web mining: first solution slides
This commit is contained in:
Michael Scholz
parent
893d4000fb
commit
c93f913e0e
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@ -1,4 +1,4 @@
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\usepackage[ngerman]{babel}
|
||||
\usepackage[utf8]{inputenc}
|
||||
|
||||
\title{1 U"bung}
|
||||
\author{U. Gebhardt, M. Scholz, V. ...}
|
||||
\date{06.05.2013}
|
||||
|
||||
|
||||
\title{1 "Ubung}
|
||||
\author{Ulf Gebhardt, Victor-Philipp Negoescu, Michael Scholz}
|
||||
\date{05.05.2013}
|
||||
|
||||
|
||||
\newcommand{\al}{\glqq}
|
||||
\newcommand{\ar}{\grqq\ }
|
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|
||||
|
||||
|
||||
% % % % % % % % % % % % % % % % % % % % % % % 1. Folie % % % % % % % % % % % % % % % % % % % % % %% % % % % % % % % % % % % % % % % % % %
|
||||
% % % % % % % % % % % % % % % % % % % % % % %% % % % % % % % % % % % % % % % % % % % % % %% % % % % % % % % % % % % % % % % % % % % % % %
|
||||
\begin{document}
|
||||
\begin{frame}
|
||||
\frametitle{Web Mining \\ 1. "Ubung}
|
||||
\textbf{Gruppe:}
|
||||
\begin{itemize}
|
||||
\item Ulf Gebhardt
|
||||
\item Victor-Philipp Negoescu
|
||||
\item Michael Scholz
|
||||
\end{itemize}
|
||||
\end{frame}
|
||||
|
||||
% % % % % % % % % % % % % % % % % % % % % % % 1. Aufgabe % % % % % % % % % % % % % % % % % % % % % %% % % % % % % % % % % % % % % % % %
|
||||
% % % % % % % % % % % % % % % % % % % % % % %% % % % % % % % % % % % % % % % % % % % % % %% % % % % % % % % % % % % % % % % % % % % % % %
|
||||
\section{1. Aufgabe}
|
||||
\begin{frame}
|
||||
\frametitle{1. Aufgabe \\ Aufgabenstellung}
|
||||
"Uberlegen Sie sich eine neuartige, originelle Web Mining Anwendung, die mit Text-Klassifikationsverfahren gel"ost werden k"onnte. Skizzieren Sie eine m"ogliche Umsetzung (Sammlung der Trainingsdaten, Klassifikation der Trainingsdaten, Einsatz des gelernten Klassifikators in der Praxis). (2 Punkte)
|
||||
\end{frame}
|
||||
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{1. Übung}
|
||||
\begin{enumerate}
|
||||
\item 1. Aufgabe
|
||||
\item 2. Aufgabe
|
||||
\item 3. Aufgabe
|
||||
\item 4. Aufgabe
|
||||
\item 5. Aufgabe
|
||||
\end{enumerate}
|
||||
\frametitle{1. Aufgabe \\ L"osung}
|
||||
Textbasiertes Klassifikationsverfahren für gecrawlte Produktrezensionen. \\
|
||||
Einteilung in die Klassen:
|
||||
\begin{itemize}
|
||||
\item \textbf{Positiv:} \\
|
||||
Rezension lobt das Produkt
|
||||
\item \textbf{Neutral:} \\
|
||||
Die Rezension besitzt keine eindeutige Tendenz
|
||||
\item \textbf{Negativ:} \\
|
||||
Die Rezension kritisiert das Produkt
|
||||
\end{itemize}
|
||||
\end{frame}
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{1. Aufgabe \\ L"osung}
|
||||
\textbf{Sammlung von Trainingsdaten:}
|
||||
\begin{itemize}
|
||||
\item Produktrezensionen enthalten meist Bewertung von 1 (sehr negativ) bis 5 (sehr positiv)
|
||||
\item Zudem enthalten sie eine ausf"uhrliche Beschreibung
|
||||
\item Trainingsdaten k"onnen automatisch durch einen Crawler gesammelt werden.
