NLP Projects Using R

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How were these projects done?

• The tweets/ text data is imported first.
• The imported data is cleaned using the R function called gsub() which is used to replace all the matches of a pattern from a string. If the pattern is not found the string will be returned as it is.
• After cleaning the text data, the sentiment of each and every tweet is calculated using the R function called get_nrc_sentiment() which calculates the presence of eight different emotions and their corresponding valence in the tweets/ text data. Then, the tweets are concatenated with their corresponding emotion.
• Once the sentiments are calculated, I have found the most positive and negative tweets in the data set.
• A pie chart is generated to visually see the distribution and number of positive, negative and neutral tweets in the data set.
• After creating the pie chart, I have created a bar chart to visually classify the 8 different emotions (anger, fear, anticipation, trust, surprise, sadness, joy, and disgust) and two sentiments (negative and positive) present in our data set.
• After calculating the sentiments and emotions, I have create the the Term Document Matrix to count the occurrence of each word, to identify popular or trending topics using the R function called TermDocumentMatrix(). This step generates a table containing the frequency of words.
• Using the TermDocumentMatrix, I have created a Word Cloud which is one of the most popular ways to visualize and analyze qualitative data. It’s an image composed of keywords found within a body of text, where the size of each word indicates its frequency in that body of text. The word cloud is generated using the R function called WordCloud .

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Project 1: NLP & Text Mining on 2016 Indian Banknote Demonetisation

1.1 Sentiment Pie Chart:

1.2 Emotions Bar Chart:

1.3 Word Cloud:

1.4 Project 1 R Script

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Project 2: NLP & Text Mining on #IndiaWantsOxygen hashtag tweets.

2.1 Sentiment Pie Chart:

2.2 Emotions Bar Chart:

2.3 Word Cloud:

2.4 Project 2 R Script

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Project 3: NLP & Text Mining on Reddit Vaccine Myths.

r/VaccineMyths subreddit posts and comments

3.1 Sentiment Pie Chart:

3.2 Emotions Bar Chart:

3.3 Word Cloud:

3.4 Project 3 R Script

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Project 4: NLP & Text Mining on Pfizer Vaccine.

r/VaccineMyths subreddit posts and comments

4.1 Sentiment Pie Chart:

4.2 Word Cloud:

4.3 Project 4 R Script

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Project 5: NLP & Text Mining on Conspiracy Subreddit.

5.1 Sentiment Pie Chart:

5.2 Word Cloud:

5.3 Project 5 R Script

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Project 6: NLP & Text Mining on Human Right Violation.

6.1 Sentiment Pie Chart:

6.2 Word Cloud:

6.3 Project 6 R Script

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