Classification of project management tool reviews using machine learning-based sentiment analysis

International Conference on Information Technology and Digital Applications 2018

计算机科学;无线电电子学

Baro, R.A.^1 ; Pagudpud, M.V.^2 ; Padirayon, L.M.^3 ; Dilan, R.E.^4

La Union, Sapilang, Bacnotan

2515, Philippines^1

Magsaysay, Saguday, Quirino

3400, Philippines^2

Namuac, Sanchez Mira, Cagayan

3518, Philippines^3

La Union, Consolacion, Agoo

2504, Philippines^4

关键词: K nearest neighbor (KNN);    Microsoft Office;    MS-project;    Portfolio managements;    Project management tools;    Second level;    Tri grams;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/482/1/012041/pdf
DOI  :  10.1088/1757-899X/482/1/012041

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

Managing the daily responsibilities in an organization is a great task for company administrators. Employing a dedicated project management tool is a great aid in all the phases of project management. With a variety of project management tools available in the market, it is a demanding task to look for a tool appropriate to the needs of the organization. It is an observation by scholars and suggests project management tool users tend to read first the reviews and comments of the product before deciding on the tool they are going to implement. This study facilitates sentiment analysis which can help identify effective and efficient project management tool. Microsoft Office Project is one of the most popular and most reviewed tools for more efficient project and portfolio management. The researchers manually extracted data from various websites containing comments and reviews from different users or reviewers. Machine learning-based-approach using RapidMiner was utilized to analyze the collected data from the web reviews. Sentiment analysis from MS Project reviews was applied using supervised learning K-nearest neighbor (KNN). The first level of classification involved classifying statements as "satisfied" and "dissatisfied." The second level of classification involved clustering sentiments as cost, experience, task, support and interface. Statements classified as satisfied is greater than dissatisfied. The best features to apply when classifying the PM tool reviews dataset are using stopwords, length, stemming and trigram.