People might not be close-at-hand but they still are - by virtue of the social network. The social network has transformed lives in many ways. People can express their views, opinions and life experiences on various platforms be it Twitter, Facebook or any other medium there is. Such events constitute of reviewing a product or service, conveying views on political banters, predicting share prices or giving feedback on the government policies like Demonetization or GST. These social platforms can be used to investigate the insights of the emotional curve that the general public is generating. This kind of analysis can help make a product better, predict the future prospects and also to implement the public policies in a better way. Such kind of research on sentiment analysis is increasing rapidly. In this research paper, we have performed temporal analysis and spatial analysis on 1,42,508 and 58,613 tweets respectively and these tweets were posted during the post-GST implementation period from July 04, 2017 to July 25, 2017. The tweets were collected using the Twitter streaming API. A well-known lexicon, National Research Council Canada (NRC) emotion Lexicon is used for opinion mining that exhibits a blend of eight basic emotions i.e. joy, trust, anticipation, surprise, fear sadness, anger, disgust and two sentiments i.e. positive and negative for 6,554 words.
@cite_9: 
@cite_15: In this paper, we present a case study on sentiment and social network analysis of opinions on Twitter. We have used Twitter data as a corpus to find opinions about GST (Good and Service Tax) among Indians applied from July 1st, 2017. Tweets, retweets and mentions are collected to extract the opinions of Indian citizens in the form of positive and negative opinions. In order to find out how users, who posted about GST are connected to each other we have generated a directed network based on the data collected, where users are the node of the graph and there exists an edge between them if they have mentioned replies or retweeted the posts. The network of users tweeted about GST form a small world network.
@cite_14: In this modern era of communication, people are always connected to the internet. Hence, everyone tends to express their opinions on social media or e-commerce websites about commercial products, movies, sports, social and geopolitical matters and even on government policies. These opinions reflect the corresponding person's view or sentiment about that particular matter, which ultimately leads to the formation of opinion polarity regarding a specific issue. In today's context, Twitter, Facebook or other social platforms often witness a lot of opinion waves regarding some of the today's most hot topics, and one of them surely is the introduction of Goods and Services Tax or GST in India. The rising sentiment analysis and opinion mining regarding this issue is helping researchers to understand the insight of public emotion. It can be also implemented to gain an idea of allover opinion polarity of people in this matter. GST was one of the most rending topic on social network platforms during Jun-July 2017, so in this paper, we present a simple and robust work to gather, analyze and graphically represent people's opinion about India's new taxation system using Naive Bayes algorithm.
Reserach @cite_14 uses Naive Bayes Algorithm to analyse public sentiments on approximately 20000 tweets collected from 27th June 2017 to 07th July 2017. The analysis shows that approximately 52 In @cite_15 @cite_9 a technique is used to process and analyse a huge amount of real time data using Hadoop cluster and Naive Bayes approach. The paper focuses on the speed of the sentiment analysis rather than accuracy of the analysis. It processes the data by removing stop words, converting unstructured data to structured data and replacing emotions to their corresponding words.