This paper introduces a novel method for sentiment analysis using subjectivity summarization based on minimum cuts. The approach involves identifying subjective portions of a text and applying a machine-learning classifier to these portions to determine sentiment polarity. The key innovation is the use of graph-based minimum cut techniques to efficiently incorporate cross-sentence contextual constraints, which helps in extracting more accurate and compact summaries of sentiment information. The method first labels sentences as subjective or objective, discarding the latter. It then applies a standard machine-learning classifier to the resulting extract, which helps in focusing on the relevant parts of the text. This approach is more effective than traditional text-categorization techniques because it avoids considering irrelevant or potentially misleading text. The paper explores the use of minimum cut formulations for subjectivity detection, which allows for the integration of inter-sentence-level contextual information with traditional bag-of-words features. This method is efficient and intuitive, enabling the incorporation of proximity information between sentences. The experiments show that subjectivity extracts created using this method accurately represent the sentiment information of the original documents in a much more compact form. For example, using a Naive Bayes classifier, the accuracy improved from 82.8% to 86.4% when using subjectivity extracts that contained only 60% of the original text. The results also indicate that these extracts are more effective than standard summarization methods like first- or last-N sentences. The study also demonstrates that incorporating contextual information, such as sentence proximity, further improves the accuracy of sentiment analysis. The use of graph-based minimum cut techniques allows for the effective integration of contextual constraints, leading to statistically significant improvements in polarity classification accuracy. In conclusion, the paper shows that subjectivity detection can compress reviews into shorter extracts that retain sentiment information effectively. The minimum-cut framework provides an efficient and effective means for sentiment analysis, enabling the development of more accurate and compact summaries of text sentiment.This paper introduces a novel method for sentiment analysis using subjectivity summarization based on minimum cuts. The approach involves identifying subjective portions of a text and applying a machine-learning classifier to these portions to determine sentiment polarity. The key innovation is the use of graph-based minimum cut techniques to efficiently incorporate cross-sentence contextual constraints, which helps in extracting more accurate and compact summaries of sentiment information. The method first labels sentences as subjective or objective, discarding the latter. It then applies a standard machine-learning classifier to the resulting extract, which helps in focusing on the relevant parts of the text. This approach is more effective than traditional text-categorization techniques because it avoids considering irrelevant or potentially misleading text. The paper explores the use of minimum cut formulations for subjectivity detection, which allows for the integration of inter-sentence-level contextual information with traditional bag-of-words features. This method is efficient and intuitive, enabling the incorporation of proximity information between sentences. The experiments show that subjectivity extracts created using this method accurately represent the sentiment information of the original documents in a much more compact form. For example, using a Naive Bayes classifier, the accuracy improved from 82.8% to 86.4% when using subjectivity extracts that contained only 60% of the original text. The results also indicate that these extracts are more effective than standard summarization methods like first- or last-N sentences. The study also demonstrates that incorporating contextual information, such as sentence proximity, further improves the accuracy of sentiment analysis. The use of graph-based minimum cut techniques allows for the effective integration of contextual constraints, leading to statistically significant improvements in polarity classification accuracy. In conclusion, the paper shows that subjectivity detection can compress reviews into shorter extracts that retain sentiment information effectively. The minimum-cut framework provides an efficient and effective means for sentiment analysis, enabling the development of more accurate and compact summaries of text sentiment.