  Checking and confirming factual information in texts and speeches is vital to
determine the veracity and correctness of the factual statements. This work was
previously done by journalists and other manual means but it is a
time-consuming task. With the advancements in Information Retrieval and NLP,
research in the area of Fact-checking is getting attention for automating it.
CLEF-2018 and 2019 organised tasks related to Fact-checking and invited
participants. This project focuses on CLEF-2019 Task-1 Check-Worthiness and
experiments using the latest Sentence-BERT pre-trained embeddings, topic
Modeling and sentiment score are performed. Evaluation metrics such as MAP,
Mean Reciprocal Rank, Mean R-Precision and Mean Precision@N present the
improvement in the results using the techniques.
