Sentiment Trend Analysis of Big Data
Nymphea Saraf Sandhu1, A. K. Upadhyay2, Sanjiv Sharma3

1Nymphea Saraf Sandhu, CSE, ASET, Amity University, Gwalior, M. P., India
2Dr. A. K. Upadhyay, CSE, ASET, Amity University, Gwalior, M. P., India.
3Dr. Sanjiv Sharma, CSE & IT, MITS, Gwalior, M. P., India.

Manuscript received on 02 July 2019 | Revised Manuscript received on 05 July 2019 | Manuscript published on 30 August 2019 | PP: 59-64 | Volume-8 Issue-10, August 2019 | Retrieval Number: I8631078919/2019©BEIESP | DOI: 10.35940/ijitee.I8631.0881019
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Abstract: Various fields like Text Mining, Linguistics, Decision Making and Natural Language Processing together form the basis for Opinion Mining or Sentiment Analysis. People share their feelings, observations and thoughts on social media, which has emerged as a powerful tool for rapidly growing enormous repository of real time discussions and thoughts shared by people. In this paper, we aim to decipher the current popular opinions or emotions from various sources, hence, contributing to sentiment analysis domain. Text from social media, blogs and product reviews are classified according to the sentiment they project. We re-examine the traditional processes of sentiment extraction, to incorporate the increase in complexity and number of the data sources and relevant topics, while re-populating the meaning of sentiment. Working across and within numerous streams of social media, expression of sentiment and classification of polarity is re-examined, thereby redefining and enhancing the realm of sentiment. Numerous social media streams are analyzed to build datasets that are topical for each stream and are later polarized according to their sentiment expression. In conclusion, defining a sentiment and developing tools for its analysis in real time of human idea exchange is the motive. 
Keywords: Sentiment Analysis, Big Data, Sentiment Polarity, Decision Support System.
Scope of the Article: Big Data