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.
In @cite_11 author finds correlation between Cricketers' performances and fans' emotions. Their work shows that fans' emotions depend on players' performance in the tournament. In @cite_0 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 @cite_13 research.
@cite_0 @cite_13 @cite_11
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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 ... Microblogging sites are the direct platform for the users to express their views. It has been observed from previous studies that people are viable to flaunt their emotions for events (eg. natural catastrophes, sports, academics etc.), for persons (actor actress, sports person, scientist) and for the places they visit. In this study we focused on a sport event, particularly the cricket tournament and collected the emotions of the fans for their favorite players using their tweets. Further, we acquired the stock market performance of the brands which are either endorsing the players or sponsoring the match in the tournament. It has been observed that performance of the player triggers the users to flourish their emotions over social media therefore, we observed correlation between players performance and fans' emotions. Therefore, we found the direct connection between player's performance with brand's behavior on stock market. 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. We present a contribution to the debate on the predictability of social events using big data analytics. We focus on the elimination of contestants in the American Idol TV shows as an example of a well defined electoral phenomenon that each week draws millions of votes in the USA. This event can be considered as basic test in a simplified environment to assess the predictive power of Twitter signals. We provide evidence that Twitter activity during the time span defined by the TV show airing and the voting period following it correlates with the contestants ranking and allows the anticipation of the voting outcome. Twitter data from the show and the voting period of the season finale have been analyzed to attempt the winner prediction ahead of the airing of the official result. We also show that the fraction of tweets that contain geolocation information allows us to map the fanbase of each contestant, both within the US and abroad, showing that strong regional polarizations occur. The geolocalized data are crucial for the correct prediction of the final outcome of the show, pointing out the importance of considering information beyond the aggregated Twitter signal. Although American Idol voting is just a minimal and simplified version of complex societ al phenomena such as political elections, this work shows that the volume of information available in online systems permits the real time gathering of quantitative indicators that may be able to anticipate the future unfolding of opinion formation events.