Deep Learning Algorithm for Picture Frame Detection on Social Media Videos

Abstract The widespread influence of social media impacts every aspect of life, including the healthcare sector. Although medics and health professionals are the final decision makers, the advice and recommendations obtained from fellow patients are significant. In this context, the present paper explores the topics of discussion posted by breast cancer patients and survivors on online forums. The study examines an online forum, Breastcancer.org, maps the discussion entries to several topics, and proposes a machine learning model based on a classification algorithm to characterize the topics. To explore the topics of breast cancer patients and survivors, approximately 1000 posts are selected and manually labeled with annotations. In contrast, millions of posts are available to build the labels. A semi-supervised learning technique is used to build the labels for the unlabeled data; hence, the large data are classified using a deep learning algorithm. The deep learning algorithm BiLSTM with BERT word embedding technique provided a better f1-score of 79.5%. This method is able to classify the following topics: medication reviews, clinician knowledge, various treatment options, seeking and providing support, diagnostic procedures, financial issues and implications for everyday life. What matters the most for the patients is coping with everyday living as well as seeking and providing emotional and informational support. The approach and findings show the potential of studying social media to provide insight into patients' experiences with cancer like critical health problems.

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Classification of User Comment Using Word2vec and Deep Learning

International Journal of Emerging Technology and Advanced Engineering ◽

10.46338/ijetae0521_01 ◽

2021 ◽

Vol 11 (5) ◽

pp. 1-8

Author(s):

Rafly Indra Kurnia ◽

◽

Abba Suganda Girsang

Keyword(s):

Social Media ◽

Deep Learning ◽

User Satisfaction ◽

Rating Scale ◽

Short Term Memory ◽

Learning Algorithm ◽

Training Data ◽

Deep Learning Algorithm ◽

User Comments

This study will classify the text based on the rating of the provider application on the Google Play Store. This research is classification of user comments using Word2vec and the deep learning algorithm in this case is Long Short Term Memory (LSTM) based on the rating given with a rating scale of 1-5 with a detailed rating 1 is the lowest and rating 5 is the highest data and a rating scale of 1-3 with a detailed rating, 1 as a negative is a combination of ratings 1 and 2, rating 2 as a neutral is rating 3, and rating 3 as a positive is a combination of ratings 4 and 5 to get sentiment from users using SMOTE oversampling to handle the imbalance data. The data used are 16369 data. The training data and the testing data will be taken from user comments MyTelkomsel’s application from the play.google.com site where each comment has a rating in Indonesian Language. This review data will be very useful for companies to make business decisions. This data can be obtained from social media, but social media does not provide a rating feature for every user comment. This research goal is that data from social media such as Twitter or Facebook can also quickly find out the total of the user satisfaction based from the rating from the comment given. The best f1 scores and precisions obtained using 5 classes with LSTM and SMOTE were 0.62 and 0.70 and the best f1 scores and precisions obtained using 3 classes with LSTM and SMOTE were 0.86 and 0.87

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Annotating and detecting topics in social media forum and modelling the annotation to derive directions-a case study

Journal Of Big Data ◽

10.1186/s40537-021-00429-7 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

B. Athira ◽

Josette Jones ◽

Sumam Mary Idicula ◽

Anand Kulanthaivel ◽

Enming Zhang

Keyword(s):

