Depression has been a significant global concern for a long time. It affects many people's daily lives, influencing their moods, eating habits, and social interactions. Early detection of depression is challenging due to the stigma of mental illness and a lack of awareness in the field of mental health. However, people worldwide express their feelings openly on social media, particularly Twitter. Twitter allows users to share emotions through short texts, images, or videos, often treating their accounts as diaries due to the platform's anonymity. This study aims at using TextBlob, VADER, and SentiWordNet analyzer with depression-related tweets, to analyze emotional responses to depression. The seed terms from PHQ-9 were used to collect 71,000 tweets from Twitter. The tweets were preprocessed with a Twitter tokenizer, while TextBlob, VADER, and SentiWordNet were used for text mining and sentiment analysis, respectively. The TextBlob analysis showed 31.9% positive, 22.8% negative, and 45.3% neutral sentiments. Conversely, the VADER analyzer revealed 29.4% positive, 38.4% negative, and 32.2% neutral sentiments, indicating a higher prevalence of negativity. The SentiWordNet analysis indicated 32.7% positive, 35.9% negative, and 31.4% neutral sentiments, emphasizing the prevalence of negative opinions. VADER outperforms TextBlob and SentiWordNet in capturing negative sentiment associated with depression, providing insights into emotions and attitudes expressed in depression-related tweets, highlighting the importance of addressing mental health issues.