Using sentiment analysis technology to analyze bank customers' textual comments

Profiling bank customers behaviour using cluster analysis for profitability

Analyzing information of bank's customers for Customer Behavior Modeling is a difficult problem since it is a multi dimensional problem. This study has been done in a bank at the analysis of customer’s behavior that isn’t limited to customer’s benefit in their general uses of banking services and also is considered as channel’s charges. In this study, used of k-means for customer segmentation a...

Deeper Sentiment Analysis Using Machine Translation Technology

This paper proposes a new paradigm for sentiment analysis: translation from text documents to a set of sentiment units. The techniques of deep language analysis for machine translation are applicable also to this kind of text mining task. We developed a high-precision sentiment analysis system at a low development cost, by making use of an existing transfer-based machine translation engine.

Sentiment Analysis of Movie Review Comments

This paper presents an empirical study of efficacy of machine learning techniques in classifying text messages by semantic meaning. We use movie review comments from popular social network Digg as our data set and classify text by subjectivity/objectivity and negative/positive attitude. We propose different approaches in extracting text features such as bag-of-words model, using large movie rev...

Textual sentiment summarization

Naive Bayes The multinomial Naive Bayes model on a dictionary is a familiar option for text classification, e.g. (Gale, Church, & Yarowski 1992), (McCallum & Nigam 1998). When there are additional features, the Naive Bayes model has also a natural extension: We simply assume that each additional feature is independent of all the others, conditional upon . In this case, we invert Bayes’ Law by o...

A Sentiment Analysis Model to Analyze Students Reviews of Teacher Performance Using Support Vector Machines