2020

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“…He has discussed how to improve the performance of deep learning techniques integrating them with traditional surface approaches based on manually extracted features. Bag [20] has given details of attribute level analysis for predicting the consumers purchase intention of durable goods. He has developed an attribute level decision support prediction model for providing the best e-commerce platform to the customers.…”

Section: Literature Overviewmentioning

confidence: 99%

Developing a Model for Sentiment Analysis Technique in the field of Tourism using Deep Learning

Arora,

Bansal

2020

IJRTE

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This paper provides a platform for analyzing and summarizing the sentiments expressed by users or customers in the field of online tourism. The objective of this research is to analyze online reviews of all the users to propose a new optimized business model to improve present services of business organization to enhance profit and customer satisfaction. The proposed system filters tourism online reviews and classifies them using sentimental technique with the help of deep learning technique. Deep learning technique will not only identify the polarity of online reviews but also recognizes relevant patterns deeply to find the hidden reviews details. After applying the deep learning technique, the results will be generated through which we can find the inferences. These inferences would provide a great help for improvisation of the subject. In this research a new optimized business model will be implemented using deep learning technique so that we would be able to compare new business model with the present system [1]. The relevance of this research lies in helping tourism industries to understand the social sentiment of their brand, product or service while monitoring online conversations. It helps in enhancing business profits by running online websites throughout by giving best services to the online users or customers.

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Machine Learning and Semantic Sentiment Analysis based Algorithms for Suicide Sentiment Prediction in Social Networks

Cited by 114 publications

References 8 publications

A Proposed Arabic Text and Text Image Classification Technique Using a URL Address

A Proposed Arabic Text and Text Image Classification Technique Using a URL Address

Suicide Risk Assessment Using Machine Learning and Social Networks: a Scoping Review

Developing a Model for Sentiment Analysis Technique in the field of Tourism using Deep Learning

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