Sentiment analysis is indeed a difficult task owing to the playful language mannerism, altered vocabulary and speak-text used on online forums. Humans tend to use words and phrases in ways that are incomprehensible to those who are not involved in the discourse. Sarcastic remarks in conversations are often utilized to mock others by saying something that isn't pleasant. Sardonic or humorous statements/ tones are used to insult or make others appear puerile. Automated sarcasm detection is considered as one of the key tasks to tweak sentiment analysis and extending it to a morphologically rich and free-order dominant indigenous Indian language Hindi is another challenge. This research puts forward ‘The Amalgam Neural Architecture’, TANA, to detect sarcasm in Hindi tweets. The architecture is trained using two embeddings, namely word and emoji embeddings and combines an LSTM with the loss function of SVM for sarcasm detection. We use the Sarc-H dataset, which is built by scrapping Hindi language tweets and manually annotating based on the hashtags ‘

由于在线论坛上使用有趣的语言习惯、改变的词汇和说话文本，情感分析确实是一项艰巨的任务。人类倾向于以不参与讨论的人无法理解的方式使用单词和短语。谈话中的讽刺性言论经常被用来通过说一些不愉快的话来嘲笑别人。讽刺或幽默的陈述/语气用于侮辱或使他人显得幼稚。自动讽刺检测被认为是调整情感分析的关键任务之一，并将其​​扩展到形态丰富且自由顺序的占主导地位的印度土著语言印地语是另一个挑战。本研究提出了' T he A malgam N eural Architecture'，TANA，用于检测印地语推文中的讽刺。该架构使用两个嵌入进行训练，即单词和表情符号嵌入，并将 LSTM 与 SVM 的损失函数相结合以进行讽刺检测。我们使用 Sarc-H 数据集，该数据集是通过删除印地语推文并根据主题标签手动注释而构建的