This_is_a_good_presentation_for_exam.pptx

Good

1. Thank you Manikant Here, we are showing sentiment analysis on the twitter data which we have collected by using the hashtags for the four campaign. Sentiment analysis is the NLP based technique which help us to know the sentiments of the person in the data like positive negative or neutral. Important thing about sentiment analysis, data should be clean. Means we are not considering number, special character, URL, hashtags, @mention and images also. We are not considering retweets also because it is not adding extra meaning in the data. With that we are eliminating duplicate tweets from the data as well. Right now, we are processing the tweets which are in English language. Now, for calculating the sentiment, we are using dictionary-based sentiment analysis, For implementing it we are using R/python language. In R, NRC library is present which gives a dictionary with sentiment score of the English words. On behalf of that, it gives a over all sentiment score to each tweet in the data. Here, in sentiment analysis, we are showing eight emotions with possitve and negative sentiments of the persons. In all the campaign, positive sentiment score is high as compared to the negative sentiment. It is showing that prople are showing faith on it. That’s why, trust factor is also high in all campaign. But with that people also showing anger, fear and sadness in the campaign. Parallelly, people are participating in the campaign because the anticipation bar is also having certain height in the analysis. Next slide please This is a descriptive analysis of the twitter data. For that we are using Word-cloud. Word cloud is visualization technique to the frequency of the word. Size of the word in the diagram is showing the frequency of the word. Big size of the text means high frequency of the word in the conversation on the twitter social media platform. Left side wordcloud is showing neg and pos words. Neg word hav red color with size of the text and pos word have green color with size of the text. In neg part, kill, shame and missing are high frequent word and in green portaion support and slient are high frequency word. It is showing fear in the person after that incident but parallelly they are giving support that campaign. Left side is the word cloud on complete data set and it is giving stress on Justice and palghar .