Performance Comparison of Sentiment Lexicons in Predicting American Citizen's Sentiment for the Apartment Rents

다국어 초록 (Multilingual Abstract)

The primary purpose of this study is to identify which sentiment lexicon performs better in predicting citizens' sentiment about apartment rents. To accomplish this research purpose, I performed sentiment analysis based on sentiment lexicon and sentiment analysis based on the machine learning algorithm simultaneously. As a result of the analysis, the AFINN Sentiment Lexicon turned out to be the best performing sentiment lexicon in all performance metrics categories. Also, the performance of each machine learning classifier showed a significant difference in classifying and predicting citizens' sentiment on apartment rents by sentiment lexicon. In other words, AdaBoost in the AFINN Sentiment Lexicon and the Opinion Lexicon and Random Forest in the NRC Emotion Lexicon proved to be the best algorithms. This study can contribute to the research of this area because it uses sentiment analysis based on the sentiment lexicon and sentiment analysis based on the machine learning algorithm simultaneously in classifying and predicting citizens' sentiment about apartment rents. Despite these advantages, this research has limitations in that it uses only a limited range of sentiment lexicons and machine learning algorithms. Therefore, future studies will have to use more sentiment lexicons and machine learning algorithms to classify and predict citizens' sentiment about apartment rents accurately.

참고문헌 (Reference)

1 Go, A., "Twitter sentiment analysis" 17 : 2009

2 Da Silva, N. F., "Tweet sentiment analysis with classifier ensembles" 66 : 2014

3 Mahadzir, N. H., "Towards sentiment analysis application in housing projects" AIP Publishing 1761 (1761): 020060-, 2016

4 Gallimore, P., "The role of investor sentiment in property investment decisions" 19 (19): 2002

5 Fields, D., "The financialisation of rental housing: A comparative analysis of New York City and Berlin" 53 (53): 2016

6 Baccianella, S., "Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining" 10 : 2010

7 Dietzel, M., "Sentiment-based commercial real estate forecasting with Google search volume data" 32 (32): 2014

8 Thelwall, M., "Sentiment strength detection for the social web" 63 (63): 2012

9 Devitt, A., "Sentiment polarity identification in financial news: A cohesionbased approach" 2007

10 Bifet, A., "Sentiment knowledge discovery in twitter streaming data" 2010

11 Mejova, Y., "Sentiment analysis: An overview" University of Iowa 2009

12 Fang, X., "Sentiment analysis using product review data" 2 (2): 2015

13 Huq, M. R., "Sentiment analysis on Twitter data using KNN and SVM" 8 (8): 2017

14 Medhat, W., "Sentiment analysis algorithms and applications: A survey" 5 (5): 2014

15 김윤기, "Sentiment Analysis on Monthly House Rent on Twitter: with Special Reference to the Relationship between Sentiment on House Rent and House Rent" 한국지적정보학회 19 (19): 37-56, 2017

16 Gyourko, J., "Rent controls and rental housing quality: A note on the effects of New York City's old controls" 27 (27): 1990

17 Koning, R. H., "Rent assistance and housing demand" 66 (66): 1997

18 Pal, M., "Random forest classifier for remote sensing classification" 26 (26): 2005

19 García-Cumbreras, M. Á., "Pessimists and optimists: Improving collaborative filtering through sentiment analysis" 40 (40): 2013

20 Rochdi, K., "Outperforming the benchmark: online information demand and REIT market performance" 33 (33): 2015

21 Lipton, Z. C., "Optimal thresholding of classifiers to maximize F1 measure" Springer 2014

22 Gokulakrishnan, B., "Opinion mining and sentiment analysis on a twitter data stream" IEEE 2012

23 Hu, M., "Mining and summarizing customer reviews" ACM 2004

24 Wyly, E., "Mapping public housing: the case of New York City" 9 (9): 2010

25 Ellen, I. G., "Low income homeownership: Examining the unexamined goal" 2002

26 Maas, A. L., "Learning word vectors for sentiment analysis" Association for Computational Linguistics 1 : 142-150, 2011

27 Tang, D., "Learning sentiment-specific word embedding for twitter sentiment classification" 1 : 2014

28 Ling, D. C., "Investor sentiment, limits to arbitrage and private market returns" 42 (42): 2014

29 Aidala, A. A., "Housing need, housing assistance, and connection to HIV medical care" 11 (11): 2007

30 Phibbs, P., "Housing assistance and non-shelter outcomes" Australian Housing and Urban Research Institute 2005

31 Marcuse, P., "Gentrification, abandonment, and displacement: Connections, causes, and policy responses in New York City" 28 : 1985

32 Somantri, O., "Feature Weights Menggunakan Particle Swarm Optimization Untuk Sentiment Analysis Penilaian Kepuasan Pelanggan Makanan Kuliner" 1 (1): 2018

33 Dang, V. H., "Enhancing the accuracy of rainfall-induced landslide prediction along mountain roads with a GIS-based random forest classifier" 2018

34 Mohammad, S. M., "Emotions evoked by common words and phrases: Using mechanical turk to create an emotion lexicon" Association for Computational Linguistics 2010

35 Deng, Y., "Duration of residence in the rental housing market" 26 (26): 2003

36 Kim, S. M., "Determining the sentiment of opinions" Association for Computational Linguistics 2004

37 Witten, I. H., "Data Mining: Practical machine learning tools and techniques" Morgan Kaufmann 2016

38 Liu, X., "D-Storm: Dynamic Resource-Efficient Scheduling of Stream Processing Applications" IEEE 2017

39 Gupta, R. K., "Crystalnest at semeval-2017 task 4: Using sarcasm detection for enhancing sentiment classification and quantification" 2017

40 Mohammad, S. M., "Crowdsourcing a word–emotion association lexicon" 29 (29): 2013

41 You, Q., "Cross-modality consistent regression for joint visual-textual sentiment analysis of social multimedia" ACM 2016

42 Gao, Y., "Convolutional neural network based sentiment analysis using Adaboost combination" IEEE 2016

43 Gupte, A., "Comparative study of classification algorithms used in sentiment analysis" 5 (5): 2014

44 Clayton, J., "Commercial real estate valuation: fundamentals versus investor sentiment" 38 (38): 2009

45 Tan, S., "An empirical study of sentiment analysis for chinese documents" 34 (34): 2008

46 Nielsen, F. A., "Afinn, informatics and mathematical modelling" technical university of Denmark 2011

47 Annett, M., "A comparison of sentiment analysis techniques: Polarizing movie blogs" 2008

48 Koto, F., "A comparative study on twitter sentiment analysis: Which features are good?" Springer 2015