Identifying Trends in Technologies and Programming Languages Using Topic Modeling

Vishal Johri; Srividya Bansal

doi:10.1109/ICSC.2018.00078

Identifying Trends in Technologies and Programming Languages Using Topic Modeling

Vishal Johri, Srividya Bansal

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Scopus citations

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	https://doi.org/10.1109/ICSC.2018.00078
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

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

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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

Johri, V & Bansal, S 2018, Identifying Trends in Technologies and Programming Languages Using Topic Modeling. in Proceedings - 12th IEEE International Conference on Semantic Computing, ICSC 2018. vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 391-396, 12th IEEE International Conference on Semantic Computing, ICSC 2018, Laguna Hills, United States, 1/31/18. https://doi.org/10.1109/ICSC.2018.00078

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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.",

keywords = "Latent Dirichlet Allocation (LDA), Machine Learning, Natural Language Processing, Topic modeling",

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booktitle = "Proceedings - 12th IEEE International Conference on Semantic Computing, ICSC 2018",

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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.

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Identifying Trends in Technologies and Programming Languages Using Topic Modeling

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