Socio-feedback : the context analysis
To provide better socio-feedback for the purpose of helping people conduct better conversation and human interaction activities, understanding the conversation context and topic has become a crucial task. This project concentrates on developing a Machine Learning system that tracks the context of co...
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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
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2015
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| Online Access: | http://hdl.handle.net/10356/65793 |
| _version_ | 1826126000635772928 |
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| author | Lu, Jiahong |
| author2 | Justin Dauwels |
| author_facet | Justin Dauwels Lu, Jiahong |
| author_sort | Lu, Jiahong |
| collection | NTU |
| description | To provide better socio-feedback for the purpose of helping people conduct better conversation and human interaction activities, understanding the conversation context and topic has become a crucial task. This project concentrates on developing a Machine Learning system that tracks the context of conversation by speech recognition, natural language processing and text classification. Throughout the process, a Naïve Bayes Classification model is built and its performance is improved gradually through different methods in each stage. At the end, the classification model is able to classify the conversation into “Business meeting”, “Court”, “Sports Chatting” and “Restaurant” contexts with an overall accuracy of 96.3%. |
| first_indexed | 2024-10-01T06:45:35Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/65793 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:45:35Z |
| publishDate | 2015 |
| record_format | dspace |
| spelling | ntu-10356/657932023-07-07T17:20:22Z Socio-feedback : the context analysis Lu, Jiahong Justin Dauwels School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems To provide better socio-feedback for the purpose of helping people conduct better conversation and human interaction activities, understanding the conversation context and topic has become a crucial task. This project concentrates on developing a Machine Learning system that tracks the context of conversation by speech recognition, natural language processing and text classification. Throughout the process, a Naïve Bayes Classification model is built and its performance is improved gradually through different methods in each stage. At the end, the classification model is able to classify the conversation into “Business meeting”, “Court”, “Sports Chatting” and “Restaurant” contexts with an overall accuracy of 96.3%. Bachelor of Engineering 2015-12-15T02:29:14Z 2015-12-15T02:29:14Z 2015 2015 Final Year Project (FYP) http://hdl.handle.net/10356/65793 en Nanyang Technological University 54 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems Lu, Jiahong Socio-feedback : the context analysis |
| title | Socio-feedback : the context analysis |
| title_full | Socio-feedback : the context analysis |
| title_fullStr | Socio-feedback : the context analysis |
| title_full_unstemmed | Socio-feedback : the context analysis |
| title_short | Socio-feedback : the context analysis |
| title_sort | socio feedback the context analysis |
| topic | DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems |
| url | http://hdl.handle.net/10356/65793 |
| work_keys_str_mv | AT lujiahong sociofeedbackthecontextanalysis |