Package seekfeel.dataholders

Examples of seekfeel.dataholders.Review

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  }

  public ArrayList<LinkedHashMap<String, Sentiment>> sentimizeTrial(
      String trialRev) {
    ConsoleReader cReader = new ConsoleReader();
    Review rev = (Review) cReader.getData(trialRev).get(0);
    dpAspExt = new DPAspectExtractor();
    rev.formatt();
    ArrayList<String> allFeats = dpAspExt.getFeatures(rev);
    ArrayList<featuredSentence> fSentences;
    fSentences = getFeaturedSentences(rev, allFeats);
    ArrayList<LinkedHashMap<String, Sentiment>> featsSentiment = new ArrayList<LinkedHashMap<String, Sentiment>>();
    for (featuredSentence sentenceWithFeats : fSentences) {
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    public void preprocess() {
        //  rp.AddProcessor(tc); // text cleaner is added to review processor
        //rp.AddProcessor(sp); // sentence splitting is added to review parser
        //rp.processReviwes(Revs);
        Review temp;
        for (DataUnit r : Revs) {
            temp = (Review) r;
            temp.Review_Sentences = new ArrayList<String>(Arrays.asList(((Review) r).Review_Body.split("[.,?!:;]+")));
        }
    }
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    public DataUnit getReviewByIndex(int index) {
        return Revs.get(index);
    }

    public ArrayList<String> getOriginalFeatures(int index) {
        Review r = (Review) Revs.get(index);

        ArrayList<String> returnedList = new ArrayList<String>();
        for (ArrayList<Product_Feature> pfs : r.Sentences_Features) {
            for (Product_Feature pf : pfs) {
                returnedList.add(pf.Name);
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            //  addAspectextractor(miraAspExt);
            int revsSize = Revs.size();
            ArrayList<featuredSentence> fSentences = new ArrayList<featuredSentence>();
            rc = new RelevanceCalculator(
                    PropertiesGetter.getProperty("Frequencies"), Revs);
            Review tempReview = null;
            FeaturesFilter f;
            ArrayList<String> extractedFeatures = null;
            ArrayList<String> filteredFeatures = null;
            for (int index = 0; index < revsSize; index++) {

                f = new FeaturesFilter();
                tempReview = ((Review) Revs.get(index));
                tempReview.formatt();
                extractedFeatures = getFeatures(tempReview);
                filteredFeatures = f.getFileteredFeatures(extractedFeatures,getRelevances(extractedFeatures));
                fSentences.addAll(getFeaturedSentences(tempReview, extractedFeatures));
            }
            endtime = System.currentTimeMillis();
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            //  addAspectextractor(miraAspExt);
            int revsSize = Revs.size();
            ArrayList<featuredSentence> fSentences = new ArrayList<featuredSentence>();
            rc = new RelevanceCalculator(
                    PropertiesGetter.getProperty("Frequencies"), Revs);
            Review tempReview = null;
            for (int index = 0; index < revsSize; index++) {

                tempReview = ((Review) Revs.get(index));
                tempReview.formatt();
                fSentences.addAll(getFeaturedSentences(tempReview, features));
            }
            // /////////////////////// sentiment analysis goes here ...............
            ArrayList<LinkedHashMap<String, Sentiment>> featsSentiment = new ArrayList<LinkedHashMap<String, Sentiment>>();
            LinkedHashMap<String, FeatureSummary> summary = new LinkedHashMap<String, FeatureSummary>();
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        return res;
    }
    public ArrayList<LinkedHashMap<String, Sentiment>> sentimizeTrial(
            String trialRev) {
        ConsoleReader cReader = new ConsoleReader();
        Review rev = (Review) cReader.getData(trialRev).get(0);
        dpAspExt = new DPAspectExtractor();
        rev.formatt();
        ArrayList<String> allFeats = dpAspExt.getFeatures(rev);
        ArrayList<featuredSentence> fSentences;
        fSentences = getFeaturedSentences(rev, allFeats);
        ArrayList<LinkedHashMap<String, Sentiment>> featsSentiment = new ArrayList<LinkedHashMap<String, Sentiment>>();
        for (featuredSentence sentenceWithFeats : fSentences) {
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        }
    }

    @Override
    public Sentiment classifyText(String text) {
        Review theText = new Review();
        theText.setDataBody(text);
        LinkedHashMap<Integer, Double> textFeats = computeSampleFeatures(theText, null);
        double result = weka.classify(textFeats);
        if (result == 0.0) {
            return Sentiment.Positive;
        } else {
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                           c++;
                        }
                        if(Sentence.contains("##") )
                        {
                            NumberOfReviews++;
                            Review  R = RP.Parse_Review_Data(Sentence);
                            if( R.getDataBody() != null)
                            {
                              Found_Reviews.add(R);
                            }
                        }
                    }
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    ArrayList<Review> Combine_Reviews_By_Title(ArrayList<Review> Reviews)
    {

        String title = Reviews.get(0).Review_Title;
        ArrayList<Review> ReturenedReviews = new ArrayList<Review>();
        Review r = new Review();
        int size=  Reviews.size();
        ArrayList<Product_Feature> Features = new ArrayList<Product_Feature>();

        for (int i = 0; i < size; i++) {
            if(Reviews.get(i).Review_Title.equals(title))
            {
                r.Review_Sentences.add(Reviews.get(i).getDataBody());
                Features = new ArrayList<Product_Feature>();
                for(Product_Feature f : ((ArrayList<Product_Feature>)Reviews.get(i).Features) )
                {
                    Features.add(f);
                }
                r.Sentences_Features.add(Features);
            }
            else
            {
                r.Review_Title = title;
                if(!r.Review_Sentences.isEmpty() )
                ReturenedReviews.add(r);
                 r = new Review();
                 title = Reviews.get(i).Review_Title;
                 r.Review_Sentences.add(Reviews.get(i).getDataBody());
                  Features = new ArrayList<Product_Feature>();
                for(Product_Feature f : ((ArrayList<Product_Feature>)Reviews.get(i).Features) )
                {
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  public DataUnit getReviewByIndex(int index) {
    return Revs.get(index);
  }

  public ArrayList<String> getOriginalFeatures(int index) {
    Review r = (Review) Revs.get(index);

    ArrayList<String> returnedList = new ArrayList<String>();
    for (ArrayList<Product_Feature> pfs : r.Sentences_Features) {
      for (Product_Feature pf : pfs) {
        returnedList.add(pf.Name);
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Related Classes of seekfeel.dataholders.Review

  • aspects_doublepropagation.Main
  • DataAcquisition.AmazonSource
  • DataAcquisition.EpinionSource
  • Filter.Controller
  • preprocessing.Formatted_Text
  • preprocessing.Reviews_Parser
  • seekfeel.datareaders.Reviews_Reader
  • seekfeel.fileparsers.Reviews_Parser
  • seekfeel.miners.features.computers.DeltaTFIDFComputerUni
  • seekfeel.miners.supervised.SupervisedPanel
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