Aspect based Sentiment Analysis of Employee's Review Experience
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Background: Employees of technology companies evaluate their experience through online reviews. Online reviews of companies from employees or former employees help job seeker to find out the weaknesses and strengths of the companies. The reviews can be used as an evaluation tool for each technology company to understand their employee's perceptions. However, most information on online reviews is not well responded since some of the detailed information of the company is missing.
Objective: This study aims to generate an Aspect-based Sentiment Analysis using user review data. The review data were then extracted and classified into five aspects: work balance, culture value, career opportunities, company benefit, and management. The output of this study is the aspect score from each company.
Methods: This study suggests a method to analyze online reviews from employees in detail, so it can prevent the missing of specific information. The analysis was sequentially carried out in five stages. First, user review data were crawled from Glassdoor and stored in a database. Second, the raw data were processed in the data pre-processing stage to delete the incomplete data. Third, the words other than noun keyword were eliminated using Standford POS Tagger. Fourth, the noun keywords were then classified into each aspect. Finally, the aspect score was calculated based on the aspect-based sentiment analysis.
Results: Result showed that the proposed method managed to turn raw review data into five aspects based on user perception.
Conclusion: The study provides information for two parties, job seeker and the company. The analysis of the review could help the job seeker to decide which company that suits his need and ability. For the companies, it can be a great assistance because they will be more aware of their strengths and weaknesses. This study could possibly also provide ratings to the companies based on the aspects that have been determined.
E. Marrese-Taylor, J. D. Vel´asquez, F. Bravo-Marquez, and Y. Matsuo, "Identifying Customer Preferences about Tourism Products using an Aspect-Based Opinion Mining Approach”, in 17th International Conference in Knowledge Based and Intelligent Information and Engineering Systems, Kitakyushu, Japan, September 2013, pp. 182-191.
M. Hu, B. Liu, "Mining opinion features in customer reviews”, in Proceedings of the National Conference on Artificial Intelligence, Menlo Park, CA, MIT Press, 2004, pp. 755–760, 2004.
M. A. Shahkhali, F. Ahmadi-Abkenari, "Sentiment Mining on Products Features based on Part of Speech Tagging Approach”, International journal of Computer Science & Network Solutions, vol. 3, no. 12, pp. 1-12, 2015.
C. S. Sindhu, "Aspect Based Sentiment Analysis of Amazon Product Reviews”, International Journal of Pure and Applied Mathematics, vol. 118, no. 22, pp. 151-157, 2014.
K. Srividya, A. M. Sowjanya, "Aspect Based Sentiment Analysis using POS Tagging and TFIDF ”, International Journal of Engineering and Advanced Technology, vol. 8, no. 6, pp. 1960-1963, 2019
M. S. Akhtar, D. Gupta, A. Ekbal, P. Bhattacharyya, "Feature selection and ensemble construction: a two-step method for aspect based sentiment analysis”, Knowledge-Based System, vol. 125, pp. 116-135, 2017.
M. Al-Smadi, O. Qawasmeh, M. Al-Ayyoub, Y. Jararweh, B. Gupta, "Deep Recurrent neural network vs. support vector machine for aspect-based sentiment analysis of Arabic hotels' reviews”, Journal of Computational Science, vol. 27, pp. 386-393, 2018.
A. Garcia-Pablos, M. Cuadros, G. Rigau, "W2VLDA: Almost unsupervised system for Aspect Based Sentiment Analysis”, Expert Systems with Applications, vol. 91, pp. 127-137, 2018.
Y. Al-Amrani, M. Lazaar, K.E. Elkadiri, "Sentiment Analysis using supervised classification algorithms”, in Proceedings of the 2nd international Conference on Big Data, Cloud and Applications, no. 61, pp. 685, 2017.
WebHarvy: Intuitive Powerful Visual Web Scraper, https://www.webharvy.com/
Chapman, P. et al. 2000. CRISP-DM 1.0 Step-by-step data mining guide. SPSS; 2000.
D. Delen, "Extracting Knowledge from Published Literature Using RapidMiner”, in Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications, 2012.
V. Kotu, B. Deshpande, "Getting Started with RapidMiner”, in Data Science (Second Edition), 2019.
T. Verma, R. Renu, D.Gaur, "Tokenization and Filtering Process in RapidMiner”, International Journal of Applied Information Systems, vol. 7, pp. 16-18, 2014.
M, Porter, "An algorithm for suffix stripping”, Program: electronic library and information systems, vol. 14, no. 3, pp. 130-137, 1980.
A. Jivani, "A Comparative Study of Stemming Algorithms”, Int. J. Comp. Tech. Appl., vol. 2, pp. 1930-1938, 2011.
D. Delen, "Predicting Box Office Success of Motion Pictures with Text Mining”, Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications, 2012
J.Yi, T. Nasukawa, R. Bunescu, W. Niblack. "Sentiment analyzer: Extracting sentiments about a given topic using natural language processing techniques”, in Proceedings of the IEEE International Conference on Data Mining, 2013.
A. Dabirian, J. Kietzmann, H. Diba, "A great place to work!? Understanding crowdsourced employer branding”, Business Horizon, vol. 60, no. 2, pp. 197-205, 2016.
P. D. Hong, A. C. Le, "Exploiting multiple word embeddings and one-hot character vectors for aspect-based sentiment analysis”, Int. J. Approx. Reason, vol. 103, pp. 1-10, 2018.
K. Bafna, D. Toshniwal, "Feature Based Summarization of Customers' Reviews of Online Products”, Procedia Computer Science, vol. 22, pp. 142-151, 2013.
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