Financial Tracker using NLP
NLP (Natural Language Processing) is a mechanism that helps computers to know natural languages like English. In general, computers can understand data, tables etc. which are well formed. But when it involves natural languages, it's unacceptable for computers to spot them. NLP helps to translate the tongue in such a fashion which will be easily processed by modern computers. Financial Tracker is an approach which will use NLP as a tool and can differentiate the user messages in various categories. the appliance of the approach will be seen at multiple levels. At a personal level, this permits users to filtrate useful financial messages from an large junk of text messages. On the opposite hand, from an industrial point of view, this can be useful in services like online loan disbursal, which are hitting the market nowadays. These services attempt to provide online loans to individuals in an exceedingly faster and quicker manner. But when it involves business view, loan recovery from customers becomes a really important & crucial aspect. As most such services can’t take strict legal actions against the fraud customers, it becomes a requirement that loan should be provided only to those customers who deserve it. At that time, this model can come under the image. As a business we will find the user’s messages from their inbox (after taking permission from the users). These messages are often filtered using NLP which might help to differentiate various types of messages within the user's inbox which might further be used as a content for further prediction and analysis on user’s behaviour in terms of cash related transactions.
Ingrid E. Fisher, Margaret R. Garnsey, & Mark E. Hughes. (2016). Natural language processing in accounting, auditing and finance: A synthesis of the literature with a roadmap for future research. DOI: 10.1002/isaf.1386.
Venera Arnaoudova, Sonia Haiduc, Andrian Marcus, & Giuliano Antoniol. (2015). The use of text retrieval and natural language processing in software engineering. DOI: 10.1145/2889160.2891053.
Tushar Ghorpade & Lata Ragha. (2012). Featured based sentiment classification for hotel reviews using NLP and Bayesian classification. DOI: 10.1109/ICCICT.2012.6398136.
Monisha Kanakaraj & Ram Mohana Reddy Guddeti. (2015). Performance analysis of Ensemble methods on Twitter sentiment analysis using NLP techniques. DOI: 10.1109/ICOSC.2015.7050801.
Shweta C. Dharmadhikari, Maya Ingle, & Parag Kulkarni. (2011). Empirical studies on machine learning based text classification algorithms. DOI: 10.5121/acij.2011.2615.
Gobinda G. Chowdhury. (2005). Natural language processing. DOI: 10.1002/aris.1440370103.
Erik Cambria & Bebo White. (2014). A review of natural language processing research. DOI: 10.1109/MCI.2014.2307227
Prakash M Nadkarni, Lucila Ohno-Machado, & Wendy W Chapman. (2011). Natural language processing: an introduction. DOI: 10.1136/amiajnl-2011-000464.
Rui Xia, Chengqing Zong, & Shoushan Li. (2011). Ensemble of feature sets and classification algorithms for sentiment classification. DOI: 10.1016/j.ins.2010.11.023.
Atefeh Farzindar & Diana Inkpen. (2015). Natural language processing for social media. DOI: 10.2200/S00659ED1V01Y201508HLT030.
E. Stamatatos, N. Fakotakis, & G. Kokkinakis. (2000). Text genre detection using common word frequencies. DOI: 10.3115/992730.992763.
Ellen Riloff & Wendy Lehnert. (1994). Information extraction as a basis for high-precision text classification. DOI: 10.1145/183422.183428.
Teresa Gonçalves & Paulo Quaresma. (2004). The impact of NLP techniques in the multilabel text classification problem. DOI: 10.1007/978-3-540-39985-8_46.
Andronicus A. Akinyelu, Aderemi & O. Adewumi. (2014). Classification of phishing email using random forest machine learning technique. DOI: 10.1155/2014/425731.
Olga Ormandjieva, Ishrar Hussain, & Leila Kosseim. (2007). Toward a text classification system for the quality assessment of software requirements written in natural language. DOI: 10.1145/1295074.1295082.
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