Hello all
I am doing some text mining on a set of five plain text files and have run into a snag when I run hclust in that there are just too many leaves for anything to be read. It returns a solid black line. The texts have been converted into a TDM which has a dim of 5,292 and 5 (as per 5 docs). My code for removing sparsity is as follows: > tdm2 <- removeSparseTerms(tdm, sparse=0.99999) > inspect(tdm2) <<TermDocumentMatrix (terms: 5292, documents: 5)>> Non-/sparse entries: 10415/16045 Sparsity : 61% Maximal term length: 22 Weighting : term frequency (tf) While the tf-idf weighting returns this when 0.99999 sparseness is removed: > inspect(tdm.tfidf) <<TermDocumentMatrix (terms: 5292, documents: 5)>> Non-/sparse entries: 7915/18545 Sparsity : 70% Maximal term length: 22 Weighting : term frequency - inverse document frequency (normalized) (tf-idf) I have experimented by decreasing the value I use for decreasing sparseness, and that helps a bit, for example: > tdm2 <- removeSparseTerms(tdm, sparse=0.215) > inspect(tdm2) <<TermDocumentMatrix (terms: 869, documents: 5)>> Non-/sparse entries: 3976/369 Sparsity : 8% Maximal term length: 14 Weighting : term frequency (tf) But, no matter what I do, the resulting plot is unreadable. The code for plotting the cluster is: > hc <- hclust(dist(tdm2, method = "euclidean"), method = "complete") > plot(hc, yaxt = 'n', main = "Hierarchical clustering") Can someone kindly either advise me what I am doing wrong and/ or signpost me to some detailed info on how to fix this. Many thanks in anticipation. Andy [[alternative HTML version deleted]] ______________________________________________ [hidden email] mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. |
I'm not familiar with these subjects.
And hopefully, someone who is, will offer some better suggestions. But to get things started, maybe... (1) What packages are you using (re: tdm)? (2) Where does the problem happen, in dist, hclust, the plot method for hclust, or in the package(s) you are using? (3) Do you think you could produce a small reproducible example, showing what is wrong, and explaining you would like it to do instead? Note that if the problem relates to hclust, or the plot method, then you should be able to produce a much simpler example. e.g. mycount.matrix <- matrix (rpois (25000, 20),, 5) head (mycount.matrix, 3) tail (mycount.matrix, 3) plot (hclust (dist (mycount.matrix) ) ) On Tue, Sep 15, 2020 at 6:54 AM Andrew <[hidden email]> wrote: > > Hello all > > I am doing some text mining on a set of five plain text files and have > run into a snag when I run hclust in that there are just too many leaves > for anything to be read. It returns a solid black line. > > The texts have been converted into a TDM which has a dim of 5,292 and 5 > (as per 5 docs). > > My code for removing sparsity is as follows: > > > tdm2 <- removeSparseTerms(tdm, sparse=0.99999) > > > inspect(tdm2) > > <<TermDocumentMatrix (terms: 5292, documents: 5)>> > Non-/sparse entries: 10415/16045 > Sparsity : 61% > Maximal term length: 22 > Weighting : term frequency (tf) > > While the tf-idf weighting returns this when 0.99999 sparseness is removed: > > > inspect(tdm.tfidf) > <<TermDocumentMatrix (terms: 5292, documents: 5)>> > Non-/sparse entries: 7915/18545 > Sparsity : 70% > Maximal term length: 22 > Weighting : term frequency - inverse document frequency > (normalized) (tf-idf) > > I have experimented by decreasing the value I use for decreasing > sparseness, and that helps a bit, for example: > > > tdm2 <- removeSparseTerms(tdm, sparse=0.215) > > inspect(tdm2) > <<TermDocumentMatrix (terms: 869, documents: 5)>> > Non-/sparse entries: 3976/369 > Sparsity : 8% > Maximal term length: 14 > Weighting : term frequency (tf) > > But, no matter what I do, the resulting plot is unreadable. The code for > plotting the cluster is: > > > hc <- hclust(dist(tdm2, method = "euclidean"), method = "complete") > > plot(hc, yaxt = 'n', main = "Hierarchical clustering") > > Can someone kindly either advise me what I am doing wrong and/ or > signpost me to some detailed info on how to fix this. > > Many thanks in anticipation. > > Andy > > > [[alternative HTML version deleted]] > > ______________________________________________ > [hidden email] mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. ______________________________________________ [hidden email] mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. |
Hi Abby
