This paper presents a method for improving target-dependent Twitter sentiment classification by incorporating target-dependent features and considering related tweets. Target-dependent sentiment classification involves determining the sentiment of a tweet towards a specific query, such as whether it is positive, negative, or neutral about that query. Traditional approaches often use target-independent strategies, which may misclassify sentiments for the given target. Additionally, these approaches typically ignore the context of the tweets, such as related tweets, which can be crucial for accurate sentiment classification. The authors propose a three-step approach: first, classifying the tweet as subjective or neutral about the target; second, classifying the sentiment as positive or negative if it is subjective; and third, using graph-based optimization to improve performance by considering related tweets. The first two steps use binary SVM classifiers, while the third step leverages graph-based methods to incorporate context. To enhance target-dependent classification, the authors introduce extended targets, which are related entities that can be inferred from the context of the target. These extended targets are identified through methods such as co-reference resolution, association based on PMI, and extracting head nouns. These extended targets help in more accurately determining the sentiment towards the original target. The authors also incorporate context-aware approaches by considering related tweets, such as retweets, tweets from the same person, and replies. These related tweets provide additional information that can improve the accuracy of sentiment classification, especially for short and ambiguous tweets. The experiments show that incorporating target-dependent features and context significantly improves the performance of target-dependent sentiment classification. The results indicate that the proposed methods outperform previous approaches, particularly in reducing errors caused by incorrect target associations. The graph-based optimization further enhances the performance by leveraging the relationships between tweets. Overall, the study highlights the importance of considering both the content and context of tweets for accurate sentiment analysis.This paper presents a method for improving target-dependent Twitter sentiment classification by incorporating target-dependent features and considering related tweets. Target-dependent sentiment classification involves determining the sentiment of a tweet towards a specific query, such as whether it is positive, negative, or neutral about that query. Traditional approaches often use target-independent strategies, which may misclassify sentiments for the given target. Additionally, these approaches typically ignore the context of the tweets, such as related tweets, which can be crucial for accurate sentiment classification. The authors propose a three-step approach: first, classifying the tweet as subjective or neutral about the target; second, classifying the sentiment as positive or negative if it is subjective; and third, using graph-based optimization to improve performance by considering related tweets. The first two steps use binary SVM classifiers, while the third step leverages graph-based methods to incorporate context. To enhance target-dependent classification, the authors introduce extended targets, which are related entities that can be inferred from the context of the target. These extended targets are identified through methods such as co-reference resolution, association based on PMI, and extracting head nouns. These extended targets help in more accurately determining the sentiment towards the original target. The authors also incorporate context-aware approaches by considering related tweets, such as retweets, tweets from the same person, and replies. These related tweets provide additional information that can improve the accuracy of sentiment classification, especially for short and ambiguous tweets. The experiments show that incorporating target-dependent features and context significantly improves the performance of target-dependent sentiment classification. The results indicate that the proposed methods outperform previous approaches, particularly in reducing errors caused by incorrect target associations. The graph-based optimization further enhances the performance by leveraging the relationships between tweets. Overall, the study highlights the importance of considering both the content and context of tweets for accurate sentiment analysis.