Finally I’ve implemented a Naive Bayes Classifier for Sentiment analysis and
works quite good, but I’ve few questions.
The training phase creates a .bin file that is the model of the classifier,
I’ve tried to read but I can’t.
What does the .bin file represent?
I’m asking this because I’d like to know better how the classifier works,
where I can read something about its implementation?
Thank in advance, your help was irrepleaceble to create my classifier.
On 29 Jul 2014, at 18:40, vaibhav srivastava <vaibhavcs...@gmail.com> wrote:
> Hi Filipponi,
> This case testnb will not work. As in the end part of it code its takes
> label to print the confusion matrix.
>
> if you want to use your Model to predict what are the possible out come,
> you have to use the class "TestNaiveBayesDriver.java" to write that.
>
> and comment this section /*if (bestIdx != Integer.MIN_VALUE) {
> ClassifierResult classifierResult = new
> ClassifierResult(labelMap.get(bestIdx), bestScore);
> analyzer.addInstance(pair.getFirst().toString(), classifierResult);
> }
> */
> that case the output file of BayesTestMapper is the going to store values
> for you if you can use seqdumper you can get the values for key
> "471685156584292353".
> or suppose
>
> Key: /471685156584292353/ Value:/471685156584292353/:{1:
> 0.19424138174284086,24:0.19424138174284086,25:0.1810660431557166,44:0.19424138174284086,78:0.19424138174284086
> NaiveBayesModel model =NaiveBayesModel.materialize(output, conf); //
> output path of Model
> classifier = new ComplementaryNaiveBayesClassifier(model);
> classifier.classifyFull(vector); // this returns A vector of
> probabilities in 1 of n-1 encoding for your label. input will be the vector
> {1:
> 0.19424138174284086,24:0.19424138174284086,25:0.1810660431557166,44:0.19424138174284086,78:0.19424138174284086
> }
> Thanks
> Vaibhav.
>
>
>
>
>
>
>
>
> On Tue, Jul 29, 2014 at 9:06 PM, Luca Filipponi <luca.filippon...@gmail.com>
> wrote:
>
>> I appreciate your help, but for my lack of knowledge I didn't understand.
>>
>> I'll try to explain better my problem :D
>>
>> What I've done is to create a sequence File starting from csv like this (
>> is italian tweet :D ):
>>
>> negativo,471685156584292353, @beppe_grillo intanto .. Piangi tu ... Per
>> adesso io rido !!!!!
>>
>> positivo,471685170698149888,RT @carlucci_cc: @valy_s renzie si preoccupa
>> di chi gli garantisce voti...ma stanno scoprendo il prezzo di quei
>> fottutissimi #80euro dagli ...
>>
>> neutrale,471685174426886144,Di #elezioni, di venditori di fumo e di altre
>> schifezze... http://t.co/euFbtP7hQ1 ... #Europee2014 via
>>
>> So I create a sequence file in this way:
>>
>>
>> String[] tokens = line.split(",", 3);
>>
>> String label = tokens[0];
>> String id = tokens[1];
>> String message = tokens[2];
>> key.set("/" + label + "/" + id);
>> value.set(message);
>> writer.append(key, value);
>>
>>
>> So I'm creating a sequence File of the form <Text,Text> where the key is
>> composed in this way : "/label/documentID/" and the value contains the
>> original text of the document.
>>
>> After this step I create tfidf document using mahout utilities, then I've
>> a sequence file Text,VectorWritable like this:
>>
>> Key: /negativo/468437278663409666
>> Value:/negativo/468437278663409666:{143:0.2884088933275849,233:0.2884088933275849,241:0.2772479861583959,309:0.22061363650715415}
>>
>> Then I am using the command on the newly created vector:
>>
>> ./mahout trainnb -i tfidf-vectors -el -li labelindex -o model -ow -c
>>
>> And then:
>>
>> ./mahout testnb -i tfidf-vector -m model -l labelindex -ow -o
>> trainingVectorTest-result -c
>>
>> and this is the output:
>>
>> 14/07/25 15:44:04 INFO test.TestNaiveBayesDriver: Complementary Results:
>> =======================================================
>> Summary
>> -------------------------------------------------------
>> Correctly Classified Instances : 112 99,115%
>> Incorrectly Classified Instances : 1 0,885%
>> Total Classified Instances : 113
>>
>> =======================================================
>> Confusion Matrix
>> -------------------------------------------------------
>> a b c <--Classified as
>> 47 0 0 | 47 a = negativo
>> 0 41 0 | 41 b = neutrale
>> 0 1 24 | 25 c = positivo
>>
>> =======================================================
>> Statistics
>> -------------------------------------------------------
>> Kappa 0,9361
>> Accuracy 99,115%
>> Reliability 74%
>> Reliability (standard deviation) 0,4937
>>
>>
>> What I want to do now is to use the classifier on a new dataset that is
>> unlabeled, so I've a csv like this:
>>
>> 471685156584292353,@beppe_grillo intanto .. Piangi tu ... Per adesso io
>> rido !!!!!
