Advertisement Prediction in Social Media Environment Using Big Data Framework

Mohbey, Krishna Kumar; Kumar, Sunil; Koolwal, Vartika

doi:10.1007/978-981-13-8759-3_12

Cited by 2 publications

(3 citation statements)

References 18 publications

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“…Various tests have been conducted to demonstrate the efficiency of the strategy from various viewpoints. SVM, Logistic Regression, Naive Bayes, Decision Tree, Random Forest, and LSTM approaches [46] were used to compare the efficacy of sentiment categorization [38].…”

Section: Methodsmentioning

confidence: 99%

“…To use this API, we must first develop an application with four secret keys [37]. Live tweets can be easily collected using Twitter API [38,39].…”

Section: Data Collectionmentioning

confidence: 99%

“…Additional white spaces, as well as special characters, must be addressed. Only different tweets will be considered, and all of the unique filtered tweets will be converted to lowercase for convenience of use in other processes [38].…”

Section: Data Filteringmentioning

confidence: 99%

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Categorizing Sentiment Polarities in Social Networks Data Using Convolutional Neural Network

2021

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Social media and networks are essential in today's world of communication technology. Because most people utilize social networking platforms to communicate their sentiments and emotions, each click generates vast data. As a result, we need particularly efficient ways to extract useful information from user data acquired from various social networking platforms such as Twitter. This research focuses on categorizing the different reviews offered by persons of various ethnic groups. The trials were conducted using real-time data obtained from a social media network. To classify opinions, sentiment polarities such as positive, negative, and neutral were used. Furthermore, the Convolutional Neural Network approach was utilized to conduct experiments, and the results were compared to other machine learning algorithms. The results reveal that the Convolutional Neural Network model can achieve accuracy levels of up to 94.47 %, 95.4 %, and 94 %, respectively, when evaluated using Phone, Laptop, and TV review datasets. The proposed framework can be utilized to develop a strong relationship between commercial businesses and their clients. In addition, this study discusses the different applications of sentiment analysis techniques, particularly in social networking and security.

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Section: Methodsmentioning

confidence: 99%

“…To use this API, we must first develop an application with four secret keys [37]. Live tweets can be easily collected using Twitter API [38,39].…”

Section: Data Collectionmentioning

confidence: 99%