USING DIFFERENT SEARCHING SCHEMAS FOR FUZZY KEYWORD SEARCH OVER CLOUD DATA

Cloud Computing is a construct that allows you to access applications that actually reside at a location other than our computer or other internet-connected devices, Cloud computing uses internet and central remote servers to maintain data and applications, the data is stored in off-premises and accessing this data through keyword search. So there comes the importance of encrypted cloud data search Traditional keyword search was based on plaintext keyword search, but for protecting data privacy the sensitive data should be encrypted before outsourcing. Fuzzy keyword search greatly enhances system usability by returning the matching files; Fuzzy technique uses approximate full text search and retrieval. Three different Fuzzy Search Schemas, The wild card method, gram based method and tree traverse search scheme, are dicussed and also the efficiency of these algorithms is analyzed

Sentiment Analysis of Financial News with Supervised Learning

Financial data in banks are unstructured and complicated. It is challenging to analyze these texts manually due to the small amount of labeled training data in financial text. Moreover, the financial text consists of language in the economic domain where a general-purpose model is not efficient. In this thesis, data had collected from MFN (Modular Finance) financial news, this data is scraped and persisted in the database and price indices are collected from Bloomberg terminal. Comprehensive study and tests are conducted to find the state-of-art results for classifying the sentiments using traditional classifiers like Naive Bayes and transfer learning models like BERT and FinBERT. FinBERT outperform the Naive Bayes and BERT classifier. The time-series indices for sentiments are built, and their correlations with price indices calculated using Pearson correlation. Augmented Dickey-Fuller (ADF) is used to check if both the time series data are stationary. Finally, the statistical hypothesis Granger causality test determines if the sentiment time series helps predict price. This result shows that there is a significant correlation and causal relation between sentiments and price.

Sentiment Analysis of Financial News with Supervised Learning

Financial data in banks are unstructured and complicated. It is challenging to analyze these texts manually due to the small amount of labeled training data in financial text. Moreover, the financial text consists of language in the economic domain where a general-purpose model is not efficient. In this thesis, data had collected from MFN (Modular Finance) financial news, this data is scraped and persisted in the database and price indices are collected from Bloomberg terminal. Comprehensive study and tests are conducted to find the state-of-art results for classifying the sentiments using traditional classifiers like Naive Bayes and transfer learning models like BERT and FinBERT. FinBERT outperform the Naive Bayes and BERT classifier. The time-series indices for sentiments are built, and their correlations with price indices calculated using Pearson correlation. Augmented Dickey-Fuller (ADF) is used to check if both the time series data are stationary. Finally, the statistical hypothesis Granger causality test determines if the sentiment time series helps predict price. This result shows that there is a significant correlation and causal relation between sentiments and price.