Many thanks for reaching out with an offer of help. Very much appreciated. (1) The packages I'm using are 'tm' for text-mining and the TDM and for the clustering it is 'cluster' (2) Not sure where the problem is happening as it doesn't show up as an error. Where it manifests is in the plotting, however logic would suggest that it concerns the removal of sparse terms, so that would be in the TDM process (3) I don't think I can provide a reproducible example. When I practice using data sets that packages provide, all is fine. The trouble is when I apply it to my own data sets which are five documents, etc., as described. I think the nub of it is really to find a way that I can subset the TDM to return the twenty or thirty most frequently used words, and then to plot those using hclust. However, when searching on-line I haven't been able to find any suggestions on how to do that, nor is there any mention of using that approach in the books and tutorials I have. If you (or someone on this list) can advise on how I can sort the terms in the TDM from most to least frequent, and then to subset the top twenty or thirty most frequently occurring terms (preferably using tf as well as tf-idf) and then I can plot that sub-set, then I think that that would do the trick, and the terms would be plotted clearly and legibly. Thanks again for your offer of help. I hope that my reply helps clarify rather than muddy the situation. Best wishes Andy On 17/09/2020 08:43, Abby Spurdle wrote: > I'm not familiar with these subjects. > And hopefully, someone who is, will offer some better suggestions. > > But to get things started, maybe... > (1) What packages are you using (re: tdm)? > (2) Where does the problem happen, in dist, hclust, the plot method > for hclust, or in the package(s) you are using? > (3) Do you think you could produce a small reproducible example, > showing what is wrong, and explaining you would like it to do instead? > > Note that if the problem relates to hclust, or the plot method, then > you should be able to produce a much simpler example. > e.g. > > mycount.matrix <- matrix (rpois (25000, 20),, 5) > head (mycount.matrix, 3) > tail (mycount.matrix, 3) > > plot (hclust (dist (mycount.matrix) ) ) > > On Tue, Sep 15, 2020 at 6:54 AM Andrew <[hidden email]> wrote: >> Hello all >> >> I am doing some text mining on a set of five plain text files and have >> run into a snag when I run hclust in that there are just too many leaves >> for anything to be read. It returns a solid black line. >> >> The texts have been converted into a TDM which has a dim of 5,292 and 5 >> (as per 5 docs). >> >> My code for removing sparsity is as follows: >> >> > tdm2 <- removeSparseTerms(tdm, sparse=0.99999) >> >> > inspect(tdm2) >> >> <<TermDocumentMatrix (terms: 5292, documents: 5)>> >> Non-/sparse entries: 10415/16045 >> Sparsity : 61% >> Maximal term length: 22 >> Weighting : term frequency (tf) >> >> While the tf-idf weighting returns this when 0.99999 sparseness is removed: >> >> > inspect(tdm.tfidf) >> <<TermDocumentMatrix (terms: 5292, documents: 5)>> >> Non-/sparse entries: 7915/18545 >> Sparsity : 70% >> Maximal term length: 22 >> Weighting : term frequency - inverse document frequency >> (normalized) (tf-idf) >> >> I have experimented by decreasing the value I use for decreasing >> sparseness, and that helps a bit, for example: >> >> > tdm2 <- removeSparseTerms(tdm, sparse=0.215) >> > inspect(tdm2) >> <<TermDocumentMatrix (terms: 869, documents: 5)>> >> Non-/sparse entries: 3976/369 >> Sparsity : 8% >> Maximal term length: 14 >> Weighting : term frequency (tf) >> >> But, no matter what I do, the resulting plot is unreadable. The code for >> plotting the cluster is: >> >> > hc <- hclust(dist(tdm2, method = "euclidean"), method = "complete") >> > plot(hc, yaxt = 'n', main = "Hierarchical clustering") >> >> Can someone kindly either advise me what I am doing wrong and/ or >> signpost me to some detailed info on how to fix this. >> >> Many thanks in anticipation. >> >> Andy >> >> >> [[alternative HTML version deleted]] >> >> ______________________________________________ >> [hidden email] mailing list -- To UNSUBSCRIBE and more, see >> https://stat.ethz.ch/mailman/listinfo/r-help >> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html >> and provide commented, minimal, self-contained, reproducible code. ______________________________________________ [hidden email] mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. |
Hi Andrew,
From your last email the answer to your problem may be the findFreqTerms() function. Just increase the number of times a term has to appear and check the result until you get the matrix size that you want. Jim On Fri, Sep 18, 2020 at 5:32 PM Andrew <[hidden email]> wrote: > > Hi Abby > > Many thanks for reaching out with an offer of help. Very much appreciated. > > (1) The packages I'm using are 'tm' for text-mining and the TDM and for > the clustering it is 'cluster' > (2) Not sure where the problem is happening as it doesn't show up as an > error. Where it manifests is in the plotting, however logic would > suggest that it concerns the removal of sparse terms, so that would be > in the TDM process > (3) I don't think I can provide a reproducible example. When I practice > using data sets that packages provide, all is fine. The trouble is when > I apply it to my own data sets which are five