Text Mining essay

Conduct sentiment analysis on MLK’s speech to determine how positive/negative his speech was. Split his speech into four quartiles to see how that sentiment changes over time.Create two bar charts to display your results. Find two files (one for positive words and one for negative words) from the UIC website. These files are about halfway down the page listed as “A list of English positive and negative opinion words or sentiment words”. Use the link below: Save these files in your “data” folder. Create two vectors of words one for the positive words and one for the negative words. Note that when reading in the word files there might be lines at the start and/or the end that will need to be removed (i.e. you should clean your dataset). Find MLK’s speech on the AnalyticTech website. Use the link below: Read in the file using the XML package. Otherwise cut and paste the document into a .txt file. If you parse the html file using the XML package the following code might help: Create a term matrix. Create a list of counts for each word. Determine how many positive words were in the speech. Scale the number based on the total number of words in the speech. Hint: One way to do this is to use match() and then which(). Determine how many negative words were in the speech. Scale the number based on the total number of words in the speech.Hint: This is basically the same as Step 3. Redo the “positive” and “negative” calculations for each 25% of the speech by following the steps below. Compare the results (e.g. a simple bar chart of the 4 numbers).For each quarter of the text you calculate the positive and negative ratio as was done in Step 4 and Step 5.The only extra work is to split the text to four equal parts then visualize the positive and negative ratios by plotting. The final graphs should look like below: HINT: The code below shows how to start the first 25% of the speech. Finish the analysis and use the same approach for the rest of the speech. What do you see from the positive/negative ratio in the graph? State what you learned from the MLK speech using the sentiment analysis results: -example from thee professor————— Chapter 15: Happy Words? —————— pos <- “positive-words.txt” neg <- “negative-words.txt” p <- scan(pos character(0)sep = “n”) n <- scan(neg character(0)sep = “n”) head(p 50) head(n 50) p <- p[-1:-29] n <- n[-1:-30] head(p 10) head(n 10) totalWords <- sum(wordCounts) words <- names(wordCounts) matched <- match(words p nomatch = 0) head(matched10) matched[9] p[1083] words[9] mCounts <- wordCounts[which(matched != 0)] length(mCounts) mWords <- names(mCounts) nPos <- sum(mCounts) nPos matched <- match(words n nomatch = 0) nCounts <- wordCounts[which(matched != 0)] nNeg <- sum(nCounts) nWords <- names(nCounts) nNeg length(nCounts) totalWords <- length(words) ratioPos <- nPos/totalWords ratioPos ratioNeg <- nNeg/totalWords Requirements: short answers | .doc file http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html http://www.analytictech.com/mb021/mlk.htm ‘