In this paper, we have developed a preprocessing and Sentiment analysis (SA) tool (PSAT) that will be used to analyze the sentiments of peoples, cricket audience, former cricketers, and other sports personality on abandoned tour of Pakistan by New Zealand reasoning security concerns. This paper focuses on data cleaning and analyzing the sentiments from textual data. The data is collected from different social websites, Facebook, and tweeter. In which the user can choose a topic and specify their preferences. The model uses recent linked tweets to detect the polarity (negative, positive, both and neutral) of the issue and displays the findings. Around 3000 Arabic tweets were randomly selected and evenly labelled to train the programmed. In this research, we offer a novel technique that uses a combination of parameters to apply sentiment analysis of cricket text tweets and comments. Those parameters are (1) the time of the tweets, (2) preprocessing methods like stemming and retweets, (3) removing whitespaces, (4) Capitalizing. The PSAT tool combined with Naive Bayes classifier a group of classification algorithms based on Bayes’ Theorem. The Accuracy PSAT tool is 75% approx. and F1 Score is 69%. According to our experiment, The Naive Bayes machine learning approach is the most accurate at predicting topic polarity. The tool is excellent for intermediate and advanced users, and it can assist them in determining the ideal parameter combinations for sentiment analysis