Breast Cancer ◽

Social Media ◽

Deep Learning ◽

Cancer Patients ◽

Learning Algorithm ◽

Treatment Options ◽

Healthcare Sector ◽

Final Decision ◽

Breast Cancer Patients ◽

Deep Learning Algorithm

AbstractThe widespread influence of social media impacts every aspect of life, including the healthcare sector. Although medics and health professionals are the final decision makers, the advice and recommendations obtained from fellow patients are significant. In this context, the present paper explores the topics of discussion posted by breast cancer patients and survivors on online forums. The study examines an online forum, Breastcancer.org, maps the discussion entries to several topics, and proposes a machine learning model based on a classification algorithm to characterize the topics. To explore the topics of breast cancer patients and survivors, approximately 1000 posts are selected and manually labeled with annotations. In contrast, millions of posts are available to build the labels. A semi-supervised learning technique is used to build the labels for the unlabeled data; hence, the large data are classified using a deep learning algorithm. The deep learning algorithm BiLSTM with BERT word embedding technique provided a better f1-score of 79.5%. This method is able to classify the following topics: medication reviews, clinician knowledge, various treatment options, seeking and providing support, diagnostic procedures, financial issues and implications for everyday life. What matters the most for the patients is coping with everyday living as well as seeking and providing emotional and informational support. The approach and findings show the potential of studying social media to provide insight into patients' experiences with cancer like critical health problems.

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Aspect Based Emotion Detection and Topic Modeling on Social Media Reviews

10.3233/apc210242 ◽

2021 ◽

Author(s):

Ganesh N. Jorvekar ◽

Mohit Gangwar

Keyword(s):

Neural Network ◽

Social Media ◽

Deep Learning ◽

Recurrent Neural Network ◽

Learning Algorithm ◽

Emotion Detection ◽

Natural Language Text ◽

Deep Learning Algorithm ◽

User Comments ◽

Deep Learning Model

In recent years, the number of user comments and text materials has increased dramatically. Analysis of the emotions has drawn interest from researchers. Earlier research in the field of artificial-intelligence concentrate on identification of emotion and exploring the explanation the emotions can’t recognized or misrecognized. The association between the emotions leads to the understanding of emotion loss. In this Work we are trying to fill the gap between emotional recognition and emotional co-relation mining through social media reviews of natural language text. The association between emotions, represented as the emotional uncertainty and evolution, is mainly triggered by cognitive bias in the human emotion. Numerous types of features and Recurrent neural-network (RNN) as deep learning model provided to mine the emotion co-relation from emotion detection using text. The rule on conflict of emotions is derived on a symmetric basis. TF-IDF, NLP Features and Co-relation features has used for feature extraction as well as section and Recurrent Neural Network (RNN) and Hybrid deep learning algorithm for classification has used to demonstrates the entire research experiments. Finally evaluate the system performance with various existing system and show the effectiveness of proposed system.

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Annotating and Detecting Topics in Social Media Forum and Modelling the Annotation to Derive Directions-A Case Study

10.21203/rs.3.rs-132773/v1 ◽

2020 ◽

Author(s):

Athira B ◽

Josette Jones ◽

Sumam Mary Idicula ◽

Anand Kulanthaivel ◽

Enming Zhang

Keyword(s):

Breast Cancer ◽

Social Media ◽

Deep Learning ◽

Cancer Patients ◽

Learning Algorithm ◽

Treatment Options ◽

Healthcare Sector ◽

Final Decision ◽

Breast Cancer Patients ◽

Deep Learning Algorithm

Abstract The widespread influence of social media impacts every aspect of life, including the healthcare sector. Although medics and health professionals are the final decision makers, the advice and recommendations obtained from fellow patients are significant. In this context, the present paper explores the topics of discussion posted by breast cancer patients and survivors on online forums. The study examines an online forum, Breastcancer.org, maps the discussion entries to several topics, and proposes a machine learning model based on a classification algorithm to characterize the topics. To explore the topics of breast cancer patients and survivors, approximately 1000 posts are selected and manually labeled with annotations. In contrast, millions of posts are available to build the labels. A semi-supervised learning technique is used to build the labels for the unlabeled data; hence, the large data are classified using a deep learning algorithm. The deep learning algorithm BiLSTM with BERT word embedding technique provided a better f1-score of 79.5%. This method is able to classify the following topics: medication reviews, clinician knowledge, various treatment options, seeking and providing support, diagnostic procedures, financial issues and implications for everyday life. What matters the most for the patients is coping with everyday living as well as seeking and providing emotional and informational support. The approach and findings show the potential of studying social media to provide insight into patients' experiences with cancer like critical health problems.

Download Full-text