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Consumer’s Sentiment Analysis of Popular Phone Brands and Operating System Preference
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 155 - Number 4 |
| Year of Publication: 2016 |
| Authors: Ashna Bali, Payal Agarwal, Gayatri Poddar, Devyani Harsole, Nilufar M. Zaman |
10.5120/ijca2016912288
|
Ashna Bali, Payal Agarwal, Gayatri Poddar, Devyani Harsole, Nilufar M. Zaman . Consumer’s Sentiment Analysis of Popular Phone Brands and Operating System Preference. International Journal of Computer Applications. 155, 4 ( Dec 2016), 15-19. DOI=10.5120/ijca2016912288
Abstract
In today’s era, which is symbolic of ‘information overload’, we are living in a time where knowledge needs to be mined from information which is available in abundance, one such instance being mining sentiments from user textual reviews which plays a decisive role in consumer’s decision making processes. Today’s customers are intelligent enough and will cross-check before purchasing any product and the best place for that are online reviews which are ubiquitous. In today’s competitive market, analyzing user needs and sentiments is of uttermost importance as one bad review can make or break the profits of a brand since Word of Mouth plays a significant role in building a customer base for a brand or product. An approach for consumer’s sentiment analysis has been proposed, which will mine user’s sentiments and thus pave a foundation for generating the popularity of the products which will lead to ‘personalized’ results which are necessary in today’s client-centric world. This in turn is helpful for brands wanting to expand the horizon of their user turnover and to devise better retailing strategies to sell their products. The approach is to convert these textual reviews into star ratings that will describe the ‘likeability’ and popularity of the products.
References
- Xiaojiang Lei, Xueming Qian, Member, IEEE, and Guoshuai Zhao, ”Rating Prediction Based on Social Sentiment From Textual Reviews”, IEEE TRANSACTIONS ON MULTIMEDIA, VOL. 18, NO. 9, SEPTEMBER 2016.
- Ravendra Ratan Singh Jandail Computing Science and Engineering, Galgotias University, India, ”A proposed Novel Approach for Sentiment Analysis and Opinion Mining”, International Journal of UbiComp (IJU), Vol.5, No.1/2, April 2014.
- Asma Musabah Alkalbani, Ahmed Mohamed Ghamry, Farookh Khadeer Hussain, Omar Khadeer Hussain, ”Sentiment Analysis and classification for Software as a Service Reviews”,2016 IEEE 30th International Conference on Advanced Information Networking and Applications.
- Deepali Arora, Kin Fun Li and Stephen W. Neville, Department of Electrical and Computer Engineering University of Victoria, ”Consumers’ sentiment analysis of popular phone brands and operating system preference using Twitter data: A feasibility study”, 2015 IEEE 29th International Conference on Advanced Information Networking and Applications.
- Wei Yen Chong, Bhawani Selvaretnam, Lay-Ki Soon, ”Natural Language Processing for Sentiment Analysis”, 2014 4th International Conference on Artificial Intelligence with Applications in Engineering and Technology.
- Marjan Goodarzi, Maryam Tayefeh Mahmoudi, Ramin Zamani, ”A Framework for Sentiment Analysis on Schemabased Research Content Via Lexica Analysis”, 2014 7th International Symposium on Telecommunications (IST’2014).
- Zohreh Madhoushi, Abdul Razak Hamdan, Suhaila Zainudin, ”Sentiment Analysis Techniques in Recent Works”, Science and Information Conference 2015 July 28-30, 2015 — London, UK.
- Duan, Wenjing, Bin Gu, and Andrew B. Whinston. "Do online reviews matter?—An empirical investigation of panel data." Decision support systems 45.4 (2008): 1007-1016.
- Liebmann, Michael, Michael Hagenau, and Dirk Neumann. "Information Processing in Electronic Markets: Measuring Subjective Interpretation Using Sentiment Analysis." (2012).
- Chevalier, Judith A., and Dina Mayzlin. "The effect of word of mouth on sales: Online book reviews." Journal of marketing research 43.3 (2006): 345-354.
- Dragut, Eduard C., et al. "Construction of a sentimental word dictionary."Proceedings of the 19th ACM international conference on Information and knowledge management. ACM, 2010.
- Liu, Xumin, et al. "Extracting, ranking, and evaluating quality features of web services through user review sentiment analysis." Web Services (ICWS), 2015 IEEE International Conference on. IEEE, 2015.
- Mukwazvure, Addlight, and K. P. Supreethi. "A hybrid approach to sentiment analysis of news comments." Reliability, Infocom Technologies and Optimization (ICRITO)(Trends and Future Directions), 2015 4th International Conference on. IEEE, 2015.
- Nithya, R., and D. Maheswari. "Sentiment Analysis on Unstructured Review." Intelligent Computing Applications (ICICA), 2014 International Conference on. IEEE, 2014.
- Rain, Callen. "Sentiment Analysis in Amazon Reviews Using Probabilistic Machine Learning." Swarthmore College (2013).
- Y. Sawakoshi, M. Okada and K. Hashimoto, "An Investigation of Effectiveness of "Opinion" and "Fact" Sentences for Sentiment Analysis of Customer Reviews," Computer Application Technologies(CCATS), 2015 International Conference on, Matsue, 2015, pp. 98-102.
- Bhatt, Aashutosh, et al. "Amazon Review Classification and Sentiment Analysis."
- Fang, Xing, and Justin Zhan. "Sentiment analysis using product review data." Journal of Big Data 2.1 (2015): 1.
- Schouten, Kim, and Flavius Frasincar. "Survey on aspect-level sentiment analysis." IEEE Transactions on Knowledge and Data Engineering 28.3 (2016): 813-830.
- Collomb, Anaıs, et al. "A Study and Comparison of Sentiment Analysis Methods for Reputation Evaluation."
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