Example #1
0
        // Construye bloques de último momento (sin detalle, quick), o pequeños, basados en tweets (i.e. no en otros bloques menores).
        public DbTimeBlock BuildTimeBlockFromTweets(bool quick, TimeSpan length)
        {
            IEnumerable <DbTweet> globalToProc = GetTweetsInTimeInterval(DateTime.Now.Add(-length), DateTime.Now, null);

            AnalysisResults globalAR = null;

            if (quick)
            {
                globalAR = TA.AnalyzeTweetSet(globalToProc, true);
            }
            else
            {
                globalAR = TA.AnalyzeTweetSet(globalToProc, false);
            }

            Dictionary <DbTopic, AnalysisResults> allTopicsAR = new Dictionary <DbTopic, AnalysisResults>();

            foreach (DbTopic t in db.Query <DbTopic>())
            {
                IEnumerable <DbTweet> topicToProc = GetTweetsInTimeInterval(DateTime.Now.Add(-length), DateTime.Now, t);

                AnalysisResults topicAR = null;
                if (quick)
                {
                    topicAR = TA.AnalyzeTweetSet(topicToProc, true);
                }
                else
                {
                    topicAR = TA.AnalyzeTweetSet(topicToProc, false);
                }

                allTopicsAR.Add(t, topicAR);
            }

            DbTimeBlock toRet = new DbTimeBlock(DateTime.Now.Add(-length), length, globalAR, allTopicsAR);

            db.Store(toRet);

            Console.Out.WriteLine("\n\n[ CONSTRUIDO TIME BLOCK, quick: " + quick + "+ ]\n");

            return(toRet);
        }
Example #2
0
        // Construye bloques de último momento (sin detalle, quick), o pequeños, basados en tweets (i.e. no en otros bloques menores).
        public DbTimeBlock BuildTimeBlockFromTweets(bool quick, TimeSpan length)
        {
            IEnumerable<DbTweet> globalToProc = GetTweetsInTimeInterval(DateTime.Now.Add(-length), DateTime.Now, null);

            AnalysisResults globalAR = null;
            if(quick)
                globalAR = TA.AnalyzeTweetSet(globalToProc, true);
            else
                globalAR = TA.AnalyzeTweetSet(globalToProc, false);

            Dictionary<DbTopic, AnalysisResults> allTopicsAR = new Dictionary<DbTopic, AnalysisResults>();
            foreach (DbTopic t in db.Query<DbTopic>())
            {
                IEnumerable<DbTweet> topicToProc = GetTweetsInTimeInterval(DateTime.Now.Add(-length), DateTime.Now, t);

                AnalysisResults topicAR = null;
                if(quick)
                    topicAR = TA.AnalyzeTweetSet(topicToProc, true);
                else
                    topicAR = TA.AnalyzeTweetSet(topicToProc, false);

                allTopicsAR.Add(t, topicAR);
            }

            DbTimeBlock toRet = new DbTimeBlock(DateTime.Now.Add(-length), length, globalAR, allTopicsAR);
            db.Store(toRet);

            Console.Out.WriteLine("\n\n[ CONSTRUIDO TIME BLOCK, quick: "+quick+"+ ]\n");

            return toRet;
        }
Example #3
0
        // Construye bloques medianos o grandes, basados en bloques chicos o medianos respectivamente.
        public void BuildTimeBlockFromBlocks(TimeSpan length)
        {
            TimeSpan childBlocksLength = TimeSpan.FromSeconds(0);
            if (length == TS_medium)
                childBlocksLength = TS_short;
            else if (length == TS_long)
                childBlocksLength = TS_medium;

            IEnumerable<DbTimeBlock> ch = from DbTimeBlock t in db
                                         where t.Length == childBlocksLength && t.Used == false
                                         orderby t.Start descending
                                         select t;
            int cant = ch.Count();
            AnalysisResults GlobalAR = new AnalysisResults();
            Dictionary<DbTopic, AnalysisResults> TopicAR = new Dictionary<DbTopic, AnalysisResults>();
            foreach (DbTopic t in db.Query<DbTopic>())
                TopicAR.Add(t, new AnalysisResults());

            if (cant > this.TimeBlockConsolidationThreshold)
            {
                foreach (DbTimeBlock cht in ch)
                {
                    GlobalAR.Popularity += cht.GlobalAR.Popularity;
                    GlobalAR.PosVal += cht.GlobalAR.PosVal;
                    GlobalAR.NegVal += cht.GlobalAR.NegVal;
                    GlobalAR.Ambiguity += cht.GlobalAR.Ambiguity;

                    // Para poder comparar las palabras importantes de un subbloque con otro, ya que cada uno fue calculado con tfidf distintos.
                    double normalizationFactor = 0;
                    foreach(KeyValuePair<string,double> kv in cht.GlobalAR.relevantList)
                        normalizationFactor += kv.Value;

                    foreach(KeyValuePair<string,double> kv in cht.GlobalAR.relevantList)
                        if(GlobalAR.RelevantTerms.ContainsKey(kv.Key))
                            GlobalAR.RelevantTerms[kv.Key] += kv.Value/normalizationFactor;
                        else
                            GlobalAR.RelevantTerms[kv.Key] = kv.Value/normalizationFactor;

                    // ídem a lo anterior, para cada topic
                    foreach (KeyValuePair<DbTopic, AnalysisResults> tar in cht.TopicAR)
                    {
                        TopicAR[tar.Key].Popularity += tar.Value.Popularity;
                        TopicAR[tar.Key].PosVal += tar.Value.PosVal;
                        TopicAR[tar.Key].NegVal += tar.Value.NegVal;
                        TopicAR[tar.Key].Ambiguity += tar.Value.Ambiguity;

