MODEL OF SENTIMENT ANALYSIS FOR SOCIAL MEDIA DATA

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1 MODEL OF SENTIMENT ANALYSIS FOR SOCIAL MEDIA DATA Nurul Atasha Khairuddin, Kamilia Kamardin Advanced Informatics School, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, Kuala Lumpur, Malaysia. Abstract Since the introduction of social media, companies are increasingly adopting social media technologies, using Twitter to reach out to customers or YouTube to demonstrate product features. Social media is widely applied as a mean of marketing and public relation by companies. This paper is reviewing and present a two current system-based on customer s sentiment measurement in social media. The analysis will cover on system architecture for both system to determine the best characteristic that system should have in order to develop a data analysis application. At the end of the paper, the researcher will be proposed a new architecture which based on the best features from both current chooses system. Keywords: Text Mining, Data Analysis, Sentiment Analysis, Social Media 1. Introduction Marketing element in industries is a rapid growing industries with the large number of customers, the amount of data increasing drastically and it provides company opportunity to analyse sentiment of the customer (Sun, et al., Sept.-Nov. 2014). However, the current system in the marketing or customer research and development sector are not able to produce analysis based on the data. Besides that, data that collected by the industries are only unstructured data rather than structured data (Hsin-Ying, et al., 2014). Companies are not collecting data from social media such as Facebook, Twitter, Google plus and etc. where the customers expressing their feelings and thoughts through the social medias (Hsin-Ying, et al., 2014). Thus, it leads the companies to invest money on the wrong customers where the customers do not require such services. Social Medias are overloaded with data that able to use by companies with data mining technology in order extract hidden meanings behind the data which will increase the quality and efficiency of the marketing services. (Balar, et al., 2013) (Adel & Dr. Nedhal, 2013). Besides that, marketing industry depends on market analyst to analyse sentiment of the customers but * Nurul Atasha Khairuddin. address: natasha28@live.utm.my 1

2 the analysis only depends on one person s opinion. Market analyst hard to analyse the vast amount of data to analyse the customer sentiment with dynamic economy environment which change frequently by various external factors (Guo, et al., 2012). The Sentiment Analysis on Social Media Data would be using data mining technique specifically it would be text mining because the collected data is unstructured data type. Based on the analysis of the data, the system will classify the group of customer in their respective group. The result of analysis will provide in visual format so that the user able to understand easily. The system will obtain unstructured data such as text and emotions from the social media to perform data cleaning process and will be used for analysis on customer and risk that associated with the customer. 2. Related Works HotelOpinion System HotelOpinion system is a system that analyse the customer reviews from Agoda website based on Thai language (Kongthon, et al., 2011). This system is a web based system that allow the user to compare two hotels in Thailand, based on the reviews that posted by the customer. The system will analyse the reviews based on the feature-based opinion mining, where the user able to choose particular feature they like among the hotels that they are interested in to be compare. The user has to choose where the user want to stay in the city then the user have to select which hotel they want to stay based on that the system will provide an sentiment analysis chart from the reviews so the user easily can make decision without going through all the comments. The system will categorize whether the sentiment of the user more towards good or bad (Kongthon, et al., 2011). Besides that, the system also allows the mangers of the hotel to view whether the reviews more towards good or bad so they can improve the services that has bad reviews which will increase customer satisfaction. City Image Network Monitoring System (CINMS) City Image Monitoring System (CINMS) is a system that helps the government sector to monitor the city using opinion mining (Gang & Jing, 2010). The CINMS will obtain textual information from the webpages to obtain opinion about the city news, emergencies and other information that will help the government to improve the place immediately if have any negative feedback. The system will obtain data from the forums, discussion group, blogs and etc. Based on the data the system will analyse the person or organization opinion whether the opinion is positive or negative (Gang & Jing, 2010). There will be few steps where firstly, the system will extract the related information from collected data. Secondly, the system will convert the unstructured text into the structure text. Lastly, based on structured data the system will identify whether the opinion of the user is expressed in the positive, negative or neutral form. Then the result will be displayed to the user in chart representation (Gang & Jing, 2010). 3. Research Model The architecture in Figure 1 shows how the opinion mining works in the system. The system contains automatic construction of lexicons to the Thai language (Kongthon, et al., 2011). The approach that used in the system is based on the syntactic pattern analysis that has two types of lexicons domain dependent and domain independent. Domain-dependent lexicons include features and polar words. Domain-independent is a 2