documents, etc., as described. > > I think the nub of it is really to find a way that I can subset the TDM > to return the twenty or thirty most frequently used words, and then to > plot those using hclust. However, when searching on-line I haven't been > able to find any suggestions on how to do that, nor is there any mention > of using that approach in the books and tutorials I have. > > If you (or someone on this list) can advise on how I can sort the terms > in the TDM from most to least frequent, and then to subset the top > twenty or thirty most frequently occurring terms (preferably using tf as > well as tf-idf) and then I can plot that sub-set, then I think that that > would do the trick, and the terms would be plotted clearly and legibly. > > Thanks again for your offer of help. I hope that my reply helps clarify > rather than muddy the situation. > > Best wishes > Andy > > > On 17/09/2020 08:43, Abby Spurdle wrote: > > I'm not familiar with these subjects. > > And hopefully, someone who is, will offer some better suggestions. > > > > But to get things started, maybe... > > (1) What packages are you using (re: tdm)? > > (2) Where does the problem happen, in dist, hclust, the plot method > > for hclust, or in the package(s) you are using? > > (3) Do you think you could produce a small reproducible example, > > showing what is wrong, and explaining you would like it to do instead? > > > > Note that if the problem relates to hclust, or the plot method, then > > you should be able to produce a much simpler example. > > e.g. > > > > mycount.matrix <- matrix (rpois (25000, 20),, 5) > > head (mycount.matrix, 3) > > tail (mycount.matrix, 3) > > > > plot (hclust (dist (mycount.matrix) ) ) > > > > On Tue, Sep 15, 2020 at 6:54 AM Andrew <[hidden email]> wrote: > >> Hello all > >> > >> I am doing some text mining on a set of five plain text files and have > >> run into a snag when I run hclust in that there are just too many leaves > >> for anything to be read. It returns a solid black line. > >> > >> The texts have been converted into a TDM which has a dim of 5,292 and 5 > >> (as per 5 docs). > >> > >> My code for removing sparsity is as follows: > >> > >> > tdm2 <- removeSparseTerms(tdm, sparse=0.99999) > >> > >> > inspect(tdm2) > >> > >> <<TermDocumentMatrix (terms: 5292, documents: 5)>> > >> Non-/sparse entries: 10415/16045 > >> Sparsity : 61% > >> Maximal term length: 22 > >> Weighting : term frequency (tf) > >> > >> While the tf-idf weighting returns this when 0.99999 sparseness is removed: > >> > >> > inspect(tdm.tfidf) > >> <<TermDocumentMatrix (terms: 5292, documents: 5)>> > >> Non-/sparse entries: 7915/18545 > >> Sparsity : 70% > >> Maximal term length: 22 > >> Weighting : term frequency - inverse document frequency > >> (normalized) (tf-idf) > >> > >> I have experimented by decreasing the value I use for decreasing > >> sparseness, and that helps a bit, for example: > >> > >> > tdm2 <- removeSparseTerms(tdm, sparse=0.215) > >> > inspect(tdm2) > >> <<TermDocumentMatrix (terms: 869, documents: 5)>> > >> Non-/sparse entries: 3976/369 > >> Sparsity : 8% > >> Maximal term length: 14 > >> Weighting : term frequency (tf) > >> > >> But, no matter what I do, the resulting plot is unreadable. The code for > >> plotting the cluster is: > >> > >> > hc <- hclust(dist(tdm2, method = "euclidean"), method = "complete") > >> > plot(hc, yaxt = 'n', main = "Hierarchical clustering") > >> > >> Can someone kindly either advise me what I am doing wrong and/ or > >> signpost me to some detailed info on how to fix this. > >> > >> Many thanks in anticipation. > >> > >> Andy > >> > >> > >> [[alternative HTML version deleted]] > >> > >> ______________________________________________ > >> [hidden email] mailing list -- To UNSUBSCRIBE and more, see > >> https://stat.ethz.ch/mailman/listinfo/r-help > >> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > >> and provide commented, minimal, self-contained, reproducible code. > > ______________________________________________ > [hidden email] mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. ______________________________________________ [hidden email] mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. |
Hello Jim