|
||||
\item Crawler durchl"auft die DOM-Struktur oder verwendet API-Schnittstelle (falls vorhanden)
|
||||
\end{itemize}
|
||||
Ergebnis: \\
|
||||
Zuordnung von Bewertungen (1-5) zu Rezension (Text)
|
||||
\end{frame}
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{1. Aufgabe \\ L"osung}
|
||||
\textbf{Klassifikation der Trainingsdaten:} \\
|
||||
Zuweisung von Bewertung zu definierter Klasse:
|
||||
\begin{itemize}
|
||||
\item Bewertung \{1, 1.5, 2\}: Negativ
|
||||
\item Bewertung \{2.5, 3, 3.5\}: Neutral
|
||||
\item Bewertung \{4, 4.5, 5\}: Positiv
|
||||
\end{itemize}
|
||||
Gesammelte Trainingsdaten k"onnten nun entsprechend dieser Zuweisung in die passende Klasse eingeordnet werden.
|
||||
\end{frame}
|
||||
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{1. Aufgabe \\ L"osung}
|
||||
\textbf{Einsatz des gelernten Klassifikators in der Praxis:}
|
||||
|
||||
\end{frame}
|
||||
|
||||
% % % % % % % % % % % % % % % % % % % % % % % 2. Aufgabe % % % % % % % % % % % % % % % % % % % % % %% % % % % % % % % % % % % % % % % %
|
||||
% % % % % % % % % % % % % % % % % % % % % % %% % % % % % % % % % % % % % % % % % % % % % %% % % % % % % % % % % % % % % % % % % % % % % %
|
||||
\section{2. Aufgabe}
|
||||
\begin{frame}
|
||||
\frametitle{2. Aufgabe \\ Aufgabenstellung}
|
||||
Schreiben Sie ein einfaches Programm, das eine sortierte Liste der in einem Text vorkommenden Worte (im weitesten Sinn alles was durch Leerzeichen begrenzt wird) mit den assoziierten H"aufigkeiten (absolut und prozentual) erstellt und sortiert ausgibt. (2 Punkte)
|
||||
\begin{itemize}
|
||||
\item Vergleichen Sie die 30 am h"aufigsten vorkommenden Worte in zwei oder mehreren l"angeren Texten der gleichen Sprache (z.B. E-books, Projekt Gutenberg, etc.). Sind diese Worte als Merkmale f"ur Text-Klassifizierungs-Aufgaben geeignet? Warum?
|
||||
\item Modifizieren Sie Ihr Programm dahingehend, dass es eine Liste von Stoppw"ortern erhalten kann, die ignoriert werden. Wiederholen Sie die vorherige Aufgabe, indem Sie jedoch diesmal die Stoppw"orter der jeweiligen Sprache ignorieren. Wie w"urden Sie nun die Eignung der 30 h"aufigisten W"orter einsch"atzen?
