Can twitter analytics predict election outcome? An insight from 2017 Punjab assembly elections Abstract   Since the beginning of this decade, there has seen an exponential growth in number of internet users using social media, especially Twitter for sharing their views on various topics of common interest like sports, products, politics etc. Due to the active participation of large number of people on Twitter, huge amount of data (i.e. big data) is being generated, which can be put to use (after refining) to analyze real world problems. This paper takes into consideration the Twitter data related to the 2017 Punjab (a state of India) assembly elections and applies different social media analytic techniques on collected tweets to extract and unearth hidden but useful information. In addition to this, we have employed machine learning algorithm to perform polarity analysis and have proposed a new seat forecasting method to accurately predict the number of seats that a political party is likely to win in the elections. Our results confirmed that Indian National Congress was likely to emerge winner and that in fact was the outcome, when results got declared.

A BigData approach for sentiment analysis of twitter data using Naive Bayes and SVM Algorithm Data mining and sentiment analysis are two most versatile research areas in field of real time knowledge extraction. Real time twitter data analysis can plays very crucial role to observe the thinking and view point of people and users. Nowadays, social networking sites have become centric points to share your thoughts and viewpoints. Analysis of social networking data can help a lot to observe trend of society. It can also help to derive user interest and hidden activities. Sentiment analysis is the approach to determine whether piece of writing is positive, negative or neutral. It also help to derive user opinion and attitude of writer. Sentiment analysis of twitter user can help to track eventual view point of user. Country wise accumulative view point can help to derive overall opinion of country citizens and their thinking criteria. This work has proposed sentiment analysis model to observe positive and negative view point of different countries based on sentiment analysis approach. The complete work will be implemented using Hadoop Ecosystem to perform parallel processing on large data.