Abstract
The customer sentiments are very important to any business, as positive or negative feedback can affect the sales and adoption of the product in the market and subsequently define the product’s success. The monthly active usage of major social media platform such as Facebook is 2.32 billion monthly active users (MAU) and of Twitter is 126 million; hence, the market for understanding the customer sentiment through social media can be a game changer for a company and can help define the success of the company in the future. If the sentiments of the users are not captured correctly, it could lead to catastrophic failure of the product and hamper company’s reputation. Existing systems require a lot of manual tasks such as customer surveys, aggregating the sentiments then generating excel reports which are not very interactive and require a lot of time to gather results. These reports also do not show real-time data. People express their opinion on social media. Companies can use such platforms to capture honest and transparent opinions of the consumers. The cognitive service evaluates tweet texts and returns a sentiment score for each text, ranging from 0 (negative) to 1 (positive). This capability is useful for detecting positive and negative sentiments in social media such as Facebook, Twitter, customer reviews, and discussion forums. The machine learning model used by the cognitive service helps determine sentiment using data provided by the user. This feedback can allow the company to know the acceptance of the product in prototype stages and can use the same to modify the product as per the customer feedback before making the product generally available. The implementation environment uses Azure services (Logic apps, Cognitive Services, SQL Database, App Services) with Power BI used to generate real-time business intelligent reports to capture customer sentiment, and a Windows 10 workstation can be used to access all these services.
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References
N. Jindal, B. Liu, Opinion spam and analysis, in Proceedings of the 2008 International Conference on, Web Search and Data Mining, WSDM ’08 (ACM, New York, 2008), pp. 219–230
A. Mukherjee, B. Liu, N. Glance, Spotting fake reviewer groups in consumer reviews, in Proceedings of the 21st, International Conference on World Wide Web, WWW ’12 (ACM, New York, 2012), pp. 191–200
B. Pang, L. Lee, A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts, in Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, ACL ’04 (Association for Computational Linguistics, Stroudsburg, 2004)
P.D. Turney, Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews, in Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, ACL ’02 (Association for Computational Linguistics, Stroudsburg, 2002), pp. 417–424
C. Whitelaw, N. Garg, S. Argamon, Using appraisal groups for sentiment analysis, in Proceedings of the 14th ACM International Conference on Information and Knowledge Management, CIKM ’05 (ACM, New York, 2005), pp. 625–631
X. Fang, J. Zhan, Sentiment analysis using product review data. J. Big Data 2, 5 (2015)
S. Shayaa, N.I. Jaafar, S. Bahri, A. Sulaiman, P.S. Wai, Y.W. Chung, A.Z. Piprani, M.A. Al-Garadi, Sentiment analysis of big data: methods, applications, and open challenges. IEEE Access 6 (2018)
G. Xu, Y. Meng, X. Qiu, Z. Yu, X. Wu, Sentiment analysis of comment texts based on BiLSTM. IEEE Access 7 (2019)
J. Zhou, Y. Lu, H.-N. Dai, H. Wang, H. Xiao, Sentiment analysis of Chinese microblog based on stacked bidirectional LSTM. IEEE Access 7 (2019)
E. Cambria, Affective computing and sentiment analysis. IEEE Intell. Syst. 31 (2016)
C.W. Park, D.R. Seo, Sentiment analysis of Twitter corpus related to artificial intelligence assistants, in 2018 5th International Conference on Industrial Engineering and Applications (ICIEA). IEEE
C. Clavel, Z. Callejas, Sentiment analysis: from opinion mining to human-agent interaction. IEEE Trans. Affect. Comput. 7(1) (2016)
S.-M. Kim, E. Hovy, Determining the sentiment of opinions, in Proceedings of the 20th International Conference on Computational Linguistics. Association for Computational Linguistics, Stroudsburg, PA, USA (2004), p. 1367
B. Liu, Sentiment analysis and subjectivity, in Handbook of Natural Language Processing, 2nd edn. (Taylor and Francis Group, Boca Raton, 2010)
A. Pak, P. Paroubek, Twitter as a corpus for sentiment analysis and opinion mining, in Proceedings of the Seventh Conference on International Language Resources and Evaluation (European Languages Resources Association, Valletta, 2010)
A. Go, R. Bhayani, L. Huang, Twitter sentiment classification using distant supervision, 1–12. CS224N Project Report, Stanford (2009)
B. Liu, M. Hu, J. Cheng, Opinion observer: analyzing and comparing opinions on the web, in Proceedings of the 14th International Conference on World Wide Web, WWW ’05 (ACM, New York, 2005), pp. 342–351
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Manjula, P., Kumar, N., Al-Absi, A.A. (2021). Customer Sentiment Analysis Using Cloud App and Machine Learning Model. In: Pattnaik, P.K., Sain, M., Al-Absi, A.A., Kumar, P. (eds) Proceedings of International Conference on Smart Computing and Cyber Security. SMARTCYBER 2020. Lecture Notes in Networks and Systems, vol 149. Springer, Singapore. https://doi.org/10.1007/978-981-15-7990-5_32
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DOI: https://doi.org/10.1007/978-981-15-7990-5_32
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