|
||||
\end{itemize}
|
||||
\end{frame}
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{2. Aufgabe \\ L"osung}
|
||||
\end{frame}
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{2. Aufgabe \\ L"osung}
|
||||
\end{frame}
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{2. Aufgabe \\ L"osung}
|
||||
\end{frame}
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{2. Aufgabe \\ L"osung}
|
||||
\end{frame}
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{2. Aufgabe \\ L"osung}
|
||||
\end{frame}
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{2. Aufgabe \\ L"osung}
|
||||
\end{frame}
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{2. Aufgabe \\ L"osung}
|
||||
\end{frame}
|
||||
|
||||
% % % % % % % % % % % % % % % % % % % % % % % 3. Aufgabe % % % % % % % % % % % % % % % % % % % % % %% % % % % % % % % % % % % % % % % %
|
||||
% % % % % % % % % % % % % % % % % % % % % % %% % % % % % % % % % % % % % % % % % % % % % %% % % % % % % % % % % % % % % % % % % % % % % %
|
||||
\section{3. Aufgabe}
|
||||
\begin{frame}
|
||||
\frametitle{3. Aufgabe \\ Aufgabenstellung}
|
||||
Die Auftrittswahrscheinlichkeiten von Worten in Texten folgen einer sogenannten Zipf-Verteilung, d.h. einer Verteilung, die doppelt logarithmisch ist. "Uberpr"ufen Sie das anhand der gew"ahlten Texte. (2 Punkte)
|
||||
\begin{itemize}
|
||||
\item Plotten Sie die H"aufigkeiten (y-Achse) "uber den Rang (X-Achse), also die Anzahl der Vorkommnisse des h"aufigsten Wortes zuerst, dann die Anzahl des zweith"aufigsten Wortes, etc. Betrachten Sie sowohl eine absolute als auch eine logarithmische Skalierung beider Achsen. Was k"onnen Sie beobachten?
|
||||
\item Bestimmen Sie die Anzahl der Worte, die mit einer gegebenen H"aufigkeit vrkommen (also, wie viele W"orter gibt es, die mit H"aufigkeit 1 vorkommen, wie viele mit H"aufigkeit 2, etc.). Produzieren Sie "ahnliche Grafiken (Anzahl der Worte mit einer gewissen H"aufigkeit "uber die H"aufigkeit) und interpretieren Sie diese.
|
||||
\end{itemize}
|
||||
\end{frame}
|
||||
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{3. Aufgabe \\ L"osung}
|
||||
\end{frame}
|
||||
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{3. Aufgabe \\ L"osung}
|
||||
\end{frame}
|
||||
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{3. Aufgabe \\ L"osung}
|
||||
\end{frame}
|
||||
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{3. Aufgabe \\ L"osung}
|
||||
\end{frame}
|
||||
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{3. Aufgabe \\ L"osung}
|
||||
\end{frame}
|
||||
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{3. Aufgabe \\ L"osung}
|
||||
\end{frame}
|
||||
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{3. Aufgabe \\ L"osung}
|
||||
\end{frame}
|
||||
|
||||
|
||||
% % % % % % % % % % % % % % % % % % % % % % % 4. Aufgabe % % % % % % % % % % % % % % % % % % % % % %% % % % % % % % % % % % % % % % % %
|
||||
% % % % % % % % % % % % % % % % % % % % % % %% % % % % % % % % % % % % % % % % % % % % % %% % % % % % % % % % % % % % % % % % % % % % % %
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{4. Aufgabe \\ Aufgabenstellung}
|
||||
Modifizieren Sie das Programm, so dass es nicht Worte sondern Buchstaben und Buchstabenpaare z"ahlt. Vergleichen Sie deren H"aufigkeitsverteilung sowohl zweier in der gleichen Sprache verfassten Texte als auch zweier in verschiedenen Sprachen abgefasster Texte. (2 Punkte)
|
||||
\end{frame}
|
||||
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{4. Aufgabe \\ L"osung}
|
||||
\end{frame}
|
||||
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{4. Aufgabe \\ L"osung}
|
||||
\end{frame}
|
||||
