Abstract
Technology question and answer websites are a great source of technical knowledge. Users of these websites raise various types of technical questions, and answer them. These questions cover a wide range of domains in Computer Science like Networks, Data Mining, Multimedia, Multi-threading, Web Development, Mobile App Development, etc. Analyzing the actual textual content of these websites can help computer science and software engineering community better understand the needs of developers and learn about the current trends in technology. In this project, textual data from famous question and answer website called StackOverflow, is analyzed using Latent Dirichlet Allocation (LDA) topic modeling algorithm. The results show that this techniques help discover dominant topics in developer discussions. These topics are analyzed to find a number of interesting observations such as popular technology/language, impact of a technology, technology trends over time, relationship of a technology/language with other technologies and comparison of technologies addressing an area of computer science or software engineering.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings - 12th IEEE International Conference on Semantic Computing, ICSC 2018 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 391-396 |
| Number of pages | 6 |
| Volume | 2018-January |
| ISBN (Electronic) | 9781538644072 |
| DOIs | |
| State | Published - Apr 9 2018 |
| Event | 12th IEEE International Conference on Semantic Computing, ICSC 2018 - Laguna Hills, United States Duration: Jan 31 2018 → Feb 2 2018 |
Other
| Other | 12th IEEE International Conference on Semantic Computing, ICSC 2018 |
|---|---|
| Country | United States |
| City | Laguna Hills |
| Period | 1/31/18 → 2/2/18 |
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Keywords
- Latent Dirichlet Allocation (LDA)
- Machine Learning
- Natural Language Processing
- Topic modeling
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Networks and Communications
- Human-Computer Interaction
- Information Systems and Management
Cite this
Identifying Trends in Technologies and Programming Languages Using Topic Modeling. / Johri, Vishal; Bansal, Srividya.
Proceedings - 12th IEEE International Conference on Semantic Computing, ICSC 2018. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. p. 391-396.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Identifying Trends in Technologies and Programming Languages Using Topic Modeling
AU - Johri, Vishal
AU - Bansal, Srividya
PY - 2018/4/9
Y1 - 2018/4/9
N2 - Technology question and answer websites are a great source of technical knowledge. Users of these websites raise various types of technical questions, and answer them. These questions cover a wide range of domains in Computer Science like Networks, Data Mining, Multimedia, Multi-threading, Web Development, Mobile App Development, etc. Analyzing the actual textual content of these websites can help computer science and software engineering community better understand the needs of developers and learn about the current trends in technology. In this project, textual data from famous question and answer website called StackOverflow, is analyzed using Latent Dirichlet Allocation (LDA) topic modeling algorithm. The results show that this techniques help discover dominant topics in developer discussions. These topics are analyzed to find a number of interesting observations such as popular technology/language, impact of a technology, technology trends over time, relationship of a technology/language with other technologies and comparison of technologies addressing an area of computer science or software engineering.
AB - Technology question and answer websites are a great source of technical knowledge. Users of these websites raise various types of technical questions, and answer them. These questions cover a wide range of domains in Computer Science like Networks, Data Mining, Multimedia, Multi-threading, Web Development, Mobile App Development, etc. Analyzing the actual textual content of these websites can help computer science and software engineering community better understand the needs of developers and learn about the current trends in technology. In this project, textual data from famous question and answer website called StackOverflow, is analyzed using Latent Dirichlet Allocation (LDA) topic modeling algorithm. The results show that this techniques help discover dominant topics in developer discussions. These topics are analyzed to find a number of interesting observations such as popular technology/language, impact of a technology, technology trends over time, relationship of a technology/language with other technologies and comparison of technologies addressing an area of computer science or software engineering.
KW - Latent Dirichlet Allocation (LDA)
KW - Machine Learning
KW - Natural Language Processing
KW - Topic modeling
UR - http://www.scopus.com/inward/record.url?scp=85048384043&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85048384043&partnerID=8YFLogxK
U2 - 10.1109/ICSC.2018.00078
DO - 10.1109/ICSC.2018.00078
M3 - Conference contribution
AN - SCOPUS:85048384043
VL - 2018-January
SP - 391
EP - 396
BT - Proceedings - 12th IEEE International Conference on Semantic Computing, ICSC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
ER -