Thanks for that. I'll read up on it and will give it a go, either later today or tomorrow. I am assuming this will work for both tf and tf-idf weighted TDMs? Much appreciated. :-) Best wishes Andy On 18/09/2020 09:18, Jim Lemon wrote: > Hi Andrew, > >From your last email the answer to your problem may be the > findFreqTerms() function. Just increase the number of times a term has > to appear and check the result until you get the matrix size that you > want. > > Jim > > On Fri, Sep 18, 2020 at 5:32 PM Andrew <[hidden email]> wrote: >> Hi Abby >> >> Many thanks for reaching out with an offer of help. Very much appreciated. >> >> (1) The packages I'm using are 'tm' for text-mining and the TDM and for >> the clustering it is 'cluster' >> (2) Not sure where the problem is happening as it doesn't show up as an >> error. Where it manifests is in the plotting, however logic would >> suggest that it concerns the removal of sparse terms, so that would be >> in the TDM process >> (3) I don't think I can provide a reproducible example. When I practice >> using data sets that packages provide, all is fine. The trouble is when >> I apply it to my own data sets which are five documents, etc., as described. >> >> I think the nub of it is really to find a way that I can subset the TDM >> to return the twenty or thirty most frequently used words, and then to >> plot those using hclust. However, when searching on-line I haven't been >> able to find any suggestions on how to do that, nor is there any mention >> of using that approach in the books and tutorials I have. >> >> If you (or someone on this list) can advise on how I can sort the terms >> in the TDM from most to least frequent, and then to subset the top >> twenty or thirty most frequently occurring terms (preferably using tf as >> well as tf-idf) and then I can plot that sub-set, then I think that that >> would do the trick, and the terms would be plotted clearly and legibly. >> >> Thanks again for your offer of help. I hope that my reply helps clarify >> rather than muddy the situation. >> >> Best wishes >> Andy >> >> >> On 17/09/2020 08:43, Abby Spurdle wrote: >>> I'm not familiar with these subjects. >>> And hopefully, someone who is, will offer some better suggestions. >>> >>> But to get things started, maybe... >>> (1) What packages are you using (re: tdm)? >>> (2) Where does the problem happen, in dist, hclust, the plot method >>> for hclust, or in the package(s) you are using? >>> (3) Do you think you could produce a small reproducible example, >>> showing what is wrong, and explaining you would like it to do instead? >>> >>> Note that if the problem relates to hclust, or the plot method, then >>> you should be able to produce a much simpler example. >>> e.g. >>> >>> mycount.matrix <- matrix (rpois (25000, 20),, 5) >>> head (mycount.matrix, 3) >>> tail (mycount.matrix, 3) >>> >>> plot (hclust (dist (mycount.matrix) ) ) >>> >>> On Tue, Sep 15, 2020 at 6:54 AM Andrew <[hidden email]> wrote: >>>> Hello all >>>> >>>> I am doing some text mining on a set of five plain text files and have >>>> run into a snag when I run hclust in that there are just too many leaves >>>> for anything to be read. It returns a solid black line. >>>> >>>> The texts have been converted into a TDM which has a dim of 5,292 and 5 >>>> (as per 5 docs). >>>> >>>> My code for removing sparsity is as follows: >>>> >>>> > tdm2 <- removeSparseTerms(tdm, sparse=0.99999) >>>> >>>> > inspect(tdm2) >>>> >>>> <<TermDocumentMatrix (terms: 5292, documents: 5)>> >>>> Non-/sparse entries: 10415/16045 >>>> Sparsity : 61% >>>> Maximal term length: 22 >>>> Weighting : term frequency (tf) >>>> >>>> While the tf-idf weighting returns this when 0.99999 sparseness is removed: >>>> >>>> > inspect(tdm.tfidf) >>>> <<TermDocumentMatrix (terms: 5292, documents: 5)>> >>>> Non-/sparse entries: 7915/18545 >>>> Sparsity : 70% >>>> Maximal term length: 22 >>>> Weighting : term frequency - inverse document frequency >>>> (normalized) (tf-idf) >>>> >>>> I have experimented by decreasing the value I use for decreasing >>>> sparseness, and that helps a bit, for example: >>>> >>>> > tdm2 <- removeSparseTerms(tdm, sparse=0.215) >>>> > inspect(tdm2) >>>> <<TermDocumentMatrix (terms: 869, documents: 5)>> >>>> Non-/sparse entries: 3976/369 >>>> Sparsity : 8% >>>> Maximal term length: 14 >>>> Weighting : term frequency (tf) >>>> >>>> But, no matter what I do, the resulting plot is unreadable. The code for >>>> plotting the cluster is: >>>> >>>> > hc <- hclust(dist(tdm2, method = "euclidean"), method = "complete") >>>> > plot(hc, yaxt = 'n', main = "Hierarchical clustering") >>>> >>>> Can someone kindly either advise me what I am doing wrong and/ or >>>> signpost me to some detailed info on how to fix this. >>>> >>>> Many thanks in anticipation. >>>> >>>> Andy >>>> >>>> >>>> [[alternative HTML version deleted]] >>>> >>>> ______________________________________________ >>>> [hidden email] mailing list -- To UNSUBSCRIBE and more, see >>>> https://stat.ethz.ch/mailman/listinfo/r-help >>>> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html >>>> and provide commented, minimal, self-contained, reproducible code. >> ______________________________________________ >> [hidden email] mailing list -- To UNSUBSCRIBE and more, see >> https://stat.ethz.ch/mailman/listinfo/r-help >> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html >> and provide commented, minimal, self-contained, reproducible code. ______________________________________________ [hidden email] mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. |
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