Deep Learning-Based Sentimental Analysis for Large-Scale Imbalanced Twitter Data

Jamal, Nasir; Chen, Xianqiao; Aldabbas, Hamza

doi:10.3390/fi11090190

Cited by 14 publications

(4 citation statements)

References 31 publications

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“…In our future work, we plan to refine our results by expanding the data set. We also intend to further explore this topic, particularly in terms of detecting and characterizing spam content, methods, and campaigns (as opposed to spamming accounts) [38]. In addition, we will try to distinguish translated tweets created by an Arab writer [39].…”

Section: Discussionmentioning

confidence: 99%

Predicting Rogue Content and Arabic Spammers on Twitter

Alharbi

Aljaedi

2019

Future Internet

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Twitter is one of the most popular online social networks for spreading propaganda and words in the Arab region. Spammers are now creating rogue accounts to distribute adult content through Arabic tweets that Arabic norms and cultures prohibit. Arab governments are facing a huge challenge in the detection of these accounts. Researchers have extensively studied English spam on online social networks, while to date, social network spam in other languages has been completely ignored. In our previous study, we estimated that rogue and spam content accounted for approximately three quarters of all content with Arabic trending hashtags in Saudi Arabia. This alarming rate, supported by autonomous concurrent estimates, highlights the urgent need to develop adaptive spam detection methods. In this work, we collected a pure data set from spam accounts producing Arabic tweets. We applied lightweight feature engineering based on rogue content and user profiles. The 47 generated features were analyzed, and the best features were selected. Our performance results show that the random forest classification algorithm with 16 features performs best, with accuracy rates greater than 90%.

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Section: Discussionmentioning

confidence: 99%

Predicting Rogue Content and Arabic Spammers on Twitter

Alharbi

Aljaedi

2019

Future Internet

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“…The disadvantage was that, although the users were correctly classified, detecting them as stressed required an excessive amount of time. So, the challenge here is to reduce the depression detection time. More machine learning or deep learning systems 86 that are less likely to over fit the data should be used, as well as a more accurate way to measure the impact of features, should be found. As per the survey, it has been found that most of the developed models only recognize tweets in the English language 87 . So a more reliable system can be developed to consider tweets written in other languages.…”

Section: Challenges and Future Directionmentioning

confidence: 99%

“…3. More machine learning or deep learning systems 86 that are less likely to over fit the data should be used, as well as a more accurate way to measure the impact of features, should be found. As per the survey, it has been found that most of the developed models only recognize tweets in the English language.…”

Section: Depression Detection From Social Postmentioning

confidence: 99%

Review of automated depression detection: Social posts, audio and video, open challenges and future direction

Yadav

Sharma

Patil

2022

Concurrency and Computation

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Depression is the primary cause of illness and injury in the country, with over 280 million people suffering from it as per the 2021 survey. Depression is one of the most common psychiatric illnesses in the world, affecting millions of people. Early detection of major depression symptoms and treatment with timely intervention can help to prevent the emergence of major depression. This has generated the need for some novel techniques to be utilized for depression detection to help doctors in diagnosing and treating depression effectively. Depression can be investigated through online posts, audio files, facial expressions, as well as through video files. In this regard, the research presents a detailed survey of the existing machine learning methods in depression detection, along with the different datasets available. The research explores automated depression detection strategies and the various ways to detect depression from text, audio, and video, and even examines the various systems and procedures for detecting depression using various criteria. More than 140 related research articles were considered for this review, with 80 of them being processed for comparative analysis based on various key performance indicators. The classifications of various methods used to detect stress, anxiety, and depression are highlighted followed by open research challenges and issues while analyzing depression techniques.

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“…Human languages can be analyzed and understood by NLP algorithms. Sentiment analysis intends to parse sentiment from textual information and extract their polarity and viewpoint [26]. Singla et al [27] proposed a method to analyze the Amazon mobile phone reviews, which are categorized into negative and positive polarity.…”

Section: Text Analysismentioning

confidence: 99%

Progressive Teaching Improvement For Small Scale Learning: A Case Study in China

Jiang

Chen

et al. 2020

Future Internet

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Learning data feedback and analysis have been widely investigated in all aspects of education, especially for large scale remote learning scenario like Massive Open Online Courses (MOOCs) data analysis. On-site teaching and learning still remains the mainstream form for most teachers and students, and learning data analysis for such small scale scenario is rarely studied. In this work, we first develop a novel user interface to progressively collect students’ feedback after each class of a course with WeChat mini program inspired by the evaluation mechanism of most popular shopping website. Collected data are then visualized to teachers and pre-processed. We also propose a novel artificial neural network model to conduct a progressive study performance prediction. These prediction results are reported to teachers for next-class and further teaching improvement. Experimental results show that the proposed neural network model outperforms other state-of-the-art machine learning methods and reaches a precision value of 74.05% on a 3-class classifying task at the end of the term.

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Deep Learning-Based Sentimental Analysis for Large-Scale Imbalanced Twitter Data

Cited by 14 publications

References 31 publications

Predicting Rogue Content and Arabic Spammers on Twitter

Predicting Rogue Content and Arabic Spammers on Twitter

Review of automated depression detection: Social posts, audio and video, open challenges and future direction

Progressive Teaching Improvement For Small Scale Learning: A Case Study in China

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