>>
>> So I wrote a sequence file with:
>>
>> key= /documentid/ value= Content of the document
>>
>> and then use mahout utilities to create a tfidf-vector:
>>
>> Key: /471685156584292353/
>> Value:/471685156584292353/:{1:0.19424138174284086,24:0.19424138174284086,25:0.1810660431557166,44:0.19424138174284086,78:0.19424138174284086
>> ...
>>
>> But when I use the command testnb on this new dataset I get this exception:
>>
>> java.lang.IllegalArgumentException: Label not found: 471685156584292353
>>
>> I know that this is due, to the fact that the documentID is recognized as
>> label, but I don't know how to resolve that, could be great if you provide
>> me some similar example, becouse I can't find nothing similar.
>>
>> Thank you so much in advance, your help is really appreciated.
>>
>> Luca Filipponi.
>>
>>
>> Il giorno 29/lug/2014, alle ore 16:43, vaibhav srivastava <
>> vaibhavcs...@gmail.com> ha scritto:
>>
>>> Hi
>>> The sequence file format will be Text and Vector Writable.
>>> suppose you have test document named as 1,2,3,4.
>>> The you can have sequence file format as Key : /test/1 Value : <vectors1>
>>> /test/2 Value : <vectors2>
>>>
>>> this line in BayesTestMapper
>>> //the key is the expected value
>>>
>>> context.write(new Text(SLASH.split(key.toString())[1]), new
>>> VectorWritable(result));
>>>
>>>
>>> and TestNaiveBayesDriver.java might help you . if you remove this part
>> from
>>> this code you will not get confusion matrix and initial labels are not
>>> required.
>>>
>>>
>>>
>>>
>>> if (bestIdx != Integer.MIN_VALUE) {
>>>
>>> ClassifierResult classifierResult = new ClassifierResult(labelMap
>>> .get(bestIdx), bestScore);
>>>
>>> analyzer.addInstance(pair.getFirst().toString(),
>> classifierResult);
>>>
>>> }
>>>
>>>
>>> your out file will contain our document name suppose 1 and label vector
>>> with its values.
>>>
>>>
>>> hope this help.
>>>
>>> Thanks,
>>>
>>> Vaibhav
>>>
>>> vaibhavcs...@gmail.com
>>>
>>>
>>>
>>>
>>> On Tue, Jul 29, 2014 at 7:16 PM, Luca Filipponi <
>> luca.filippon...@gmail.com>
>>> wrote:
>>>
>>>> I am using mahout 0.9, which part of source code should I look?
>>>>
>>>> My problem is that I don't know how to the sequence file without the
>> label
>>>> should be structured.
>>>>
>>>> Do you have any hint?
>>>>
>>>> Il giorno 29/lug/2014, alle ore 15:24, vaibhav srivastava <
>>>> vaibhavcs...@gmail.com> ha scritto:
>>>>
>>>>> Hi,
>>>>> If you want to create a test set and if you do not want to measure
>>>> accuracy.
>>>>> Then you can make an instance of claasifier and load your model on that
>>>>> classifier and then can find the best score.
>>>>> Look at navie bayes test code.
>>>>> Hope this help. Thanks .
>>>>> On 29 Jul 2014 12:53, "Luca Filipponi" <luca.filippon...@gmail.com>
>>>> wrote:
>>>>>
>>>>>> Hi , I am trying to develop sentiment analysis on italian tweet from
>>>>>> twitter using the naive bayes classifier, but I've some trouble.
>>>>>>
>>>>>> My idea was to classify a lot of tweet as positive, negative or
>>>> neautral,
>>>>>> and using that as training set for the Classifier. To do that I've
>>>> wrote a
>>>>>> sequence file, in the format <Text,Text>, where in the key there is
>>>>>> /label/tweetID and in the key the text, and then the text of all the
>>>>>> dataset is converted in tfidf vector, using mahout utilities.
>>>>>>
>>>>>> Then I'm using the command:
>>>>>>
>>>>>> ./mahout trainnb and ./mahout testnb to check the classifier, and the
>>>>>> score is right (I've got nearly 100% because the test set is the same
>> as
>>>>>> the train set)
>>>>>>
>>>>>> My question is if I want to use a test set that is unlabeled how
>> should
>>>> it
>>>>>> be created? because if the format isn't like:
>>>>>>
>>>>>> key = /label/ the classifier can't find the label and I've got an
>>>>>> exception
>>>>>>
>>>>>> but in a new dataset, obviously this will be unlabeled because i need
>> to
>>>>>> classify that, so I don't know what put in the key of the sequence
>> file.
>>>>>>
>>>>>> I've searched online for some example, but the only ones that I've
>> found
>>>>>> use the split command, on the original dataset, and then testing on
>>>> part of
>>>>>> that, but isn't my case.
>>>>>>
>>>>>>
>>>>>> Every idea for developing a better sentiment analysis is welcome,
>> thanks
>>>>>> in advance for the help.
>>>>>>
>>>>>>
>>>>
>>>>
>>>
>>>
>>> --
>>> Thanks and Regards,
>>> Vaibhav Srivastava
>>> Email-id: vaibhavcs...@gmail.com
>>> Mobile no.: 9552543029
>>
>>
>
>
> --
> Thanks and Regards,
> Vaibhav Srivastava
> Email-id: vaibhavcs...@gmail.com
> Mobile no.: 9552543029
