There are many factors that can contribute to corrosion in the pipeline. Therefore, it is important for decision makers to analyze and identify the main factor of corrosion in order to take appropriate actions. The factor of corrosion can be analyzed using data mining based on historical datasets collected from monitoring sensors. The purpose of this study is to analyze the trends of corroding agents for pipeline corrosion based on symbolic representation of time series corrosion dataset using Symbolic Aggregation Approximation (SAX). The paper presents the analysis and evaluation of the patterns using Ngram model. Text mining using N-gram model is proposed to mine trend changes from corrosion time series dataset that are transformed as symbolic representation. N-gram was applied for the analysis in order to find significant symbolic patterns that are represented as text. Pattern analysis is performed and the results are discussed according to each environmental factor of pipeline corrosion. © 2018 International Journal of Advanced Computer Science and Applications.