Sarcasm is a way of expressing feelings in which people say or write something which is completely different or opposite to what they actually mean to say. Hence it is very difficult to identify sarcasm . It is usually an ironic or satirical remark tempered by humor. Mainly, people use it to say the opposite of what's true to make someone look or feel foolish. Understanding the sarcasm can improve the accuracy of sentiment analysis. Sentiment analysis (or opinion mining) is a natural language processing technique used to determine whether data is positive, negative or neutral. This helps in identifying what the opinions of users or individual or society are. In this project an attempt is made to develop a model to detect if a sentence is sarcastic or if it is not sarcastic. Keywords: Sarcasm detection, GloVe Embedding, LSTM, Natural Language Processing, Sentiment

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The spreading of fake news has given rise to many problems in society. It is due to its ability to cause a lot of social and national damage with destructive impacts. Sometimes it gets very difficult to know if the news is genuine or fake. Therefore it is very important to detect if the news is fake or not. "Fake News" is a term used to represent fabricated news or propaganda comprising misinformation communicated through traditional media channels like print, and television as well as nontraditional media channels like social media. Techniques of NLP and Machine learning can be used to create models which can help to detect fake news. In this paper we have presented six LSTM models using the techniques of NLP and ML. The datasets in comma-separated values format, pertaining to political domain were used in the project. The different attributes like the title and text of the news headline/article were used to perform the fake news detection. The results showed that the proposed solution performs well in terms of providing an output with good accuracy, precision and recall. The performance analysis made between all the models showed that the models which have used GloVe and Word2vec method work better than the models using TF-IDF. Further, a larger dataset for better output and also other factors such as the author ,publisher of the news can be used to determine the credibility of the news. Also, further research can also be done on images, videos, images containing text which can help in improving the models in future. Keywords: Fake news detection, LSTM(long short term memory),Word2Vec,TF-IDF,Natural Language Processing.