You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
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.

Cited Abstract(s):
[1]: AbstractBrexit i.e. “British Exit” is one of the major events in the history of economics and British politics. When EU referendum took place, 52 of votes were in favor of United Kingdom leaving the European Union. This was a major event affecting the overall economy of Britain and was also one of the most talked about events. Twitter is a social network platform where users from all over the world discussed a lot about Brexit and British politics. Analysis of such user generated text can reveal a lot about the event and what people think of it. Sentiment analysis is a trending technique to get insights from any written text. Our aim in this study is to analyze the tweets geospatially and then perform Geo-spatial Sentiment analysis based on the location of the events verses the geospatial tweets distribution for that particular event on global level. Also shows the keywords and hashtags were mostly used by people during that time and how they were being used i.e. which hashtag was used positively, which ...

[2]: 

[3]: Microblogging sites are now becoming more popular among the peoples as it provides a direct platform for the users to flatus their views on any topic. It has been observed from the previous studies that Social networking platforms, Microblogs are widely used by users to show their emotions, sentiment for any events like Natural Disasters, Earthquake, Sports etc and the places they visit. In this paper, we are focusing on a sports event that is Cricket tournament. For that particular, we collected the tweets over Cricket tournament and shows how fans sentiments varies over the period of time. We have established that the fan emotions depends on the performance of cricket players. In this study, we have used the Lexicon based approached for calculating the sentiment of fans over the time.
In [3] author finds correlation between Cricketers' performances and fans' emotions. Their work shows that fans' emotions depend on players' performance in the tournament. In [1] the author performs the Geo-spatial sentiment analysis for the UK-EU referendum over the Twitter data. It analyses the data to find out the most talked about British politicians and the public sentiments for them. We have used the same approach to remove the noise from tweets as discussed in [2] research.