                        normalizationFactor = 0;
                        foreach(KeyValuePair<string,double> kv in cht.TopicAR[tar.Key].relevantList)
                            normalizationFactor += kv.Value;

                        foreach(KeyValuePair<string,double> kv in cht.TopicAR[tar.Key].relevantList)
                            if(TopicAR[tar.Key].RelevantTerms.ContainsKey(kv.Key))
                                TopicAR[tar.Key].RelevantTerms[kv.Key] += kv.Value/normalizationFactor;
                            else
                                TopicAR[tar.Key].RelevantTerms[kv.Key] = kv.Value / normalizationFactor;
                    }

                    cht.Used = true;
                    db.Store(cht);
                }

                GlobalAR.Ambiguity /= cant;
                GlobalAR.DictionaryToList();

                foreach (DbTopic t in db.Query<DbTopic>())
                {
                    TopicAR[t].Ambiguity /= cant;
                    TopicAR[t].DictionaryToList();
                }

                DbTimeBlock toAdd = new DbTimeBlock(DateTime.Now.Add(-length), length, GlobalAR, TopicAR);
                db.Store(toAdd);

                Console.Out.WriteLine("\n\n[ CONSTRUIDO TIME BLOCK (desde otros bloques), tamaño: " + length +"+ ]\n");

            }
        }
Example #4
0
        // Construye bloques medianos o grandes, basados en bloques chicos o medianos respectivamente.
        public void BuildTimeBlockFromBlocks(TimeSpan length)
        {
            TimeSpan childBlocksLength = TimeSpan.FromSeconds(0);

            if (length == TS_medium)
            {
                childBlocksLength = TS_short;
            }
            else if (length == TS_long)
            {
                childBlocksLength = TS_medium;
            }

            IEnumerable <DbTimeBlock> ch = from DbTimeBlock t in db
                                           where t.Length == childBlocksLength && t.Used == false
                                           orderby t.Start descending
                                           select t;
            int             cant     = ch.Count();
            AnalysisResults GlobalAR = new AnalysisResults();
            Dictionary <DbTopic, AnalysisResults> TopicAR = new Dictionary <DbTopic, AnalysisResults>();

            foreach (DbTopic t in db.Query <DbTopic>())
            {
                TopicAR.Add(t, new AnalysisResults());
            }

            if (cant > this.TimeBlockConsolidationThreshold)
            {
                foreach (DbTimeBlock cht in ch)
                {
                    GlobalAR.Popularity += cht.GlobalAR.Popularity;
                    GlobalAR.PosVal     += cht.GlobalAR.PosVal;
                    GlobalAR.NegVal     += cht.GlobalAR.NegVal;
                    GlobalAR.Ambiguity  += cht.GlobalAR.Ambiguity;

                    // Para poder comparar las palabras importantes de un subbloque con otro, ya que cada uno fue calculado con tfidf distintos.
                    double normalizationFactor = 0;
                    foreach (KeyValuePair <string, double> kv in cht.GlobalAR.relevantList)
                    {
                        normalizationFactor += kv.Value;
                    }

                    foreach (KeyValuePair <string, double> kv in cht.GlobalAR.relevantList)
                    {
                        if (GlobalAR.RelevantTerms.ContainsKey(kv.Key))
                        {
                            GlobalAR.RelevantTerms[kv.Key] += kv.Value / normalizationFactor;
                        }
                        else
                        {
                            GlobalAR.RelevantTerms[kv.Key] = kv.Value / normalizationFactor;
                        }
                    }

                    // ídem a lo anterior, para cada topic
                    foreach (KeyValuePair <DbTopic, AnalysisResults> tar in cht.TopicAR)
                    {
                        TopicAR[tar.Key].Popularity += tar.Value.Popularity;
                        TopicAR[tar.Key].PosVal     += tar.Value.PosVal;
                        TopicAR[tar.Key].NegVal     += tar.Value.NegVal;
                        TopicAR[tar.Key].Ambiguity  += tar.Value.Ambiguity;

                        normalizationFactor = 0;
                        foreach (KeyValuePair <string, double> kv in cht.TopicAR[tar.Key].relevantList)
                        {
                            normalizationFactor += kv.Value;
                        }

                        foreach (KeyValuePair <string, double> kv in cht.TopicAR[tar.Key].relevantList)
                        {
                            if (TopicAR[tar.Key].RelevantTerms.ContainsKey(kv.Key))
                            {
                                TopicAR[tar.Key].RelevantTerms[kv.Key] += kv.Value / normalizationFactor;
                            }
                            else
                            {
                                TopicAR[tar.Key].RelevantTerms[kv.Key] = kv.Value / normalizationFactor;
                            }
                        }
                    }

                    cht.Used = true;
                    db.Store(cht);
                }

                GlobalAR.Ambiguity /= cant;
                GlobalAR.DictionaryToList();

                foreach (DbTopic t in db.Query <DbTopic>())
                {
                    TopicAR[t].Ambiguity /= cant;
                    TopicAR[t].DictionaryToList();
                }

                DbTimeBlock toAdd = new DbTimeBlock(DateTime.Now.Add(-length), length, GlobalAR, TopicAR);
                db.Store(toAdd);

                Console.Out.WriteLine("\n\n[ CONSTRUIDO TIME BLOCK (desde otros bloques), tamaño: " + length + "+ ]\n");
            }
        }