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This a chat-respond program and the problem I am currently facing is that I try to make it exit if the input answer does not match one of my answers, but since all the answers of the three of the questions were put together as Response so even the right for the question it randomly chose was inputted, python still thinks it is not right because it does not meet the other two and then it will exit. Please tell me how can I take a different approach on this programs.Thanks for the help1

I have a large dataframe consisting of tweets, and keyword dictionaries loaded as values that have words associated with morality (kw_Moral) and emotion (kw_Emo). In the past I have used the keyword dictionaries to subset a dataframe to get only the tweets that have one or more of the keywords present.

For example, to create a subset with only those tweets that have emotional keywords, I loaded in my keyword dictionary...

I have a large dataframe consisting of tweets, and a keyword dictionary loaded as a list that has words and word stems associated with emotion (kw_Emo). I need to find a way to count how many times any given word/word stem from kw_Emo is present each tweet. In kw_Emo, word stems are marked with an asterisk ( * ). For example, one word stem is ador*, meaning that I need to account for the presence of adorable, adore, adoring, or any pattern of letters that starts with ador….

From a previous Stack Overflow discussion (see previous question on my profile), I was greatly helped with the following solution, but it only counts exact character matches (Ex. only ador, not adorable):

1. Load relevant package.

   library(stringr)

2. Identify and remove the * from word stems in kw_Emo.

   for (x in 1:length(kw_Emo)) { if (grepl("[*]", kw_Emo[x]) == TRUE) { kw_Emo[x] <- substr(kw_Emo[x],1,nchar(kw_Emo[x])-1) } }

3. Create new columns, one for each word/word stem from kw_Emo, with default value 0.

   for (x in 1:length(keywords)) { dataframe[, keywords[x]] <- 0}

4. Split each Tweet to a vector of words, see if the keyword is equal to any, add +1 to the appropriate word/word stems' column.

   for (x in 1:nrow(dataframe)) { partials <- data.frame(str_split(dataframe[x,2], " "), stringsAsFactors=FALSE) partials <- partials[partials[] != ""] for(y in 1:length(partials)) { for (z in 1:length(keywords)) { if (keywords[z] == partials[y]) { dataframe[x, keywords[z]] <- dataframe[x, keywords[z]] + 1 } } } }

Is there a way to alter this solution to account for word stems? I'm wondering if it's possible to first use a stringr pattern to replace occurrences of a word stem with the exact characters, and then use this exact match solution. For instance, something like stringr::str_replace_all(x, "ador[a-z]+", "ador"). But I'm unsure how to do this with my large dictionary and numerous word stems. Maybe the loop removing [*], which essentially identifies all word stems, can be adapted somehow?

Here is a reproducible sample of my dataframe, called TestTweets with the text to be analysed in a column called clean_text:

dput(droplevels(head(TestTweets, 20)))

I tried to make it only ask "do you want to continue" 3 times but it doesn't seem to work, it just kept on running. How do I fix this? It is a chat-response program which the computer askes one question and the user response.

I have some functions:

How do I make it choose a different question each time?

I am making this chat-respond program, my goal is for the computer to pick a question out of the database randomly and able to respond base on the input answer. There are no error notices but the problem is that the first question it asks does not return an answer and it asks all the question on the list, not just one. How do I fix this? Thanks for the help!