3 regular word that provides different meaning and functions in the sentence. The system will be tagged the words based on these two lexicons. The system used lexicons that tagged to perform feature-based opinion mining and the system will classify and summarize the reviews as positive or negative based on different features (Kongthon, et al., 2011). Then the system will provide a graph that shows the patterns based on negative and positive comments. Figure 1: HotelOpinion System Architecture The figure shows how the CINMS works, the system has three major stages. First stage is collection and preparation stage, the corpus acquisition system will obtain all the information that related to the city such as reviews, hot news, emergencies and other information in fixed time (Gang & Jing, 2010). Then the data will be selected manually and as well automatically with the system. The data will be saved in the structured format after the extraction. The system will determine whether the opinion is positive, negative or neutral based on the lexicon technique (Gang & Jing, 2010). In the second stage, the system will use training set to build feature set and sentiment set. Then the system will use feedback classifier to adjust the data, the training set will be updated based on the feedback to improve the classification. Finally, the analysis will be represented to the user through charts. In this system, the method that used in text classification, natural processing to identify the opinion of the user. 3

4 Figure 2: System Architecture for CINMS 4. Methodology The methodology that has been use to review both data analysis model in this research is comparing and combining. This paper has compared and classified the criteria for both data analysis model based on work process and flow, expected result and hierarchical criteria. 5. Proposed System The Customer Analysis on Big Data system s main function are to analyse the sentiment of company s customer based on data that collected from the social media. The system will able to collect data from Twitter, Facebook, Google plus and Forums to obtain the positive and negative comments from the customers. Moreover, the system only able to analyse comments, post or tweets that written in English. Furthermore, the system will analyse the customer s sentiment based on the negative and positive comments, tweets or post then the system will represent the analysed data in the visualization form such as graph where the user able to choose which graph they want to represent the analysed result. In addition, the system also will provide comment on area that need to improvise by the company based on the analysis. The user of the system also able to view and print previous reports of the analysis. The basic architecture of the system will be shown in the system design to illustrate how the system works. 4

5 Figure 3: Detail System Architecture for Customer Analysis Big Data system The Figure 6 above shows the architecture of the system, the crawler will be used to crawl the data from social media then pre-processing technique will be applied such as data cleaning, feature extraction and feature selection. Next the data will be labelled and categorized then the data will be represented in the hierarchical representation to obtain all the links between the data. Text mining algorithm will be applied to find the patterns and refining technique will be applied to result to get better results it will be continued in loop until optimal pattern is obtained. The knowledge discovered from the analysis will be shown to the user. 6. Conclusion This paper had proposed a system based data mining system to be applied in Customer analysis on big data system that supports the companies and marketing in monitoring what is published on the web about their organizations. The system is capable of detecting and retrieving reviews on the web, to classify and analyse them, as well as to generate comprehensive overviews of these comments. We showed that, despite some remaining issues, the system provides good performance for the analysis and the classification tasks. Further research will be necessary especially with respect to the demarcation of evaluative and neutral text as well as to the handling of multi-topic segments, especially for the user interface. In conclusion, based on the domain research and technical research the research will be using most of the techniques. 5

6 References [1] Balar, A., Malviya, N., Prasad, S. & Ajinkya, G., Forecasting consumer behavior with innovative value proposition for organizations using Big Data Analytics IEEE International Conference on Computational Intelligence and Computing Research, pp. 1,4. [2] Sun, N., G. Morris, J. & Xu, J., Sept.-Nov icare: A framework for big data-based banking customer analytics. IBM Journal of Research and Development, Volume 58, pp. 4:1,4:9 [3] Simon T, Goldberg A, Adini B. Socializing in emergencies A review of the use of social media in emergency situations. International Journal of Information Management. 35(5), 2015, [4] Hsin-Ying, W., Liu, K.-L. & Trappey, C., Understanding Customers Using Facebook Pages: Data Mining Users Feedback Using Text Analysis. Proceedings of the 2014 IEEE 18th International Conference on Computer Supported Cooperative Work in Design, pp [5] Adel, O. N. & Dr. Nedhal, A. A. S., The Integrating Between Web Usage Mining and Data Mining Techniques th International Conference on Computer Science and Information Technology (CSIT), pp [6] Kongthon, A. et al., HotelOpinion: An Opinion Mining System on Hotel Reviews in Thailand. Technology Management in the Energy Smart World (PICMET), pp [7] Guo, Z. X., Li, M. & Wong, W. K., Intelligent multivariate sales forecasting using wrapper approach and neural networks. 10th IEEE International Conference on Industrial Informatics (INDIN), pp [8] Gang, L. & Jing, C., Study on the City Image Network Monitoring System based on Opinion-mining International Conference on Networking and Digital Society, pp