Sentic API for mental health detection

Sentiment text analysis, which is a pivotal aspect of Natural Language Processing (NLP), involves reading different texts and identifying their labels (positive, negative, neutral). This report will dive into developing a Sentic API with the testing of different models and techniques and comparing t...

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Bibliographic Details
Main Author: Yang, Willis Xianzu
Other Authors: Erik Cambria
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2024
Subjects:
Online Access:https://hdl.handle.net/10356/174302
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1743022024-05-03T15:37:58Z Sentic API for mental health detection Yang, Willis Xianzu Erik Cambria School of Computer Science and Engineering cambria@ntu.edu.sg Computer and Information Science Sentiment text analysis Neural network Long short-term memory Sentiment text analysis, which is a pivotal aspect of Natural Language Processing (NLP), involves reading different texts and identifying their labels (positive, negative, neutral). This report will dive into developing a Sentic API with the testing of different models and techniques and comparing the result of the different methods used. In this project, we have explored the different techniques namely Sentic API, TextBlob and Valer Aware Dictionary and sEntiment Reasoner (VADER). In addition, after we have done the sentiment text analysis, we will be feeding this data into models for training. This is a form of supervised training and the models that we have explored into are Recurrent Neural Networks and Long Short-Term Memory (LSTM) networks. Within this model, we will also be training the models with different hyperparameters to compare and find the best parameters for the model that we have come up with. Bachelor's degree 2024-03-26T00:56:52Z 2024-03-26T00:56:52Z 2024 Final Year Project (FYP) Yang, W. X. (2024). Sentic API for mental health detection. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/174302 https://hdl.handle.net/10356/174302 en SCSE23-0101 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Computer and Information Science
Sentiment text analysis
Neural network
Long short-term memory
spellingShingle Computer and Information Science
Sentiment text analysis
Neural network
Long short-term memory
Yang, Willis Xianzu
Sentic API for mental health detection
description Sentiment text analysis, which is a pivotal aspect of Natural Language Processing (NLP), involves reading different texts and identifying their labels (positive, negative, neutral). This report will dive into developing a Sentic API with the testing of different models and techniques and comparing the result of the different methods used. In this project, we have explored the different techniques namely Sentic API, TextBlob and Valer Aware Dictionary and sEntiment Reasoner (VADER). In addition, after we have done the sentiment text analysis, we will be feeding this data into models for training. This is a form of supervised training and the models that we have explored into are Recurrent Neural Networks and Long Short-Term Memory (LSTM) networks. Within this model, we will also be training the models with different hyperparameters to compare and find the best parameters for the model that we have come up with.
author2 Erik Cambria
author_facet Erik Cambria
Yang, Willis Xianzu
format Final Year Project
author Yang, Willis Xianzu
author_sort Yang, Willis Xianzu
title Sentic API for mental health detection
title_short Sentic API for mental health detection
title_full Sentic API for mental health detection
title_fullStr Sentic API for mental health detection
title_full_unstemmed Sentic API for mental health detection
title_sort sentic api for mental health detection
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/174302
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