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{4. Aufgabe \\ L"osung}
|
||||
\end{frame}
|
||||
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{4. Aufgabe \\ L"osung}
|
||||
\end{frame}
|
||||
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{4. Aufgabe \\ L"osung}
|
||||
\end{frame}
|
||||
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{4. Aufgabe \\ L"osung}
|
||||
\end{frame}
|
||||
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{4. Aufgabe \\ L"osung}
|
||||
\end{frame}
|
||||
|
||||
|
||||
|
||||
\section{1. Aufgabe}
|
||||
% % % % % % % % % % % % % % % % % % % % % % % 5. Aufgabe % % % % % % % % % % % % % % % % % % % % % %% % % % % % % % % % % % % % % % % %
|
||||
% % % % % % % % % % % % % % % % % % % % % % %% % % % % % % % % % % % % % % % % % % % % % %% % % % % % % % % % % % % % % % % % % % % % % %
|
||||
\section{5. Aufgabe}
|
||||
\begin{frame}
|
||||
\frametitle{5. Aufgabe \\ Aufgabenstellung}
|
||||
Laden Sie sich unser kleiner Firefox-Plugin herunter und machen Sie dich damit und den Entwicklungstools vertraut. Weiter unten finden Sie Hinweise zur Verwendung. Erweitern Sie die Funktion \textit{student(text)} in der Datei \textit{lib/student.js} dahingehend, dass es drei Sprachen erkennen kann. Die Liste der bisher unterst"utzten Sprachen k"onnen Sie gernen in \textit{languages.js} nach Belieben erweitern. Verwenden Sie f"ur die Spracherkennung eine einfache Heuristik, die z.B. auf den in der vorherigen Aufgabe vorgestellen Stoppw"ortern basiert. Browsen Sie dann ein wenig im Wem herum und berichten uns von Ihren Ergebnissen und Erfahrungen mit dem Plugin. (1 Punkt)
|
||||
\end{frame}
|
||||
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{5. Aufgabe \\ L"osung}
|
||||
Anpassungen in \textit{utility.js}:
|
||||
\begin{itemize}
|
||||
\item Erstelle Funktionen \textit{getDeStopwords()\{...\}}, \textit{getEnStopwords()\{...\}} und \textit{getFrStopwords()\{...\}}, welche die dazugeh"origen Stoppw"orter in Form eines Arrays zur"uckgeben
|
||||
\item Erstelle Funktion \textit{arrayContains(array, element)\{...\}}, welche pr"uft ob sich das gegebene Element im gegebenen Array befindet
|
||||
\end{itemize}
|
||||
\end{frame}
|
||||
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{5. Aufgabe \\ L"osung}
|
||||
Anpassungen in \textit{student.js}:
|
||||
\begin{itemize}
|
||||
\item Zerlege Text in Tokens
|
||||
\item Erstelle Variablen zum Z"ahlen der deutschen, englischen und franz"osischen W"orter im Text.
|
||||
\item Pr"ufe nun jedes Token ob es in den sprachspezifischen Stoppw"ortern vorkommt. Ist dies der Fall -> erh"ohe die dazuge"ohrige Variable um 1. \\
|
||||
Nutze hierf"ur die zuvor in \textit{utility.js} definierten Funktionen.
|
||||
\item Gebe die Sprache zur"uck deren Z"ahlvariable den h"ochsten Wert hat.
|
||||
\end{itemize}
|
||||
\end{frame}
|
||||
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{5. Aufgabe \\ L"osung}
|
||||
Ergebnisse:
|
||||
\begin{itemize}
|
||||
\item Einfache \al Z"ahlheuristik\ar funktioniert zuverl"assig
|
||||
\item Alle besuchten Webseiten wurden richtig erkannt
|
||||
\item Bug im Addon? \\
|
||||
Nutzt man im Browser mehrere Tabs, so zeigt das Addon beim Wechsel des Tabs immer die Sprache der zuletzt geladenen Webseite an.
|
||||
\end{itemize}
|
||||
\end{frame}
|
||||
|
||||
|
||||
|
||||
@ -1,3 +1,6 @@
|
||||
\beamer@endinputifotherversion {3.12pt}
|
||||
\select@language {ngerman}
|
||||
\beamer@sectionintoc {1}{1. Aufgabe}{2}{0}{1}
|
||||
\beamer@sectionintoc {2}{2. Aufgabe}{7}{0}{2}
|
||||
\beamer@sectionintoc {3}{3. Aufgabe}{15}{0}{3}
|
||||
\beamer@sectionintoc {4}{5. Aufgabe}{31}{0}{4}
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user