Emotion artificial intelligence is a field of study that focuses on figuring out how to recognize emotions, especially in the area of text mining. Today is the age of social media which has opened a door for us to share our individual expressions, emotions, and perspectives on any event. We can analyze sentiment on social media posts to detect positive, negative, or emotional behavior toward society. One of the key challenges in sentiment analysis is to identify depressed text from social media text that is a root cause of mental ill-health. Furthermore, depression leads to severe impairment in day-to-day living and is a major source of suicide incidents. In this paper, we apply natural language processing techniques on Facebook texts for conducting emotion analysis focusing on depression using multiple machine learning algorithms. Preprocessing steps like stemming, stop word removal, etc. are used to clean the collected data, and feature extraction techniques like stylometric feature, TF-IDF, word embedding, etc. are applied to the collected dataset which consists of 983 texts collected from social media posts. In the process of class prediction, LSTM, GRU, support vector machine, and Naive-Bayes classifiers have been used. We have presented the results using the primary classification metrics including F1-score, and accuracy. This work focuses on depression detection from social media posts to help psychologists to analyze sentiment from shared posts which may reduce the undesirable behaviors of depressed individuals through diagnosis and treatment.

翻译：情感人工智能是一个专注于探索如何识别情感的研究领域，尤其在文本挖掘方面。当今是社交媒体时代，它为我们分享个人对任何事件的表达、情感和观点打开了一扇门。我们可以分析社交媒体帖子的情感，以检测对社会的积极、消极或情绪化行为。情感分析的一个关键挑战是从社交媒体文本中识别出抑郁文本，这是心理健康问题的根源之一。此外，抑郁会导致日常生活严重受损，并且是自杀事件的主要诱因。在本文中，我们应用自然语言处理技术对Facebook文本进行情感分析，重点关注抑郁问题，并使用了多种机器学习算法。我们采用了词干提取、停用词去除等预处理步骤来清理收集的数据，并对收集的数据集应用了文体特征、TF-IDF、词嵌入等特征提取技术。该数据集包含从社交媒体帖子中收集的983条文本。在类别预测过程中，我们使用了LSTM、GRU、支持向量机和朴素贝叶斯分类器。我们使用F1分数和准确率等主要分类指标呈现了结果。这项工作专注于从社交媒体帖子中检测抑郁，以帮助心理学家分析分享帖子中的情感，从而可能通过诊断和治疗减少抑郁个体的不良行为。