• Mridul Mishra
• Jaydeep Viradiya

Sentiment Analysis using data-mining or Machine learning is widely used now a days by people and organizations to effectively understand the opinion of people on issues like elections, certain incidents, brands, products, their reaction to situations to gauge the outcome of events like elections, referendums, brand-value, product outcome, success of marketing strategies etc. However, human languages are very abstract in nature and a sentiment analysis model may fail to capture that, and understand that. In this study, we aim to study the results of contextually similar words on a simplistic sentiment analysis model on twitter data, and if there is some flaw in results how can sentence embeddings overcome that

Machine Learning, Sentence Embeddings, Sentiment Analysis;

"Impact of Sentence Embedding on Sentiment Analysis ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.210-212, June-2019, Available :http://www.jetir.org/papers/JETIR1906318.pdf

"Impact of Sentence Embedding on Sentiment Analysis ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp210-212, June-2019, Available at : http://www.jetir.org/papers/JETIR1906318.pdf